@article{alexander2007,
  title = {Emergence of Drug Resistance: Implications for Antiviral Control of Pandemic Influenza},
  author = {Alexander, Murray E and Bowman, Christopher S and Feng, Zhilan and Gardam, Michael and Moghadas, Seyed M and R{\"o}st, Gergely and Wu, Jianhong and Yan, Ping},
  year = {2007},
  month = jul,
  journal = {Proceedings. Biological sciences / The Royal Society},
  volume = {274},
  number = {1619},
  pages = {1675--1684},
  doi = {10.1098/rspb.2007.0422},
  pii = {RP31477278JV6833},
  pmid = {17507331},
  timestamp = {2008.03.09}
}

@article{altizer2006,
  title = {Seasonality and the Dynamics of Infectious Diseases: {{Seasonality}} and Infectious Diseases},
  shorttitle = {Seasonality and the Dynamics of Infectious Diseases},
  author = {Altizer, Sonia and Dobson, Andrew and Hosseini, Parviez and Hudson, Peter and Pascual, Mercedes and Rohani, Pejman},
  year = {2006},
  month = mar,
  journal = {Ecology Letters},
  volume = {9},
  number = {4},
  pages = {467--484},
  issn = {1461023X, 14610248},
  doi = {10.1111/j.1461-0248.2005.00879.x},
  urldate = {2017-08-08},
  langid = {english},
  keywords = {Animal Diseases,Animals,Annual cycle,climate variation,Communicable Diseases,Disease Susceptibility,epidemiology,Host-Parasite Interactions,host\textendash parasite interaction,Models,population dynamics,Population Dynamics,Reproduction,Risk Factors,Seasons,Social Behavior,Theoretical,transmission,Wild},
  file = {C\:\\Users\\andre\\Zotero\\storage\\4BCITMPE\\altizer06ecolet - seasonality and the dynamics of infectious diseases.pdf;C\:\\Users\\andre\\Zotero\\storage\\BPCXAMHC\\Altizer et al. - 2006 - Seasonality and the dynamics of infectious disease.pdf;C\:\\Users\\andre\\Zotero\\storage\\YUWTCCKT\\j.1461-0248.2005.00879.html}
}

@article{bansal07,
  ids = {bansalWhenIndividualBehaviour2007a,bansalWhenIndividualBehaviour2007b},
  title = {When Individual Behaviour Matters: Homogeneous and Network Models in Epidemiology},
  shorttitle = {When Individual Behaviour Matters},
  author = {Bansal, S. and Grenfell, B. T and Meyers, L. A.},
  year = {2007},
  month = oct,
  journal = {Journal of The Royal Society Interface},
  volume = {4},
  number = {16},
  pages = {879--891},
  publisher = {{Computational and Applied Mathematics, Institute for Computational Engineering and Sciences, University of Texas at Austin, 1 University Station, C0200, Austin, TX 78712, USA.}},
  institution = {{Computational and Applied Mathematics, Institute for Computational Engineering and Sciences, University of Texas at Austin, 1 University Station, C0200, Austin, TX 78712, USA.}},
  issn = {1742-5689, 1742-5662},
  doi = {10.1098/rsif.2007.1100},
  urldate = {2017-08-08},
  __markedentry = {[bmckay52:6]},
  langid = {english},
  medline-pst = {ppublish},
  pii = {080KX13732480384},
  pmc = {PMC2394553},
  pmid = {17640863},
  keywords = {Biological,Disease Transmission,Epidemiologic Methods,Humans,Infectious,Models,Statistical},
  file = {C\:\\Users\\andre\\Zotero\\storage\\DF58G2H2\\bansal07jrsi - When individual behaviour matters - homogeneous and network models in epidemiology.pdf}
}

@article{bansal10,
  title = {The Dynamic Nature of Contact Networks in Infectious Disease Epidemiology.},
  author = {Bansal, Shweta and Read, Jonathan and Pourbohloul, Babak and Meyers, Lauren Ancel},
  year = {2010},
  month = sep,
  journal = {J Biol Dyn},
  volume = {4},
  number = {5},
  pages = {478--489},
  doi = {10.1080/17513758.2010.503376},
  abstract = {Although contact network models have yielded important insights into infectious disease transmission and control throughout the last decade, researchers have just begun to explore the dynamic nature of contact patterns and their epidemiological significance. Most network models have assumed that contacts are static through time. Developing more realistic models of the social interactions that underlie the spread of infectious diseases thus remains an important challenge for both data gatherers and modelers. In this article, we review some recent data-driven and process-driven approaches that capture the dynamics of human contact, and discuss future challenges for the field.},
  langid = {english},
  pmid = {22877143},
  keywords = {Biological,Communicable Diseases,epidemiology,Humans,Interpersonal Relations,Models,Statistics as Topic}
}

@article{bansal12,
  title = {The Impact of Past Epidemics on Future Disease Dynamics.},
  author = {Bansal, Shweta and Meyers, Lauren Ancel},
  year = {2012},
  month = sep,
  journal = {J Theor Biol},
  volume = {309},
  pages = {176--184},
  doi = {10.1016/j.jtbi.2012.06.012},
  abstract = {Many pathogens spread primarily via direct contact between infected and susceptible hosts. Thus, the patterns of contacts or contact network of a population fundamentally shape the course of epidemics. While there is a robust and growing theory for the dynamics of single epidemics in networks, we know little about the impacts of network structure on long-term epidemic or endemic transmission. For seasonal diseases like influenza, pathogens repeatedly return to populations with complex and changing patterns of susceptibility and immunity acquired through prior infection. Here, we develop two mathematical approaches for modeling consecutive seasonal outbreaks of a partially-immunizing infection in a population with contact heterogeneity. Using methods from percolation theory we consider both leaky immunity, where all previously infected individuals gain partial immunity, and polarized immunity, where a fraction of previously infected individuals are fully immune. By restructuring the epidemiologically active portion of their host population, such diseases limit the potential of future outbreaks. We speculate that these dynamics can result in evolutionary pressure to increase infectiousness.},
  langid = {english},
  pmid = {22721993},
  keywords = {Biological,Disease,Epidemics,Immune Evasion,Immunity,immunology,Models,Seasons}
}

@article{barton13,
  title = {Subtype Diversity and Reassortment Potential for Co-Circulating Avian Influenza Viruses at a Diversity Hot Spot},
  author = {Barton, Heather D. and Rohani, Pejman and Stallknecht, David E. and Brown, Justin and Drake, John M.},
  year = {2013},
  month = oct,
  journal = {The Journal of Animal Ecology},
  issn = {1365-2656},
  doi = {10.1111/1365-2656.12167},
  abstract = {Biological diversity has long been used to measure ecological health. While evidence exists from many ecosystems that declines in host biodiversity may lead to greater risk of disease emergence, the role of pathogen diversity in the emergence process remains poorly understood. Particularly, because a more diverse pool of pathogen types provides more ways in which evolutionary innovations may arise, we suggest that host-pathogen systems with high pathogen diversity are more prone to disease emergence than systems with relatively homogeneous pathogen communities. We call this prediction the diversity-emergence hypothesis. To show how this hypothesis could be tested, we studied a system comprised of North American shorebirds and their associated low-pathogenicity avian influenza (LPAI) viruses. These viruses are important as a potential source of genetic innovations in influenza. A theoretical contribution of this study is an expression predicting the rate of viral subtype reassortment to be proportional to both prevalence and Simpson's Index, a formula that has been used traditionally to quantify biodiversity. We then estimated prevalence and subtype diversity in host species at Delaware Bay, a North American AIV hotspot, and used our model to extrapolate from these data. We estimated that 4 to 39 virus subtypes circulated at Delaware Bay each year between 2000 and 2008, and that surveillance coverage (percentage of co-circulating subtypes collected) at Delaware Bay is only about 63{$\cdot$}0\%. Simpson's Index in the same period varied more than fourfold from 0{$\cdot$}22 to 0{$\cdot$}93. These measurements together with the model provide an indirect, model-based estimate of the reassortment rate. A proper test of the diversity-emergence hypothesis would require these results to be joined to independent and reliable estimates of reassortment, perhaps obtained through molecular surveillance. These results suggest both that subtype diversity (and therefore reassortment) varies from year to year and that several subtypes contributing to reassortment are going undetected. The similarity between these results and more detailed studies of one host, ruddy turnstone (Arenaria interpres), further suggests that this species may be the primary host for influenza reassortment at Delaware Bay. Biological diversity has long been quantified using Simpson's Index. Our model links this formula to a mechanistic account of reassortment in multipathogen systems in the form of subtype diversity at Delaware Bay, USA. As a theory of how pathogen diversity may influence the evolution of novel pathogens, this work is a contribution to the larger project of understanding the connections between biodiversity and disease.},
  langid = {english},
  pmcid = {PMC4000580},
  pmid = {24164627},
  keywords = {Animal Migration,Animals,Biological,Charadriiformes,classification/genetics/physiology,Delaware,epidemiology,epidemiology/virology,Influenza A virus,Influenza in Birds,Models,New Jersey,Prevalence,Reassortant Viruses,Species Specificity,virology}
}

@article{begon02,
  ids = {begonClarificationTransmissionTerms2002a,begonClarificationTransmissionTerms2002b},
  title = {A Clarification of Transmission Terms in Host-Microparasite Models: Numbers, Densities and Areas},
  shorttitle = {A Clarification of Transmission Terms in Host-Microparasite Models},
  author = {Begon, Michael and Bennett, Malcolm and Bowers, Roger G. and French, Nigel P. and Hazel, S. M. and Turner, Joseph},
  year = {2002},
  journal = {Epidemiology \& Infection},
  volume = {129},
  number = {1},
  pages = {147--153},
  publisher = {{ences, The University of Liverpool, UK.}},
  institution = {{ences, The University of Liverpool, UK.}},
  urldate = {2017-08-08},
  __markedentry = {[bmckay52:6]},
  medline-pst = {ppublish},
  owner = {andreas},
  pmid = {12211582},
  keywords = {Biological,Disease Transmission,Humans,Infectious,Models},
  timestamp = {2016.09.01},
  file = {C\:\\Users\\andre\\Zotero\\storage\\7IDZBNK7\\begon02epidinf - A clarification of transmission terms in host-microparasite models - numbers, densities and areas.pdf;C\:\\Users\\andre\\Zotero\\storage\\X8VPXDN5\\begon02epidinf - A clarification of transmission terms in host-microparasite models - numbers, densities and areas.pdf}
}

@article{beldomenico2009,
  title = {The Vicious Circle and Infection Intensity: The Case of {{Trypanosoma}} Microti in Field Vole Populations.},
  author = {Beldomenico, Pablo M and Telfer, Sandra and Gebert, Stephanie and Lukomski, Lukasz and Bennett, Malcolm and Begon, Michael},
  year = {2009},
  month = sep,
  journal = {Epidemics},
  volume = {1},
  number = {3},
  pages = {162--167},
  issn = {1878-0067},
  doi = {10.1016/j.epidem.2009.05.002},
  abstract = {In natural populations, infection and condition may act synergistically to trigger a vicious circle: poor condition predisposes to host infections, which further reduce condition, and so on. If this vicious circle originates from a reduced resistance to infection, it will not only result in greater proneness to becoming infected of those that are in poorer condition, but it may also cause infections of higher intensity. Here, we investigate the temporal relationship between host condition and intensity of infection by a specific pathogen using as a system the dynamics of the protozoan Trypanosoma microti in field vole (Microtus agrestis) populations. With two years of longitudinal data from three monthly-sampled populations, we evaluated if individuals acquiring a high intensity of infection previously had lower haematological indicators of condition (red blood cells [RBCs] and lymphocyte counts) than those that acquired lower infection intensities. Also, the association of these indicators with past and present trypanosome blood levels was investigated. The individuals that developed high levels of parasitaemia were those that previously had low lymphocyte counts. Greater intensity of infection corresponded with lower RBCs only in low to moderate weight females, and no effect of intensity of infection on lymphocyte counts was observed. However, delayed effects of high trypanosome intensity were seen on both RBCs and lymphocytes. The vicious circle may also result in high infection intensity: individuals in poor condition are not only more likely to become infected by one pathogen; they may also be the most important source of infection for that and for other pathogens, and thus key protagonists for parasite dynamics.},
  citation-subset = {IM},
  completed = {2011-11-04},
  created = {2011-02-28},
  issn-linking = {1878-0067},
  nationality = {Netherlands},
  nlm-id = {101484711},
  owner = {NLM},
  pii = {S1755-4365(09)00029-2},
  pmid = {21352763},
  pubmodel = {Print-Electronic},
  pubstatus = {ppublish},
  revised = {2016-11-25},
  keywords = {Animals,Arvicolinae,blood,Disease Susceptibility,Erythrocyte Count,Female,Health Status,Lymphocyte Count,Male,parasitology,pathogenicity,Rodent Diseases,Severity of Illness Index,transmission,Trypanosoma,Trypanosomiasis,United Kingdom,veterinary},
  timestamp = {2017.02.20}
}

@article{beldomenico2010,
  title = {Disease Spread, Susceptibility and Infection Intensity: Vicious Circles?},
  shorttitle = {Disease Spread, Susceptibility and Infection Intensity},
  author = {Beldomenico, Pablo M. and Begon, Michael},
  year = {2010},
  month = jan,
  journal = {Trends in Ecology \& Evolution},
  volume = {25},
  number = {1},
  pages = {21--27},
  issn = {01695347},
  doi = {10.1016/j.tree.2009.06.015},
  urldate = {2017-08-08},
  langid = {english},
  keywords = {Animals,Disease Susceptibility,Disease Transmission,Host-Pathogen Interactions,Immunocompetence,Infectious},
  file = {C\:\\Users\\andre\\Zotero\\storage\\SEV9ZIEX\\beldomenico09tree - begon - Disease spread, susceptibility and infection intensity - vicious circles.pdf}
}

@book{bjornstad2018,
  title = {Epidemics: {{Models}} and {{Data}} Using {{R}}},
  shorttitle = {Epidemics},
  author = {Bj{\o}rnstad, Ottar N.},
  year = {2018},
  series = {Use {{R}}!},
  publisher = {{Springer International Publishing}},
  urldate = {2019-03-13},
  abstract = {This book is designed to be a practical study in infectious disease dynamics. The book offers an easy to follow implementation and analysis of mathematical epidemiology. The book focuses on recent case studies in order to explore various conceptual, mathematical, and statistical issues. The dynamics of infectious diseases shows a wide diversity of pattern. Some have locally persistent chains-of-transmission, others persist spatially in `consumer-resource metapopulations'. Some infections are prevalent among the young, some among the old and some are age-invariant. Temporally, some diseases have little variation in prevalence, some have predictable seasonal shifts and others exhibit violent epidemics that may be regular or irregular in their timing. Models and `models-with-data' have proved invaluable for understanding and predicting this diversity, and thence help improve intervention and control. Using mathematical models to understand infectious disease dynamics has a very rich history in epidemiology. The field has seen broad expansions of theories as well as a surge in real-life application of mathematics to dynamics and control of infectious disease. The chapters of Epidemics: Models and Data using R have been organized in a reasonably logical way: Chapters 1-10 is a mix and match of models, data and statistics pertaining to local disease dynamics; Chapters 11-13 pertains to spatial and spatiotemporal dynamics; Chapter 14 highlights similarities between the dynamics of infectious disease and parasitoid-host dynamics; Finally, Chapters 15 and 16 overview additional statistical methodology useful in studies of infectious disease dynamics. This book can be used as a guide for working with data, models and `models-and-data' to understand epidemics and infectious disease dynamics in space and time.},
  isbn = {978-3-319-97486-6},
  langid = {english},
  file = {C\:\\Users\\andre\\Zotero\\storage\\CGG6X56Z\\9783319974866.html}
}

@article{black1966,
  title = {Measles Endemicity in Insular Populations: Critical Community Size and Its Evolutionary Implication.},
  author = {Black, F L},
  year = {1966},
  month = jul,
  journal = {Journal of theoretical biology},
  volume = {11},
  number = {2},
  pages = {207--211},
  issn = {0022-5193},
  citation-subset = {IM},
  completed = {1967-07-26},
  created = {1967-07-26},
  issn-linking = {0022-5193},
  nationality = {England},
  nlm-id = {0376342},
  owner = {NLM},
  pii = {0022-5193(66)90161-5},
  pmid = {5965486},
  pubmodel = {Print},
  pubstatus = {ppublish},
  revised = {2010-11-18},
  keywords = {Atlantic Islands,Bermuda,Biological Evolution,epidemiology,Hawaii,Humans,Iceland,Measles,Pacific Islands,Population Surveillance},
  timestamp = {2017.02.20}
}

@book{bolker08,
  title = {Ecological {{Models}} and {{Data}} in {{R}}},
  author = {Bolker, Benjamin M.},
  year = {2008},
  month = jul,
  edition = {508},
  publisher = {{Princeton University Press}},
  isbn = {0-691-12522-8}
}

@article{brabazon2015,
  title = {Under-Reporting of Notifiable Infectious Disease Hospitalizations: {{Significant}} Improvements in the {{Irish}} Context},
  author = {Brabazon, E. D. and Sheridan, A. and Finnegan, P. and Carton, M. W. and Bedford, D.},
  year = {2015},
  journal = {Epidemiology and Infection},
  volume = {143},
  number = {6},
  pages = {1166--1174},
  issn = {14694409},
  doi = {10.1017/S0950268814001733},
  abstract = {Notification of infectious disease is essential for prompt public health action and epidemiological analysis. The aim of this study was to compare national hospitalization data to national notification data in order to assess if there was significant under-reporting of hospitalized notifiable infectious diseases in recent years in Ireland. All in-patient discharges from public hospitals in the Republic of Ireland from 2006 to 2011 with a principal diagnosis of a notifiable disease were compared with national notification data. It was found that only a potential 1.8\% of extra notifications could have arisen due to these hospitalization events and would represent a tenfold reduction on a previous estimate of under-reporting in the Irish context. Viral meningitis, viral encephalitis, bacterial meningitis not otherwise specified and malaria were the most common diseases for which there were more hospitalizations than notifications reported. The results of this study support the conclusion that the reduction in under-reporting can mainly be accounted for by the introduction of laboratories as notifiers in conjunction with the roll out of the Computerized Infectious Disease Reporting system (CIDR). However, for the diseases highlighted, the notification data underestimates the true burden of disease and this has implications for understanding the epidemiology of these diseases.},
  isbn = {1469-4409 (Electronic)\textbackslash r0950-2688 (Linking)},
  pmid = {25035904},
  keywords = {Health statistics,Notifiable infectious diseases,Surveillance}
}

@article{breban09,
  title = {The Role of Environmental Transmission in Recurrent Avian Influenza Epidemics.},
  author = {Breban, Romulus and Drake, John M. and Stallknecht, David E. and Rohani, Pejman},
  year = {2009},
  month = apr,
  journal = {PLoS Comput Biol},
  volume = {5},
  number = {4},
  pages = {e1000346},
  doi = {10.1371/journal.pcbi.1000346},
  abstract = {Avian influenza virus (AIV) persists in North American wild waterfowl, exhibiting major outbreaks every 2-4 years. Attempts to explain the patterns of periodicity and persistence using simple direct transmission models are unsuccessful. Motivated by empirical evidence, we examine the contribution of an overlooked AIV transmission mode: environmental transmission. It is known that infectious birds shed large concentrations of virions in the environment, where virions may persist for a long time. We thus propose that, in addition to direct fecal/oral transmission, birds may become infected by ingesting virions that have long persisted in the environment. We design a new host-pathogen model that combines within-season transmission dynamics, between-season migration and reproduction, and environmental variation. Analysis of the model yields three major results. First, environmental transmission provides a persistence mechanism within small communities where epidemics cannot be sustained by direct transmission only (i.e., communities smaller than the critical community size). Second, environmental transmission offers a parsimonious explanation of the 2-4 year periodicity of avian influenza epidemics. Third, very low levels of environmental transmission (i.e., few cases per year) are sufficient for avian influenza to persist in populations where it would otherwise vanish.},
  keywords = {Animals,Biological,Birds,Disease Outbreaks,Environmental Exposure,Influenza A virus,Influenza in Birds,Models,{Models, Biological},pathogenicity/physiology,transmission/virology,veterinary}
}

@article{breban10,
  ids = {brebanGeneralMultistrainModel2010a,brebanGeneralMultistrainModel2010b},
  title = {A General Multi-Strain Model with Environmental Transmission: Invasion Conditions for the Disease-Free and Endemic States.},
  author = {Breban, Romulus and Drake, John M. and Rohani, Pejman},
  year = {2010},
  month = jun,
  journal = {J Theor Biol},
  volume = {264},
  number = {3},
  pages = {729--736},
  publisher = {{Odum School of Ecology, University of Georgia, Athens, GA 30602, USA. romulus.breban@pasteur.fr}},
  institution = {{Odum School of Ecology, University of Georgia, Athens, GA 30602, USA. romulus.breban@pasteur.fr}},
  doi = {10.1016/j.jtbi.2010.03.005},
  __markedentry = {[bmckay52:6]},
  abstract = {Although many infectious diseases of humans and wildlife are transmitted via an environmental reservoir, the theory of environmental transmission remains poorly elaborated. Here we introduce an SIR-type multi-strain disease transmission model with perfect cross immunity where environmental transmission is broadly defined by three axioms. We establish the conditions under which a multi-strain endemic state is invaded by another strain which is both directly and environmentally transmitted. We discuss explicit forms for environmental transmission terms and apply our newly derived invasion conditions to a two-strain system. Then, we consider the case of two strains with matching basic reproduction numbers (i.e., R(0)), one directly transmitted only and the other both directly and environmentally transmitted, invading each other's endemic state. We find that the strain which is only directly transmitted can invade the endemic state of the strain with mixed transmission. However, the endemic state of the first strain is neutrally stable to invasion by the second strain. Thus, our results suggest that environmental transmission makes the endemic state less resistant to invasion.},
  langid = {english},
  medline-pst = {ppublish},
  pii = {S0022-5193(10)00128-1},
  pmid = {20211630},
  keywords = {Algorithms,Animals,Communicable Diseases,Disease Reservoirs,Endemic Diseases,epidemiology/transmission,Humans,Models,{Models, Theoretical},Population Dynamics,prevention /\&/ control,Theoretical}
}

@article{brown2010,
  title = {Prevalence of Antibodies to Type a Influenza Virus in Wild Avian Species Using Two Serologic Assays.},
  author = {Brown, Justin D. and Luttrell, M Page and Berghaus, Roy D. and Kistler, Whitney and Keeler, Shamus P. and Howey, Andrea and Wilcox, Benjamin and Hall, Jeffrey and Niles, Larry and Dey, Amanda and Knutsen, Gregory and Fritz, Kristin and Stallknecht, David E.},
  year = {2010},
  month = jul,
  journal = {J Wildl Dis},
  volume = {46},
  number = {3},
  pages = {896--911},
  doi = {10.7589/0090-3558-46.3.896},
  abstract = {Serologic testing to detect antibodies to avian influenza (AI) virus has been an underused tool for the study of these viruses in wild bird populations, which traditionally has relied on virus isolation and reverse transcriptase-polymerase chain reaction (RT-PCR). In a preliminary study, a recently developed commercial blocking enzyme-linked immunosorbent assay (bELISA) had sensitivity and specificity estimates of 82\% and 100\%, respectively, for detection of antibodies to AI virus in multiple wild bird species after experimental infection. To further evaluate the efficacy of this commercial bELISA and the agar gel immunodiffusion (AGID) test for AI virus antibody detection in wild birds, we tested 2,249 serum samples collected from 62 wild bird species, representing 10 taxonomic orders. Overall, the bELISA detected 25.4\% positive samples, whereas the AGID test detected 14.8\%. At the species level, the bELISA detected as many or more positive serum samples than the AGID in all 62 avian species. The majority of positive samples, detected by both assays, were from species that use aquatic habitats, with the highest prevalence from species in the orders Anseriformes and Charadriiformes. Conversely, antibodies to AI virus were rarely detected in the terrestrial species. The serologic data yielded by both assays are consistent with the known epidemiology of AI virus in wild birds and published reports of host range based on virus isolation and RT-PCR. The results of this research are also consistent with the aforementioned study, which evaluated the performance of the bELISA and AGID test on experimental samples. Collectively, the data from these two studies indicate that the bELISA is a more sensitive serologic assay than the AGID test for detecting prior exposure to AI virus in wild birds. Based on these results, the bELISA is a reliable species-independent assay with potentially valuable applications for wild bird AI surveillance.},
  langid = {english},
  pmid = {20688695},
  keywords = {Animals,Anseriformes,Antibodies,Birds,blood,Charadriiformes,classification/immunology,Enzyme-Linked Immunosorbent Assay,epidemiology,Female,Immunodiffusion,Influenza A virus,Influenza in Birds,Male,Reproducibility of Results,Sensitivity and Specificity,Seroepidemiologic Studies,Species Specificity,standards/veterinary,Viral,virology,Wild}
}

@article{brown2012,
  title = {The Consequences of Climate Change at an Avian Influenza 'Hotspot'.},
  author = {Brown, V. L. and Rohani, Pejman},
  year = {2012},
  month = dec,
  journal = {Biol Lett},
  volume = {8},
  number = {6},
  pages = {1036--1039},
  doi = {10.1098/rsbl.2012.0635},
  abstract = {Avian influenza viruses (AIVs) pose significant danger to human health. A key step in managing this threat is understanding the maintenance of AIVs in wild birds, their natural reservoir. Ruddy turnstones (Arenaria interpres) are an atypical bird species in this regard, annually experiencing high AIV prevalence in only one location-Delaware Bay, USA, during their spring migration. While there, they congregate on beaches, attracted by the super-abundance of horseshoe crab eggs. A relationship between ruddy turnstone and horseshoe crab (Limulus polyphemus) population sizes has been established, with a declining horseshoe crab population linked to a corresponding drop in ruddy turnstone population sizes. The effect of this interaction on AIV prevalence in ruddy turnstones has also been addressed. Here, we employ a transmission model to investigate how the interaction between these two species is likely to be altered by climate change. We explore the consequences of this modified interaction on both ruddy turnstone population size and AIV prevalence and show that, if climate change leads to a large enough mismatch in species phenology, AIV prevalence in ruddy turnstones will increase even as their population size decreases.},
  langid = {english},
  keywords = {Animals,Biological,Charadriiformes,Climate Change,Delaware,epidemiology,epidemiology/transmission,Horseshoe Crabs,Influenza in Birds,Models,physiology,physiology/virology,Population Dynamics,Prevalence,Seasons}
}

@article{brown2013,
  title = {Dissecting a Wildlife Disease Hotspot: The Impact of Multiple Host Species, Environmental Transmission and Seasonality in Migration, Breeding and Mortality.},
  author = {Brown, V. L. and Drake, J. M. and Stallknecht, D. E. and Brown, J. D. and Pedersen, K. and Rohani, P.},
  year = {2013},
  month = feb,
  journal = {J R Soc Interface},
  volume = {10},
  number = {79},
  pages = {20120804},
  doi = {10.1098/rsif.2012.0804},
  abstract = {Avian influenza viruses (AIVs) have been implicated in all human influenza pandemics in recent history. Despite this, surprisingly little is known about the mechanisms underlying the maintenance and spread of these viruses in their natural bird reservoirs. Surveillance has identified an AIV 'hotspot' in shorebirds at Delaware Bay, in which prevalence is estimated to exceed other monitored sites by an order of magnitude. To better understand the factors that create an AIV hotspot, we developed and parametrized a mechanistic transmission model to study the simultaneous epizootiological impacts of multi-species transmission, seasonal breeding, host migration and mixed transmission routes. We scrutinized our model to examine the potential for an AIV hotspot to serve as a 'gateway' for the spread of novel viruses into North America. Our findings identify the conditions under which a novel influenza virus, if introduced into the system, could successfully invade and proliferate.},
  langid = {english},
  keywords = {Animal,Animal Migration,Animals,{Animals, Wild},Biological,Bird Diseases,Charadriiformes,Delaware,Ducks,epidemiology,epidemiology/transmission,epidemiology/transmission/virology,immunology/virology,Influenza in Birds,Models,{Models, Biological},physiology,Prevalence,Seasons,Sexual Behavior,{Sexual Behavior, Animal},Species Specificity,Wild}
}

@article{brown2014,
  title = {Neutrality, Cross-Immunity and Subtype Dominance in Avian Influenza Viruses},
  author = {Brown, Vicki L. and Drake, John M. and Barton, Heather D. and Stallknecht, David E. and Brown, Justin D. and Rohani, Pejman},
  year = {2014},
  journal = {PloS One},
  volume = {9},
  number = {2},
  pages = {e88817},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0088817},
  abstract = {Avian influenza viruses (AIVs) are considered a threat for their potential to seed human influenza pandemics. Despite their acknowledged importance, there are significant unknowns regarding AIV transmission dynamics in their natural hosts, wild birds. Of particular interest is the difference in subtype dynamics between human and bird populations-in human populations, typically only two or three subtypes cocirculate, while avian populations are capable of simultaneously hosting a multitude of subtypes. One species in particular-ruddy turnstones (Arenaria interpres)--has been found to harbour a very wide range of AIV subtypes, which could make them a key player in the spread of new subtypes in wild bird populations. Very little is known about the mechanisms that drive subtype dynamics in this species, and here we address this gap in our knowledge. Taking advantage of two independent sources of data collected from ruddy turnstones in Delaware Bay, USA, we examine patterns of subtype diversity and dominance at this site. We compare these patterns to those produced by a stochastic, multi-strain transmission model to investigate possible mechanisms that are parsimonious with the observed subtype dynamics. We find, in agreement with earlier experimental work, that subtype differences are unnecessary to replicate the observed dynamics, and that neutrality alone is sufficient. We also evaluate the role of subtype cross-immunity and find that it is not necessary to generate patterns consistent with observations. This work offers new insights into the mechanisms behind subtype diversity and dominance in a species that has the potential to be a key player in AIV dynamics in wild bird populations.},
  langid = {english},
  pmcid = {PMC3934864},
  pmid = {24586401},
  keywords = {Animals,Birds,Delaware,immunology,immunology/virology,Influenza A virus,Influenza in Birds,Species Specificity}
}

@article{codeco01,
  title = {Endemic and Epidemic Dynamics of Cholera: The Role of the Aquatic Reservoir.},
  author = {Code{\c c}o, C T},
  year = {2001},
  journal = {BMC infectious diseases},
  volume = {1},
  pages = {1},
  issn = {1471-2334},
  abstract = {In the last decades, attention to cholera epidemiology increased, as cholera epidemics became a worldwide health problem. Detailed investigation of V. cholerae interactions with its host and with other organisms in the environment suggests that cholera dynamics is much more complex than previously thought. Here, I formulate a mathematical model of cholera epidemiology that incorporates an environmental reservoir of V. cholerae. The objective is to explore the role of the aquatic reservoir on the persistence of endemic cholera as well as to define minimum conditions for the development of epidemic and endemic cholera. The reproduction rate of cholera in a community is defined by the product of social and environmental factors. The importance of the aquatic reservoir depends on the sanitary conditions of the community. Seasonal variations of contact rates force a cyclical pattern of cholera outbreaks, as observed in some cholera-endemic communities. Further development on cholera modeling requires a better understanding of V. cholerae ecology and epidemiology. We need estimates of the prevalence of V. cholerae infection in endemic populations as well as a better description of the relationship between dose and virulence.},
  citation-subset = {IM},
  completed = {2002-10-10},
  created = {2002-09-23},
  issn-linking = {1471-2334},
  nationality = {England},
  nlm = {PMC29087},
  nlm-id = {100968551},
  owner = {NLM},
  pmc = {PMC29087},
  pmid = {11208258},
  pubmodel = {Print-Electronic},
  pubstatus = {ppublish},
  revised = {2014-06-13},
  keywords = {Biological,Brazil,Cholera,Disease Outbreaks,Endemic Diseases,epidemiology,Humans,Models,Prevalence,Theoretical,Water Pollution,Water Supply},
  timestamp = {2017.02.20}
}

@article{diekmann10,
  title = {The Construction of Next-Generation Matrices for Compartmental Epidemic Models},
  author = {Diekmann, O. and Heesterbeek, J. A. P. and Roberts, M. G.},
  year = {2010},
  month = jun,
  journal = {Journal of The Royal Society Interface},
  volume = {7},
  number = {47},
  pages = {873--885},
  issn = {1742-5689, 1742-5662},
  doi = {10.1098/rsif.2009.0386},
  urldate = {2017-08-08},
  langid = {english},
  keywords = {Animals,Artifacts,Basic Reproduction Number,Disease Outbreaks,Humans,Population Dynamics,statistics /\&/ numerical data},
  file = {C\:\\Users\\andre\\Zotero\\storage\\83WWAZ79\\diekmann10jrsi - The construction of next-generation matrices for compartmental epidemic models.pdf}
}

@article{dobson2008,
  title = {Homage to {{Linnaeus}}: How Many Parasites? {{How}} Many Hosts?},
  author = {Dobson, Andy and Lafferty, Kevin D. and Kuris, Armand M. and Hechinger, Ryan F. and Jetz, Walter},
  year = {2008},
  journal = {Proceedings of the National Academy of Sciences},
  publisher = {{National Acad Sciences}}
}

@article{dowell2001,
  title = {Seasonal Variation in Host Susceptibility and Cycles of Certain Infectious Diseases.},
  author = {Dowell, Scott F.},
  year = {2001},
  journal = {Emerging infectious diseases},
  volume = {7},
  number = {3},
  pages = {369},
  urldate = {2017-08-08},
  keywords = {Communicable Diseases,Disease Outbreaks,Disease Susceptibility,epidemiology,Humans,Seasons,Weather},
  file = {C\:\\Users\\andre\\Zotero\\storage\\II6JZDD9\\dowell01eid - Seasonal Variation in Host Susceptibility and cycles of certain infectious diseases.pdf}
}

@article{fine2011,
  title = {"{{Herd}} Immunity": A Rough Guide.},
  author = {Fine, Paul and Eames, Ken and Heymann, David L},
  year = {2011},
  month = apr,
  journal = {Clinical infectious diseases : an official publication of the Infectious Diseases Society of America},
  volume = {52},
  number = {7},
  pages = {911--916},
  issn = {1537-6591},
  doi = {10.1093/cid/cir007},
  abstract = {The term "herd immunity" is widely used but carries a variety of meanings. Some authors use it to describe the proportion immune among individuals in a population. Others use it with reference to a particular threshold proportion of immune individuals that should lead to a decline in incidence of infection. Still others use it to refer to a pattern of immunity that should protect a population from invasion of a new infection. A common implication of the term is that the risk of infection among susceptible individuals in a population is reduced by the presence and proximity of immune individuals (this is sometimes referred to as "indirect protection" or a "herd effect"). We provide brief historical, epidemiologic, theoretical, and pragmatic public health perspectives on this concept.},
  citation-subset = {IM},
  completed = {2011-07-18},
  created = {2011-03-23},
  issn-linking = {1058-4838},
  nationality = {United States},
  nlm-id = {9203213},
  owner = {NLM},
  pii = {cir007},
  pmid = {21427399},
  pubmodel = {Print},
  pubstatus = {ppublish},
  revised = {2011-03-23},
  keywords = {Communicable Disease Control,Communicable Diseases,Disease Transmission,Herd,Humans,Immunity,immunology,Infectious,prevention \& control,Terminology as Topic,transmission},
  timestamp = {2017.02.20}
}

@article{galvani05,
  title = {Epidemiology: Dimensions of Superspreading},
  shorttitle = {Epidemiology},
  author = {Galvani, Alison P. and May, Robert M.},
  year = {2005},
  journal = {Nature},
  volume = {438},
  number = {7066},
  pages = {293--295},
  urldate = {2017-08-08},
  keywords = {Biological,Contact Tracing,Disease Transmission,epidemiology/transmission,Female,HIV Infections,Humans,Infectious,Male,Models,Severe Acute Respiratory Syndrome,statistics /\&/ numerical data},
  file = {C\:\\Users\\andre\\Zotero\\storage\\2IZPFBPR\\galvani05nature - may - Epidemiology, dimensions of superspreading.pdf}
}

@article{handel07,
  title = {Neuraminidase {{Inhibitor Resistance}} in {{Influenza}}: {{Assessing}} the {{Danger}} of {{Its Generation}} and {{Spread}}},
  author = {Handel, Andreas and Longini, Ira M and Antia, Rustom},
  year = {2007},
  month = dec,
  journal = {PLoS Comput Biol},
  volume = {3},
  number = {12},
  pages = {e240},
  doi = {http://dx.doi.org/10.1371/journal.pcbi.0030240},
  abstract = {Neuraminidase Inhibitors (NI) are currently the most effective drugs against influenza. Recent cases of NI resistance are a cause for concern. To assess the danger of NI resistance, a number of studies have reported the fraction of treated patients from which resistant strains could be isolated. Unfortunately, those results strongly depend on the details of the experimental protocol. Additionally, knowing the fraction of patients harboring resistance is not too useful by itself. Instead, we want to know how likely it is that an infected patient can generate a resistant infection in a secondary host, and how likely it is that the resistant strain subsequently spreads. While estimates for these parameters can often be obtained from epidemiological data, such data is lacking for NI resistance in influenza. Here, we use an approach that does not rely on epidemiological data. Instead, we combine data from influenza infections of human volunteers with a mathematical framework that allows estimation of the parameters that govern the initial generation and subsequent spread of resistance. We show how these parameters are influenced by changes in drug efficacy, timing of treatment, fitness of the resistant strain, and details of virus and immune system dynamics. Our study provides estimates for parameters that can be directly used in mathematical and computational models to study how NI usage might lead to the emergence and spread of resistance in the population. We find that the initial generation of resistant cases is most likely lower than the fraction of resistant cases reported. However, we also show that the results depend strongly on the details of the within-host dynamics of influenza infections, and most importantly, the role the immune system plays. Better knowledge of the quantitative dynamics of the immune response during influenza infections will be crucial to further improve the results.},
  keywords = {antagonists /\&/ inhibitors,antagonists \& inhibitors,Antiviral Agents,Biological,Computer Simulation,Disease Outbreaks,Drug Resistance,drug therapy,drug therapy/epidemiology/immunology/transmission/virology,epidemiology,Human,Humans,Immunological,immunology,Influenza,Influenza A virus,methods,Models,Neuraminidase,physiology,prevention /\&/ control,prevention \& control,Risk Assessment,Risk Factors,therapeutic use,transmission,Viral,virology,Virus Replication},
  note = {Added to JabRef: 2007.12.20
\par
Owner: andreas},
  file = {C\:\\Users\\andre\\Zotero\\storage\\ERUZQJDR\\Handel et al. - 2007 - Neuraminidase Inhibitor Resistance in Influenza A.pdf}
}

@article{handel07a,
  ids = {handel2007,handel2007a,handel2007e,handel2007f,handelWhatBestControl2006,handelWhatBestControl2007b},
  title = {What Is the Best Control Strategy for Multiple Infectious Disease Outbreaks?},
  author = {Handel, Andreas and Longini, Jr, Ira M and Antia, Rustom},
  year = {2007},
  month = mar,
  journal = {Proc Biol Sci},
  volume = {274},
  number = {1611},
  pages = {833--837},
  publisher = {{Department of Biology, Emory University, Atlanta, GA 30322, USA. ahandel@emory.edu}},
  institution = {{Department of Biology, Emory University, Atlanta, GA 30322, USA. ahandel@emory.edu}},
  doi = {10.1098/rspb.2006.0015},
  __markedentry = {[bmckay52:6]},
  abstract = {Effective control of infectious disease outbreaks is an important public health goal. In a number of recent studies, it has been shown how different intervention measures like travel restrictions, school closures, treatment and prophylaxis might allow us to control outbreaks of diseases, such as SARS, pandemic influenza and others. In these studies, control of a single outbreak is considered. It is, however, not clear how one should handle a situation where multiple outbreaks are likely to occur. Here, we identify the best control strategy for such a situation. We further discuss ways in which such a strategy can be implemented to achieve additional public health objectives.},
  eissn = {1471-2954},
  langid = {english},
  medline-pst = {ppublish},
  pii = {C7Q758640Q515247},
  pmc = {PMC2093965},
  pmid = {17251095},
  publicationstatus = {published},
  keywords = {Biological,Communicable Disease Control,Computer Simulation,Decision Support Techniques,Disease Outbreaks,Models,prevention /\&/ control,prevention \& control},
  note = {tex.eissn: 1471-2954 tex.publicationstatus: published
\par
tex.eissn: 1471-2954 tex.publicationstatus: published},
  file = {C\:\\Users\\andre\\Zotero\\storage\\VTEDNXJ7\\Handel et al. - 2007 - What is the best control strategy for multiple inf.pdf}
}

@article{handel09a,
  ids = {handel2009a,handel2009i,handel2009j,handelAntiviralResistanceControl2009a,handelAntiviralResistanceControl2009c,handelAntiviralResistanceControl2009e},
  title = {Antiviral Resistance and the Control of Pandemic Influenza: The Roles of Stochasticity, Evolution and Model Details.},
  author = {Handel, Andreas and Longini, Ira M and Antia, Rustom},
  year = {2009},
  month = jan,
  journal = {J Theor Biol},
  volume = {256},
  number = {1},
  pages = {117--125},
  publisher = {{Department of Biology, Emory University, Atlanta, GA 30322, USA. andreas.handel@gmail.com}},
  institution = {{Department of Biology, Emory University, Atlanta, GA 30322, USA. andreas.handel@gmail.com}},
  doi = {http://dx.doi.org/10.1016/j.jtbi.2008.09.021},
  __markedentry = {[bmckay52:6]},
  abstract = {Antiviral drugs, most notably the neuraminidase inhibitors, are an important component of control strategies aimed to prevent or limit any future influenza pandemic. The potential large-scale use of antiviral drugs brings with it the danger of drug resistance evolution. A number of recent studies have shown that the emergence of drug-resistant influenza could undermine the usefulness of antiviral drugs for the control of an epidemic or pandemic outbreak. While these studies have provided important insights, the inherently stochastic nature of resistance generation and spread, as well as the potential for ongoing evolution of the resistant strain have not been fully addressed. Here, we study a stochastic model of drug resistance emergence and consecutive evolution of the resistant strain in response to antiviral control during an influenza pandemic. We find that taking into consideration the ongoing evolution of the resistant strain does not increase the probability of resistance emergence; however, it increases the total number of infecteds if a resistant outbreak occurs. Our study further shows that taking stochasticity into account leads to results that can differ from deterministic models. Specifically, we find that rapid and strong control cannot only contain a drug sensitive outbreak, it can also prevent a resistant outbreak from occurring. We find that the best control strategy is early intervention heavily based on prophylaxis at a level that leads to outbreak containment. If containment is not possible, mitigation works best at intermediate levels of antiviral control. Finally, we show that the results are not very sensitive to the way resistance generation is modeled.},
  medline-pst = {ppublish},
  pii = {S0022-5193(08)00492-X},
  pmc = {PMC2624577},
  pmid = {18952105},
  keywords = {Antiviral Agents,Biological,Biological Evolution,Disease Outbreaks,drug effects,drug effects/genetics,Drug Resistance,drug therapy,drug therapy/epidemiology/prevention /\&/ control,epidemiology,genetics,Human,Humans,Influenza,Influenza A virus,Models,Mutation,prevention /\&/ control,prevention \& control,Stochastic Processes,therapeutic use,Viral},
  note = {Added to JabRef: 2009.02.08
\par
Owner: andreas},
  file = {C\:\\Users\\andre\\Zotero\\storage\\5F9JL4VH\\Handel et al. - 2009 - Antiviral resistance and the control of pandemic i.pdf;C\:\\Users\\andre\\Zotero\\storage\\DHGATQ9A\\handel09jtb - Antiviral resistance and the control of pandemic influenza.pdf}
}

@article{handel09b,
  title = {Intervention Strategies for an Influenza Pandemic Taking into Account Secondary Bacterial Infections},
  author = {Handel, Andreas and Longini, Ira M and Antia, Rustom},
  year = {2009},
  journal = {Epidemics},
  volume = {1},
  number = {3},
  pages = {185--195},
  doi = {http://dx.doi.org/DOI: 10.1016/j.epidem.2009.09.001},
  abstract = {Influenza infections often predispose individuals to consecutive bacterial infections. Both during seasonal and pandemic influenza outbreaks, morbidity and mortality due to secondary bacterial infections can be substantial. With the help of a mathematical model, we investigate the potential impact of such bacterial infections during an influenza pandemic, and we analyze how antiviral and antibacterial treatment or prophylaxis affect morbidity and mortality. We consider different scenarios for the spread of bacteria, the emergence of antiviral resistance, and different levels of severity for influenza infections (1918-like and 2009-like). We find that while antibacterial intervention strategies are unlikely to play an important role in reducing the overall number of cases, such interventions can lead to a significant reduction in mortality and in the number of bacterial infections. Antibacterial interventions become even more important if one considers the-very likely-scenario that during a pandemic outbreak, influenza strains resistant to antivirals emerge. Overall, our study suggests that pandemic preparedness plans should consider intervention strategies based on antibacterial treatment or prophylaxis through drugs or vaccines as part of the overall control strategy. A major caveat for our results is the lack of data that would allow precise estimation of many of the model parameters. As our results show, this leads to very large uncertainty in model outcomes. As we discuss, precise assessment of the impact of antibacterial strategies during an influenza pandemic will require the collection of further data to better estimate key parameters, especially those related to the bacterial infections and the impact of antibacterial intervention strategies.},
  keywords = {Anti-Bacterial Agents,Antiviral Agents,Bacteria,Bacterial Infections,Biological,complications,complications/drug therapy/epidemiology,complications/drug therapy/epidemiology/prevention /\&/ control,Computer Simulation,drug effects,Drug Resistance,drug therapy,epidemiology,Human,Humans,Influenza,Influenza pandemic,Models,Orthomyxoviridae,pandemic,Pandemics,prevention /\&/ control,prevention \& control,therapeutic use,United States,Viral},
  note = {Added to JabRef: 2009.11.28
\par
Owner: andreas},
  file = {C\:\\Users\\andre\\Zotero\\storage\\RIFSF425\\Handel et al. - 2009 - Intervention strategies for an influenza pandemic .pdf}
}

@article{handel10,
  ids = {handel2010,handel2010a,handel2010b,handel2010c,handelQuantitativeUnderstandingWithinhost2009,handelQuantitativeUnderstandingWithinhost2010,handelQuantitativeUnderstandingWithinhost2010b},
  title = {Towards a Quantitative Understanding of the Within-Host Dynamics of Influenza {{A}} Infections.},
  author = {Handel, Andreas and Longini, Ira M and Antia, Rustom},
  year = {2010},
  month = jan,
  journal = {J R Soc Interface},
  volume = {7},
  number = {42},
  pages = {35--47},
  publisher = {{Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA 30602, USA. ahandel@uga.edu}},
  institution = {{Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA 30602, USA. ahandel@uga.edu}},
  doi = {http://dx.doi.org/10.1098/rsif.2009.0067},
  __markedentry = {[bmckay52:6]},
  abstract = {Although the influenza A virus has been extensively studied, a quantitative understanding of the infection dynamics is still lacking. To make progress in this direction, we designed several mathematical models and compared them with data from influenza A infections of mice. We find that the immune response (IR) plays an important part in the infection dynamics. Both an innate and an adaptive IR are required to provide adequate explanation of the data. In contrast, regrowth of epithelial cells did not seem to be an important mechanism on the time scale of the infection. We also find that different model variants for both innate and adaptive responses fit the data well, indicating the need for additional data to allow further model discrimination.},
  citation-subset = {IM},
  completed = {2010-02-02},
  country = {England},
  issn-linking = {1742-5662},
  medline-pst = {ppublish},
  nlm-id = {101217269},
  owner = {NLM},
  pii = {rsif.2009.0067},
  pmc = {PMC2839376},
  pmid = {19474085},
  pubmodel = {Print-Electronic},
  pubstatus = {ppublish},
  revised = {2018-11-13},
  keywords = {Animal,Animals,Computer Simulation,Disease Models,Epithelial Cells,Host-Pathogen Interactions,Human,Humans,Immunity,Immunological,immunology,immunology/pathogenicity/physiology,immunology/virology,Influenza,Influenza A virus,Innate,Mice,Models,pathogenicity,physiology,virology},
  note = {Added to JabRef: 2009.11.28
\par
Owner: andreas},
  file = {C\:\\Users\\andre\\Zotero\\storage\\DDCSIQ3K\\Handel et al. - 2010 - Towards a quantitative understanding of the within.pdf}
}

@article{handel15,
  title = {Crossing the Scale from Within-Host Infection Dynamics to between-Host Transmission Fitness: A Discussion of Current Assumptions and Knowledge.},
  author = {Handel, Andreas and Rohani, Pejman},
  year = {2015},
  month = aug,
  journal = {Philos Trans R Soc Lond B Biol Sci},
  volume = {370},
  number = {1675},
  doi = {10.1098/rstb.2014.0302},
  abstract = {The progression of an infection within a host determines the ability of a pathogen to transmit to new hosts and to maintain itself in the population. While the general connection between the infection dynamics within a host and the population-level transmission dynamics of pathogens is widely acknowledged, a comprehensive and quantitative understanding that would allow full integration of the two scales is still lacking. Here, we provide a brief discussion of both models and data that have attempted to provide quantitative mappings from within-host infection dynamics to transmission fitness. We present a conceptual framework and provide examples of studies that have taken first steps towards development of a quantitative framework that scales from within-host infections to population-level fitness of different pathogens. We hope to illustrate some general themes, summarize some of the recent advances and-maybe most importantly-discuss gaps in our ability to bridge these scales, and to stimulate future research on this important topic.},
  langid = {english},
  pmid = {26150668},
  keywords = {Animals,Biological,computational models,HIV Infections,Host-Pathogen Interactions,Human,Humans,Infection,infectious disease,Influenza,Models,multi-scale,transmission},
  file = {C\:\\Users\\andre\\Zotero\\storage\\2X99NREZ\\Handel and Rohani - 2015 - Crossing the scale from within-host infection dyna.pdf}
}

@article{handel2017,
  title = {Learning Infectious Disease Epidemiology in a Modern Framework},
  author = {Handel, Andreas},
  editor = {Ouellette, Francis},
  year = {2017},
  month = oct,
  journal = {PLOS Computational Biology},
  volume = {13},
  number = {10},
  pages = {e1005642},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1005642},
  urldate = {2018-12-31},
  langid = {english},
  file = {C\:\\Users\\andre\\Zotero\\storage\\B3HQSZPB\\Handel - 2017 - Learning infectious disease epidemiology in a mode.pdf}
}

@article{handel2020,
  title = {If Long-Term Suppression Is Not Possible, How Do We Minimize Mortality for {{COVID-19}} and Other Emerging Infectious Disease Outbreaks?},
  author = {Handel, Andreas and Miller, Joel and Ge, Yang and Fung, Isaac Chun-Hai},
  year = {2020},
  month = apr,
  journal = {medRxiv},
  pages = {2020.03.13.20034892},
  publisher = {{Cold Spring Harbor Laboratory Press}},
  doi = {10.1101/2020.03.13.20034892},
  urldate = {2020-08-11},
  abstract = {{$<$}p{$>$}If COVID-19 containment policies fail and social distancing measures cannot be sustained until vaccines becomes available, the next best approach is to use interventions that reduce mortality and prevent excess infections while allowing low-risk individuals to acquire immunity through natural infection until population level immunity is achieved. In such a situation, allowing some infections to occur in lower-risk groups might lead to an overall greater reduction in mortality than trying to protect everyone equally.{$<$}/p{$>$}},
  copyright = {\textcopyright{} 2020, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/},
  langid = {english},
  file = {C\:\\Users\\andre\\Zotero\\storage\\5L96LQSM\\2020.03.13.html}
}

@book{hastie11,
  title = {The Elements of Statistical Learning: {{Data}} Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)},
  author = {Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome},
  year = {2011},
  publisher = {{Springer}},
  date-modified = {2014-07-17 00:33:16 +0000}
}

@article{heffernan2005,
  title = {Perspectives on the Basic Reproductive Ratio},
  author = {Heffernan, J M and Smith, R J and Wahl, L M},
  year = {2005},
  month = sep,
  journal = {Journal of the Royal Society, Interface / the Royal Society},
  volume = {2},
  number = {4},
  eprint = {16849186},
  eprinttype = {pubmed},
  pages = {281--293},
  issn = {1742-5689},
  doi = {10.1098/rsif.2005.0042},
  urldate = {2011-08-26},
  abstract = {The basic reproductive ratio, R0, is defined as the expected number of secondary infections arising from a single individual during his or her entire infectious period, in a population of susceptibles. This concept is fundamental to the study of epidemiology and within-host pathogen dynamics. Most importantly, R0 often serves as a threshold parameter that predicts whether an infection will spread. Related parameters which share this threshold behaviour, however, may or may not give the true value of R0. In this paper we give a brief overview of common methods of formulating R0 and surrogate threshold parameters from deterministic, non-structured models. We also review common means of estimating R0 from epidemiological data. Finally, we survey the recent use of R0 in assessing emerging diseases, such as severe acute respiratory syndrome and avian influenza, a number of recent livestock diseases, and vector-borne diseases malaria, dengue and West Nile virus.},
  pmid = {16849186},
  keywords = {Animals,Biological,Communicable Diseases,Computer Simulation,Disease Outbreaks,Disease Susceptibility,Disease Transmission,Emerging,Epidemiologic Methods,epidemiology,Humans,Incidence,Infectious,methods,Models,Population Dynamics,Prevalence,Risk Assessment,Risk Factors,statistics /\&/ numerical data,statistics \& numerical data},
  file = {C\:\\Users\\andre\\Zotero\\storage\\SG933DTM\\heffernan05jrsoci - smith wahl - Perspectives on the basic reproductive ratio.pdf}
}

@article{hethcote00a,
  title = {The Mathematics of Infectious Diseases},
  author = {Hethcote, Herbert W.},
  year = {2000},
  journal = {SIAM review},
  volume = {42},
  number = {4},
  pages = {599--653}
}

@book{hilborn97,
  title = {The Ecological Detective : Confronting Models with Data},
  author = {Hilborn, Ray and Mangel, Marc},
  year = {1997},
  publisher = {{Princeton University Press}}
}

@article{hoare2008,
  title = {Sampling and Sensitivity Analyses Tools ({{SaSAT}}) for Computational Modelling.},
  author = {Hoare, Alexander and Regan, David G and Wilson, David P},
  year = {2008},
  journal = {Theor Biol Med Model},
  volume = {5},
  pages = {4},
  institution = {{National Centre in HIV Epidemiology and Clinical Research, The University of New South Wales, Sydney, New South Wales, 2010, Australia. ahoare@nchecr.unsw.edu.au}},
  doi = {10.1186/1742-4682-5-4},
  pii = {1742-4682-5-4},
  pmid = {18304361},
  timestamp = {2009.04.10}
}

@article{jelastopulu2010,
  title = {Underreporting of Communicable Diseases in the Prefecture of {{Achaia}}, {{Western Greece}}, 1999-2004 - {{Missed}} Opportunities for Early Intervention},
  author = {Jelastopulu, E. and Merekoulias, G. and Alexopoulos, E. C.},
  year = {2010},
  journal = {Eurosurveillance},
  volume = {15},
  number = {21},
  pages = {1999--2004},
  issn = {15607917},
  doi = {10.2807/ese.15.21.19579-en},
  abstract = {This study investigates the completeness of the reporting of infectious diseases in the prefecture of Achaia, western Greece in the period of 1999-2004. We collected hospital records relating to infectious diseases retrospectively from three major hospitals in the region and compared the records to corresponding records at the prefectural public health department (PHD). After record-linkage and cross-validation a total of 1,143 notifiable cases were identified in the three hospitals, of which 707 were reported to the PHD of Achaia, resulting in an observed underreporting of infectious diseases of 38\% during the study period. At prefecture level, a further 259 cases were notified by other sources, mainly by the fourth hospital of the region not included in our study, resulting in a total of 966 cases reported to the PHD; 73\% of these were reported from the three hospitals included in our study, 27\% were notified by the fourth hospital not included in our study and less then 0.3\% by physicians working in a private practice or health centre. Meningitis (51\%), tuberculosis (12\%) and salmonellosis (8\%) were the most frequently reported diseases followed by hospitalised cases of varicella (7\%), brucellosis (6\%) and hepatitis (6\%). During the study period, clustering of specific diseases like brucellosis, meningitis, mumps, and salmonellosis was observed, indicating possible outbreaks. Our results show that notification system needs to be improved, in order to ensure proper health resources allocation and implementation of focused prevention and control strategies.},
  isbn = {1025-496X},
  pmid = {20519103}
}

@article{kajita07,
  title = {Modelling an Outbreak of an Emerging Pathogen},
  author = {Kajita, Emily and Okano, Justin T. and Bodine, Erin N. and Layne, Scott P. and Blower, Sally},
  year = {2007},
  month = sep,
  journal = {Nature Reviews Microbiology},
  volume = {5},
  number = {9},
  pages = {700--709},
  issn = {1740-1526, 1740-1534},
  doi = {10.1038/nrmicro1660},
  urldate = {2017-08-08},
  keywords = {Community-Acquired Infections,Disease Outbreaks,epidemiology,Humans,Los Angeles,Methicillin Resistance,microbiology,Models,Prisons,Staphylococcal Infections,Staphylococcus aureus,Statistical,transmission},
  file = {C\:\\Users\\andre\\Zotero\\storage\\WHJGJPHZ\\kajita07natrevmic - blower - Modelling an outbreak of an emerging pathogen.pdf}
}

@article{kazerooni2018,
  title = {Under-Ascertainment, under-Reporting and Timeliness of {{Iranian}} Communicable Disease Surveillance System for Zoonotic Diseases},
  author = {Kazerooni, P. A. and Fararouei, M. and Nejat, M. and Akbarpoor, M. and Sedaghat, Z.},
  year = {2018},
  journal = {Public Health},
  volume = {154},
  pages = {130--135},
  publisher = {{Elsevier Ltd}},
  issn = {14765616},
  doi = {10.1016/j.puhe.2017.10.029},
  abstract = {Objectives The important steps in controlling infectious diseases are fast detection, proper treatment and on-time reporting of cases to the appropriate authorities. This study was conducted to evaluate the quality of Iranian communicable diseases surveillance system (CDSS) for zoonotic diseases in rural areas of Fars province (Iran's third largest province). Study design The three most important evaluation indices of CDSS, namely under-ascertainment, under-reporting and timeliness, for the three most common zoonotic diseases were measured using independent data source obtained from door-to-door interviews and patients' medical records. Methods Interviews were conducted with 48,771 households in rural areas of Fars province from April 2014 to March 2015. The medical and registration data were obtained from the CDSS and medical or health centres. Results Under-ascertainment, total under-reporting and timeliness (delay from the time of onset of symptoms to the time of visiting a medical or health centre, to the time of reporting visited cases to the highest level of CDSS) for leishmaniasis were 19.6\%, 42.5\% and 81.61 days (48.95 due to system delay), respectively. The corresponding indices for brucellosis were 0\%, 41.8\% and 56.5 days (22 due to system delay), respectively. For animal bite, the corresponding indices were 7.83\%, 13.07\% and less than 72 h, respectively (no system delay). Conclusions Although the status of case reporting and timeliness of surveillance system in public sectors providing medical services are clearly better than those of the private sectors, the indices are far from the level needed by CDSS to be able to detect and handle epidemics on time. Training health personnel, especially physicians, from public and private sectors to secure their cooperation along with routine and indebt evaluation are necessary to improve CDSS in Iran.},
  isbn = {0033-3506},
  pmid = {619743261},
  keywords = {Sensitivity,Surveillance,Timeliness,Zoonotic diseases}
}

@article{keeler12,
  title = {Persistence of Low Pathogenic Avian Influenza Viruses in Filtered Surface Water from Waterfowl Habitats in {{Georgia}}, {{USA}}.},
  author = {Keeler, Shamus P. and Berghaus, Roy D. and Stallknecht, David E.},
  year = {2012},
  month = oct,
  journal = {J Wildl Dis},
  volume = {48},
  number = {4},
  pages = {999--1009},
  doi = {10.7589/2011-11-314},
  abstract = {The natural reservoirs for avian influenza virus (AIV) are wild bird species of the orders Anseriformes and Charadriiformes. The primary route of transmission for wild birds is through fecally contaminated surface water on shared aquatic habitats. A distilled water model has shown that AIV remains infectious in water for weeks to months with pH, salinity, and temperature affecting stability. To evaluate the effect of pH, salinity, and temperature on AIV persistence in natural surface water, we measured the duration of infectivity for two common low pathogenic AIV subtypes in 15 filtered surface water samples collected from major waterfowl habitats in Georgia, USA. Trials were performed at three incubation temperatures 10, 17, and 28 C. Consistent with previous studies, pH and temperature had a significant effect on the stability of AIV in filtered surface water. Both viruses were less stable at warmer temperatures and in acidic water (pH {$<$}5.0). Due to the limited range of salinity of the field water samples, the role of salinity in AIV stability in surface water could not adequately be evaluated. Variations in persistence times between water samples with comparable pH and salinities indicated that other factors affect AIV stability in natural surface water. These results contribute to the current understanding of AIV persistence in aquatic habitats and may help in identifying areas with an increased likelihood of AIV persistence and potential transmission.},
  langid = {english},
  pmid = {23060501},
  keywords = {Animals,Birds,Disease Reservoirs,Female,Georgia,Hydrogen-Ion Concentration,Influenza A virus,Influenza in Birds,isolation /\&/ purification/pathogenicity/physiology,Male,Microbial Viability,Temperature,transmission/virology,veterinary/virology,Virus Shedding,Water Microbiology,Wild}
}

@article{keeler13,
  title = {Strain-Related Variation in the Persistence of Influenza {{A}} Virus in Three Types of Water: Distilled Water, Filtered Surface Water, and Intact Surface Water.},
  author = {Keeler, Shamus P. and Lebarbenchon, Camille and Stallknecht, David E.},
  year = {2013},
  journal = {Virol J},
  volume = {10},
  pages = {13},
  doi = {10.1186/1743-422X-10-13},
  abstract = {The persistence of influenza A (IA) virus in aquatic habitats has been demonstrated to be a determinant for virus transmission dynamics in wild duck populations. In this study, we investigated virus strain-related variation in persistence in water for nine wild duck isolated IA viruses of three subtypes (H3N8, H4N6, and H8N4).We experimentally estimated the loss of infectivity over time in three different types of water: distilled, filtered surface water, and intact surface water. All viruses persisted longest in distilled water followed by filtered surface water with markedly reduced durations of persistence observed in the intact surface water. Strain-related variations were observed in distilled and filtered surface water but limited variation was observed in the intact surface water.Our findings suggest that the role of surface water for long-term (between years) maintenance of AI viruses in the environment may be limited, and suggest that the physico-chemical characteristics of water, as well as microorganisms, may be of strong importance. Results also indicate that the extent of strain-related variation observed in distilled water may overestimate persistence abilities for IA viruses in the wild and supports the need to develop experiments that account for these effects to assess subtype, genotype, as well as spatial and temporal variation in the persistence of IA viruses in aquatic habitats.},
  langid = {english},
  keywords = {analysis/chemistry,Animals,Chick Embryo,classification/isolation /\&/ purification/pathogenicity,Ducks,Ecosystem,H3N8 Subtype,Hydrogen-Ion Concentration,Influenza A virus,Influenza A Virus,Madin Darby Canine Kidney Cells,Orthomyxoviridae Infections,Species Specificity,Time Factors,virology,Virus Cultivation,Water,Water Microbiology}
}

@article{keeler14,
  ids = {keelerAbioticFactorsAffecting2014a},
  title = {Abiotic Factors Affecting the Persistence of Avian Influenza Virus in Surface Waters of Waterfowl Habitats.},
  author = {Keeler, Shamus P. and Dalton, Melinda S. and Cressler, Alan M. and Berghaus, Roy D. and Stallknecht, David E.},
  year = {2014},
  month = may,
  journal = {Appl Environ Microbiol},
  volume = {80},
  number = {9},
  pages = {2910--2917},
  publisher = {{Southeast Cooperative Wildlife Disease Study, Department of Population Health, College of Veterinary Medicine, University of Georgia, Athens, Georgia, USA.}},
  institution = {{Southeast Cooperative Wildlife Disease Study, Department of Population Health, College of Veterinary Medicine, University of Georgia, Athens, Georgia, USA.}},
  doi = {10.1128/AEM.03790-13},
  __markedentry = {[bmckay52:6]},
  abstract = {Avian influenza (AI) virus can remain infectious in water for months, and virus-contaminated surface water is considered to be a source of infection within wild waterfowl populations. Previous work has characterized the effects of pH, salinity, and temperature on viral persistence in water, but most of that work was done with modified distilled water. The objective of this study was to identify the abiotic factors that influence the duration of AI virus persistence in natural surface water. Surface water samples were collected from 38 waterfowl habitats distributed across the United States. Samples were submitted to the U.S. Geological Survey National Water Quality Laboratory for chemical analysis and the University of Georgia for viral reduction time analysis. Samples were filtered with 0.22-?m filters, and the durations of persistence of three wild-bird-derived influenza A viruses within each water sample at 10, 17, and 28\textdegree C were determined. The effects of the surface water physicochemical factors on the duration of AI viral persistence in laboratory experiments were evaluated by multivariable linear regression with robust standard errors. The duration of AI virus persistence was determined to be longest in filtered surface water with a low temperature ({$<$}17\textdegree C), a neutral-to-basic pH (7.0 to 8.5), low salinity ({$<$}0.5 ppt), and a low ammonia concentration ({$<$}0.5 mg/liter). Our results also highlighted potential strain-related variation in the stability of AI virus in surface water. These results bring us closer to being able to predict the duration of AI virus persistence in surface water of waterfowl habitats.},
  langid = {english},
  medline-pst = {ppublish},
  pii = {AEM.03790-13},
  pmc = {PMC3993280},
  pmid = {24584247},
  keywords = {Animals,Anseriformes,chemistry/virology,classification/genetics/isolation /\&/ purification,Ecosystem,Fresh Water,growth /\&/ development/virology,Hydrogen-Ion Concentration,Influenza A virus,Influenza in Birds,Temperature,United States,virology,Wild}
}

@article{keeling02,
  title = {Understanding the Persistence of Measles: Reconciling Theory, Simulation and Observation.},
  author = {Keeling, Matt J and Grenfell, Bryan T},
  year = {2002},
  month = feb,
  journal = {Proceedings. Biological sciences / The Royal Society},
  volume = {269},
  number = {1489},
  pages = {335--343},
  issn = {0962-8452},
  abstract = {Ever since the pattern of localized extinction associated with measles was discovered by Bartlett in 1957, many models have been developed in an attempt to reproduce this phenomenon. Recently, the use of constant infectious and incubation periods, rather than the more convenient exponential forms, has been presented as a simple means of obtaining realistic persistence levels. However, this result appears at odds with rigorous mathematical theory; here we reconcile these differences. Using a deterministic approach, we parameterize a variety of models to fit the observed biennial attractor, thus determining the level of seasonality by the choice of model. We can then compare fairly the persistence of the stochastic versions of these models, using the 'best-fit' parameters. Finally, we consider the differences between the observed fade-out pattern and the more theoretically appealing 'first passage time'.},
  pmid = {11886620},
  keywords = {Biological,Cities,England,epidemiology,Humans,Measles,Models,Population Density,Seasons,Statistical,Stochastic Processes,Urban Population,Wales}
}

@article{keeling05,
  title = {Networks and Epidemic Models},
  author = {Keeling, Matt J. and Eames, Ken T.D.},
  year = {2005},
  month = sep,
  journal = {Journal of The Royal Society Interface},
  volume = {2},
  number = {4},
  pages = {295--307},
  issn = {1742-5689, 1742-5662},
  doi = {10.1098/rsif.2005.0051},
  urldate = {2017-08-08},
  keywords = {Animals,Biological,Communicable Diseases,Computer Simulation,Disease Outbreaks,Disease Susceptibility,Disease Transmission,Emerging,Epidemiologic Methods,epidemiology,Humans,Incidence,Infectious,methods,Models,Population Dynamics,Prevalence,Risk Assessment,Risk Factors,Social Support,statistics /\&/ numerical data},
  file = {C\:\\Users\\andre\\Zotero\\storage\\26AH3UZX\\keeling05jrsi - eames - networks and epidemic models - review.pdf;C\:\\Users\\andre\\Zotero\\storage\\SW2RXVPN\\keeling05jrsi - eames - networks and epidemic models - review.pdf}
}

@book{keeling07,
  title = {Modeling {{Infectious Diseases}} in {{Humans}} and {{Animals}}},
  author = {Keeling, Matt and Rohani, Pejman},
  year = {2007},
  publisher = {{Princeton University Press}},
  note = {Added to JabRef: 2009.07.18
\par
Owner: andreas}
}

@book{keeling08,
  title = {Modeling {{Infectious Diseases}} in {{Humans}} and {{Animals}}},
  author = {Keeling, Matt and Rohani, Pejman},
  year = {2007},
  publisher = {{Princeton University Press}}
}

@article{keeling1997,
  title = {Modelling the Persistence of Measles.},
  author = {Keeling, M J},
  year = {1997},
  month = dec,
  journal = {Trends in microbiology},
  volume = {5},
  number = {12},
  pages = {513--518},
  issn = {0966-842X},
  doi = {10.1016/S0966-842X(97)01147-5},
  abstract = {For vaccination programs to be effective, it is important to understand and predict the persistence of the disease. By considering the process at different scales (from the individual to the population level), several models allow the persistence of diseases, such as measles, to be captured.},
  citation-subset = {IM},
  completed = {1998-02-25},
  created = {1998-02-25},
  issn-linking = {0966-842X},
  nationality = {England},
  nlm-id = {9310916},
  owner = {NLM},
  pii = {S0966-842X(97)01147-5},
  pmid = {9447665},
  pubmodel = {Print},
  pubstatus = {ppublish},
  revised = {2006-11-15},
  keywords = {Biological,epidemiology,Genetic Heterogeneity,Humans,Measles,Models,Population,Time Factors},
  timestamp = {2017.02.20}
}

@article{kilpatrick2012,
  title = {Drivers, Dynamics, and Control of Emerging Vector-Borne Zoonotic Diseases.},
  author = {Kilpatrick, A Marm and Randolph, Sarah E},
  year = {2012},
  month = dec,
  journal = {Lancet (London, England)},
  volume = {380},
  number = {9857},
  pages = {1946--1955},
  issn = {1474-547X},
  doi = {10.1016/S0140-6736(12)61151-9},
  abstract = {Emerging vector-borne diseases are an important issue in global health. Many vector-borne pathogens have appeared in new regions in the past two decades, while many endemic diseases have increased in incidence. Although introductions and emergence of endemic pathogens are often considered to be distinct processes, many endemic pathogens are actually spreading at a local scale coincident with habitat change. We draw attention to key differences between dynamics and disease burden that result from increased pathogen transmission after habitat change and after introduction into new regions. Local emergence is commonly driven by changes in human factors as much as by enhanced enzootic cycles, whereas pathogen invasion results from anthropogenic trade and travel where and when conditions (eg, hosts, vectors, and climate) are suitable for a pathogen. Once a pathogen is established, ecological factors related to vector characteristics can shape the evolutionary selective pressure and result in increased use of people as transmission hosts. We describe challenges inherent in the control of vector-borne zoonotic diseases and some emerging non-traditional strategies that could be effective in the long term.},
  citation-subset = {AIM, IM},
  completed = {2012-12-17},
  created = {2012-12-03},
  issn-linking = {0140-6736},
  mid = {NIHMS495681},
  nationality = {England},
  nlm = {PMC3739480},
  nlm-id = {2985213R},
  owner = {NLM},
  pii = {S0140-6736(12)61151-9},
  pmc = {PMC3739480},
  pmid = {23200503},
  pubmodel = {Print},
  pubstatus = {ppublish},
  revised = {2017-02-20},
  keywords = {Animals,Blood-Borne Pathogens,Climate Change,Communicable Diseases,Disease Vectors,Emerging,epidemiology,Global Health,Humans,Incidence,prevention \& control,Risk Factors,Socioeconomic Factors,Tick Infestations,transmission,Zoonoses},
  timestamp = {2017.02.20}
}

@article{kirsch2016,
  title = {Impact of Interventions and the Incidence of Ebola Virus Disease in {{Liberia}}\textemdash Implications for Future Epidemics},
  author = {Kirsch, Thomas D and Moseson, Heidi and Massaquoi, Moses and Nyenswah, Tolbert G and Goodermote, Rachel and {Rodriguez-Barraquer}, Isabel and Lessler, Justin and Cumings, Derek and Peters, David H},
  year = {2016},
  month = oct,
  journal = {Health Policy and Planning},
  pages = {czw113},
  issn = {0268-1080, 1460-2237},
  doi = {10.1093/heapol/czw113},
  urldate = {2017-08-08},
  langid = {english},
  file = {C\:\\Users\\andre\\Zotero\\storage\\TEF6JCW5\\kirsch16hpp - Impact of interventions and the incidence of ebola virus disease in Liberia - implications for future epidemics.pdf}
}

@article{klepac2013,
  title = {Towards the Endgame and beyond: Complexities and Challenges for the Elimination of Infectious Diseases},
  shorttitle = {Towards the Endgame and Beyond},
  author = {Klepac, P. and Metcalf, C. J. E. and McLean, A. R. and Hampson, K.},
  year = {2013},
  month = jun,
  journal = {Philosophical Transactions of the Royal Society B: Biological Sciences},
  volume = {368},
  number = {1623},
  pages = {20120137--20120137},
  issn = {0962-8436, 1471-2970},
  doi = {10.1098/rstb.2012.0137},
  urldate = {2017-08-08},
  langid = {english},
  keywords = {Communicable Disease Control,Disease Eradication,Disease Susceptibility,epidemiology,Humans,immune escape,Immune Evasion,Mass Vaccination,methods,physiology,physiopathology,Public Health,statistics \& numerical data,susceptible build-up,trends,vaccination coverage,vaccine refusal},
  file = {C\:\\Users\\andre\\Zotero\\storage\\XBH4AJ73\\klepac13ptrsb - towards the endgame and beyond - complexities and challenges for the elimination of infectious diseases.pdf}
}

@article{klepac2015,
  title = {Six Challenges in the Eradication of Infectious Diseases.},
  author = {Klepac, Petra and Funk, Sebastian and Hollingsworth, T Deirdre and Metcalf, C Jessica E and Hampson, Katie},
  year = {2015},
  month = mar,
  journal = {Epidemics},
  volume = {10},
  pages = {97--101},
  issn = {1878-0067},
  doi = {10.1016/j.epidem.2014.12.001},
  abstract = {Eradication and elimination are increasingly a part of the global health agenda. Once control measures have driven infection to low levels, the ecology of disease may change posing challenges for eradication efforts. These challenges vary from identifying pockets of susceptibles, improving monitoring during and after the endgame, to quantifying the economics of disease eradication versus sustained control, all of which are shaped and influenced by processes of loss of immunity, susceptible build-up, emergence of resistance, population heterogeneities and non-compliance with control measures. Here we discuss how modelling can be used to address these challenges.},
  citation-subset = {IM},
  completed = {2016-01-15},
  created = {2015-04-06},
  issn-linking = {1878-0067},
  nationality = {Netherlands},
  nlm-id = {101484711},
  owner = {NLM},
  pii = {S1755-4365(14)00070-X},
  pmid = {25843393},
  pubmodel = {Print-Electronic},
  pubstatus = {ppublish},
  revised = {2016-12-17},
  keywords = {Communicable Disease Control,Communicable Diseases,Cost-Benefit Analysis,Disease Eradication,Disease Susceptibility,Dynamics,economics,Elimination,epidemiology,Heterogeneity,Humans,immunology,methods,Modelling,Models,Population Surveillance,Statistical,Surveillance},
  timestamp = {2017.02.20}
}

@article{klepac2020,
  title = {Contacts in Context: Large-Scale Setting-Specific Social Mixing Matrices from the {{BBC Pandemic}} Project},
  author = {Klepac, Petra and Kucharski, Adam J and Conlan, Andrew JK and Kissler, Stephen and Tang, Maria and Fry, Hannah and Gog, Julia R},
  year = {2020},
  journal = {medRxiv},
  eprint = {https://www.medrxiv.org/content/early/2020/03/05/2020.02.16.20023754.full.pdf},
  publisher = {{Cold Spring Harbor Laboratory Press}},
  doi = {10.1101/2020.02.16.20023754},
  abstract = {Social mixing patterns are crucial in driving transmission of infectious diseases and informing public health interventions to contain their spread. Age-specific social mixing is often inferred from surveys of self-recorded contacts which by design often have a very limited number of participants. In addition, such surveys are rare, so public health interventions are often evaluated by considering only one such study. Here we report detailed population contact patterns for United Kingdom based self-reported contact data from over 36,000 volunteers that participated in the massive citizen science project BBC Pandemic. The amount of data collected allows us generate fine-scale age-specific population contact matrices by context (home, work, school, other) and type (conversational or physical) of contact that took place. These matrices are highly relevant for informing prevention and control of new outbreaks, and evaluating strategies that reduce the amount of mixing in the population (such as school closures, social distancing, or working from home). In addition, they finally provide the possibility to use multiple sources of social mixing data to evaluate the uncertainty that stems from social mixing when designing public health interventions.Competing Interest StatementThe authors have declared no competing interest.Funding StatementAJK was supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (grant Number 206250/Z/17/Z).Author DeclarationsAll relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.YesAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesData in this manuscript is available in the form of contact matrices in XLS files.},
  elocation-id = {2020.02.16.20023754},
  timestamp = {2020-03-11}
}

@article{kuo09,
  title = {A Non-Foodborne Norovirus Outbreak among School Children during a Skiing Holiday, {{Austria}}, 2007.},
  author = {Kuo, Hung-Wei and Schmid, Daniela and Schwarz, Karin and Pichler, Anna-Margaretha and Klein, Heidelinde and K{\"o}nig, Christoph and {de Martin}, Alfred and Allerberger, Franz},
  year = {2009},
  journal = {Wiener klinische Wochenschrift},
  volume = {121},
  number = {3-4},
  pages = {120--124},
  issn = {0043-5325},
  doi = {10.1007/s00508-008-1131-1},
  __markedentry = {[bmckay52:6]},
  abstract = {Norovirus is increasingly recognized as a leading cause of outbreaks of foodborne disease. We report on an outbreak in Austria that reached a total of 176 cases, affecting pupils and teachers from four schools on a skiing holiday in a youth hostel in the province of Salzburg in December 2007. A questionnaire was sent to the four schools in order to obtain data from persons attending the school trip on disease status, clinical onset, duration of illness and hospitalization. A cohort study was undertaken to identify the sources of infection. The school trip attendees were interviewed by questionnaire or face-to-face on their exposure to food items from the menu provided by the hostel owner. Of the 284 school holiday-makers, 176 fitted the definition of an outbreak case (attack rate 61.9\%). A total of 264 persons on the ski holiday participated in the cohort study (response rate 93\%). The day-by-day food-specific analyses did not find any food items served on any of five days (December 8-12) of the holiday to be associated with infection risk. The day-specific risk analyses revealed Monday December 10 (RR: 9.04; 95\% CI: 6.02-13.6; P \textexclamdown{} 0.001) and Tuesday December 11 (RR: 3.37; 95\% CI: 2.56-4.43; P \textexclamdown{} 0.001) as the two most risky days for having being exposed to norovirus. According to the epidemiological investigation, airborne transmission of norovirus originating from the first vomiting case most probably initiated this outbreak; foodborne genesis was excluded. During recent years, norovirus has become increasingly established as the most important causative agent of epidemic gastroenteritis in holiday-makers all over Europe. Tourism is one of the primary industries in Austria. Timely involvement of the relevant public health authorities is essential in any outbreak of norovirus gastroenteritis, irrespective of its genesis.},
  citation-subset = {IM},
  completed = {2009-05-28},
  issn-linking = {0043-5325},
  nationality = {Austria},
  nlm-id = {21620870R},
  owner = {NLM},
  pmid = {19280137},
  pubmodel = {Print},
  pubstatus = {ppublish},
  revised = {2018-11-13},
  keywords = {Adult,Austria,Caliciviridae Infections,Child,Cluster Analysis,Cohort Studies,Contact Tracing,Cross-Sectional Studies,diagnosis,Diagnosis,Differential,Disease Outbreaks,epidemiology,Female,Food Microbiology,Foodborne Diseases,Gastroenteritis,Holidays,Humans,Male,Norovirus,Skiing,statistics \& numerical data,Surveys and Questionnaires,transmission,Travel}
}

@article{kurtz07a,
  title = {Evolutionary Ecology of Immune Defence in Copepods},
  author = {Kurtz, Joachim},
  year = {2007},
  month = jan,
  journal = {Journal of Plankton Research},
  volume = {29},
  number = {suppl{$_{1}$}},
  pages = {i27--i38},
  issn = {0142-7873},
  doi = {10.1093/plankt/fbl063},
  abstract = {Copepods are host to various species of parasites, but interactions of copepods with their parasites have rarely been studied in detail. In this review, first a brief introduction to the role of copepods as (intermediate) hosts of parasites is given and then the focus is on studies in the cyclopoid copepod Macrocyclops albidus and its tapeworm parasite Schistocephalus solidus. This host-parasite system has increasingly been studied for questions of ecology, behaviour and evolution of host-parasite interactions. To discuss the fundamental principles of such interactions, an approach based on ecological immunology was chosen, which is a new research area that aims at understanding causes of variation in immune defence in the light of ecology and evolution. Our studies indicate that the defence system of the copepod host is far more specific than is generally believed for invertebrates. This relates to a high degree of antigenic heterogeneity among the parasites, identified with the use of fluorescent lectins, which bind specific carbohydrate residues on the parasite surface. Natural selection on the parasite imposed by the copepod intermediate host might also be relevant for its virulence in the subsequent fish host. This implies that immunological interactions between hosts and parasites in their ecological context are potentially important aspects during host-parasite coevolution.},
  note = {10.1093/plankt/fbl063
\par
10.1093/plankt/fbl063}
}

@article{lebarbenchon11,
  ids = {lebarbenchonViralReplicationPersistence2011a,lebarbenchonViralReplicationPersistence2011b},
  title = {Viral Replication, Persistence in Water and Genetic Characterization of Two Influenza {{A}} Viruses Isolated from Surface Lake Water.},
  author = {Lebarbenchon, Camille and Yang, My and Keeler, Shamus P. and Ramakrishnan, Muthannan A. and Brown, Justin D. and Stallknecht, David E. and Sreevatsan, Srinand},
  year = {2011},
  journal = {PLoS One},
  volume = {6},
  number = {10},
  pages = {e26566},
  publisher = {{Southeastern Cooperative Wildlife Disease Study, Department of Population Health, College of Veterinary Medicine, University of Georgia, Athens, Georgia, United States of America. cleb@uga.edu}},
  institution = {{Southeastern Cooperative Wildlife Disease Study, Department of Population Health, College of Veterinary Medicine, University of Georgia, Athens, Georgia, United States of America. cleb@uga.edu}},
  doi = {10.1371/journal.pone.0026566},
  __markedentry = {[bmckay52:6]},
  abstract = {Water-borne transmission has been suggested as an important transmission mechanism for Influenza A (IA) viruses in wild duck populations; however, relatively few studies have attempted to detect IA viruses from aquatic habitats. Water-isolated viruses have rarely been genetically characterized and evaluation for persistence in water and infectivity in natural hosts has never been documented. In this study, we focused on two IA viruses (H3N8 and H4N6 subtypes) isolated from surface lake water in Minnesota, USA. We investigated the relative prevalence of the two virus subtypes in wild duck populations at the sampling site and their genetic relatedness to IA viruses isolated in wild waterbirds in North America. Viral persistence under different laboratory conditions (temperature and pH) and replication in experimentally infected Mallards (Anas platyrhynchos) were also characterized. Both viruses were the most prevalent subtype one year following their isolation in lake water. The viruses persisted in water for an extended time period at constant temperature (several weeks) but infectivity rapidly reduced under multiple freeze-thaw cycles. Furthermore, the two isolates efficiently replicated in Mallards. The complete genome characterization supported that these isolates originated from genetic reassortments with other IA viruses circulating in wild duck populations during the year of sampling. Based on phylogenetic analyses, we couldn't identify genetically similar viruses in duck populations in the years following their isolation from lake water. Our study supports the role for water-borne transmission for IA viruses but also highlights that additional field and experimental studies are required to support inter-annual persistence in aquatic habitats.},
  langid = {english},
  medline-pst = {ppublish},
  pii = {PONE-D-11-08831},
  pmc = {PMC3197669},
  pmid = {22028909},
  keywords = {Animals,classification/genetics/isolation /\&/ purification/physiology,Ducks,Genomics,H3N8 Subtype,Hydrogen-Ion Concentration,Influenza A virus,Influenza A Virus,Laboratories,Lakes,Phylogeny,Seasons,Temperature,virology,Virus Replication,Water Microbiology}
}

@article{li2020,
  title = {Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia.},
  author = {Li, Qun and Guan, Xuhua and Wu, Peng and Wang, Xiaoye and Zhou, Lei and Tong, Yeqing and Ren, Ruiqi and Leung, Kathy S M and Lau, Eric H Y and Wong, Jessica Y and Xing, Xuesen and Xiang, Nijuan and Wu, Yang and Li, Chao and Chen, Qi and Li, Dan and Liu, Tian and Zhao, Jing and Li, Man and Tu, Wenxiao and Chen, Chuding and Jin, Lianmei and Yang, Rui and Wang, Qi and Zhou, Suhua and Wang, Rui and Liu, Hui and Luo, Yingbo and Liu, Yuan and Shao, Ge and Li, Huan and Tao, Zhongfa and Yang, Yang and Deng, Zhiqiang and Liu, Boxi and Ma, Zhitao and Zhang, Yanping and Shi, Guoqing and Lam, Tommy T Y and Wu, Joseph T K and Gao, George F and Cowling, Benjamin J and Yang, Bo and Leung, Gabriel M and Feng, Zijian},
  year = {2020},
  month = jan,
  journal = {The New England journal of medicine},
  issn = {1533-4406},
  doi = {10.1056/NEJMoa2001316},
  abstract = {The initial cases of novel coronavirus (2019-nCoV)-infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP. We collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number. Among the first 425 patients with confirmed NCIP, the median age was 59 years and 56\% were male. The majority of cases (55\%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6\% of the subsequent cases. The mean incubation period was 5.2 days (95\% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95\% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95\% CI, 1.4 to 3.9). On the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.).},
  citation-subset = {AIM, IM},
  issn-linking = {0028-4793},
  nationality = {United States},
  nlm-id = {0255562},
  owner = {NLM},
  pmid = {31995857},
  pubmodel = {Print-Electronic},
  pubstatus = {aheadofprint},
  revised = {2020-01-29}
}

@article{lindsay2002,
  title = {Survival of Nonsporulated {{Toxoplasma}} Gondii Oocysts under Refrigerator Conditions},
  author = {Lindsay, D. S. and Blagburn, B. L. and Dubey, J. P.},
  year = {2002},
  journal = {Veterinary parasitology},
  volume = {103},
  number = {4},
  pages = {309--313},
  publisher = {{Elsevier}}
}

@article{lipsitch07,
  title = {Antiviral Resistance and the Control of Pandemic Influenza},
  author = {Lipsitch, Marc and Cohen, Ted and Murray, Megan and Levin, Bruce R},
  year = {2007},
  month = jan,
  journal = {PLoS medicine},
  volume = {4},
  number = {1},
  pages = {e15},
  doi = {10.1371/journal.pmed.0040015},
  __markedentry = {[bmckay52:6]},
  date-modified = {2008-03-06 11:49:08 -0500},
  pii = {06-PLME-RA-0490R2},
  pmid = {17253900},
  timestamp = {2007.02.24}
}

@article{lipsitch09,
  title = {Influenza Seasonality: Lifting the Fog.},
  author = {Lipsitch, Marc and Viboud, C{\'e}cile},
  year = {2009},
  month = mar,
  journal = {Proc Natl Acad Sci U S A},
  volume = {106},
  number = {10},
  pages = {3645--3646},
  doi = {10.1073/pnas.0900933106},
  langid = {english},
  pmid = {19276125},
  keywords = {Human,Humans,Influenza,Seasons,transmission,Vapor Pressure}
}

@article{lloyd-smith05,
  title = {Superspreading and the Effect of Individual Variation on Disease Emergence},
  author = {{Lloyd-Smith}, J. O. and Schreiber, S. J. and Kopp, P. E. and Getz, W. M.},
  year = {2005},
  month = nov,
  journal = {Nature},
  volume = {438},
  number = {7066},
  pages = {355--359},
  issn = {0028-0836, 1476-4687},
  doi = {10.1038/nature04153},
  urldate = {2017-08-08},
  keywords = {Biological,Contact Tracing,Disease Outbreaks,Disease Susceptibility,Disease Transmission,epidemiology,epidemiology/transmission/virology,Humans,Infectious,Models,Severe Acute Respiratory Syndrome,Singapore,statistics /\&/ numerical data},
  file = {C\:\\Users\\andre\\Zotero\\storage\\FRMIDM92\\lloyd-smith05nature - Superspreading and the effect of individual variation on disease emergence.pdf}
}

@article{lloyd-smith2005,
  title = {Should We Expect Population Thresholds for Wildlife Disease?},
  author = {{Lloyd-Smith}, James O. and Cross, Paul C. and Briggs, Cheryl J. and Daugherty, Matt and Getz, Wayne M. and Latto, John and Sanchez, Maria S. and Smith, Adam B. and Swei, Andrea},
  year = {2005},
  month = sep,
  journal = {Trends in Ecology \& Evolution},
  volume = {20},
  number = {9},
  pages = {511--519},
  issn = {01695347},
  doi = {10.1016/j.tree.2005.07.004},
  urldate = {2017-08-08},
  langid = {english},
  file = {C\:\\Users\\andre\\Zotero\\storage\\3VURP8PD\\lloyd-smith05tree - Should we expect population thresholds for wildlife disease.pdf}
}

@article{luz2010,
  title = {Modeling {{Transmission Dynamics}} and {{Control}} of {{Vector-Borne Neglected Tropical Diseases}}},
  author = {Luz, Paula M. and Struchiner, Claudio J. and Galvani, Alison P.},
  editor = {Brooker, Simon},
  year = {2010},
  month = oct,
  journal = {PLoS Neglected Tropical Diseases},
  volume = {4},
  number = {10},
  pages = {e761},
  issn = {1935-2735},
  doi = {10.1371/journal.pntd.0000761},
  urldate = {2017-08-08},
  langid = {english},
  keywords = {Animals,Disease Transmission,epidemiology,Humans,Infectious,Models,Neglected Diseases,Statistical,Tick-Borne Diseases,transmission,Tropical Climate},
  file = {C\:\\Users\\andre\\Zotero\\storage\\HMGVDFKI\\luz10pnegtropdis - Modeling Transmission Dynamics and Control of Vector-Borne Neglected Tropical Diseases.pdf}
}

@article{manfras2002,
  title = {Increased Activation and Oligoclonality of Peripheral {{CD8}}+ {{T}} Cells in the Chronic Human Helminth Infection Alveolar Echinococcosis},
  author = {Manfras, Burkhard J. and Reuter, Stefan and Wendland, Thomas and Kern, Peter},
  year = {2002},
  journal = {Infection and immunity},
  volume = {70},
  number = {3},
  pages = {1168--1174},
  publisher = {{Am Soc Microbiol}}
}

@article{marino08,
  title = {A Methodology for Performing Global Uncertainty and Sensitivity Analysis in Systems Biology},
  author = {Marino, Simeone and Hogue, Ian B. and Ray, Christian J. and Kirschner, Denise E.},
  year = {2008},
  month = sep,
  journal = {J. Theor. Biol},
  volume = {254},
  number = {1},
  pages = {178--196},
  doi = {10.1016/j.jtbi.2008.04.011},
  __markedentry = {[bmckay52:6]},
  ljournal = {Journal of Theoretical Biology},
  timestamp = {2010.10.25}
}

@article{marionm61a,
  title = {Further Observations on Survival of the Protozoan Parasite, {{Histomonas}} Meleagridis, and Eggs of Poultry Nematodes in Feces of Infected Birdes.},
  author = {MARION M, F. A. R. R.},
  year = {1961},
  journal = {The Cornell veterinarian},
  volume = {51},
  pages = {3}
}

@article{mccallum01,
  title = {How Should Pathogen Transmission Be Modelled?},
  author = {McCallum, Hamish and Barlow, Nigel and Hone, Jim},
  year = {2001},
  journal = {Trends in ecology \& evolution},
  volume = {16},
  number = {6},
  pages = {295--300},
  urldate = {2017-08-08},
  file = {C\:\\Users\\andre\\Zotero\\storage\\CHH46TXT\\mccallum01tree - How Should Pathogen Transmission be Modelled.pdf}
}

@book{mcelreath20,
  title = {Statistical {{Rethinking}}: {{A Bayesian Course}} with {{Examples}} in {{R}} and {{STAN}}},
  shorttitle = {Statistical {{Rethinking}}},
  author = {McElreath, Richard},
  year = {2020},
  month = mar,
  edition = {2nd edition},
  publisher = {{Chapman and Hall/CRC}},
  address = {{Boca Raton}},
  abstract = {Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work.  The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding.  The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses.  Features   Integrates working code into the main text   Illustrates concepts through worked data analysis examples   Emphasizes understanding assumptions and how assumptions are reflected in code   Offers more detailed explanations of the mathematics in optional sections   Presents examples of using the dagitty R package to analyze causal graphs   Provides the rethinking R package on the author's website and on GitHub.},
  isbn = {978-0-367-13991-9},
  langid = {english}
}

@article{mckenna1990,
  title = {The Detection of Anthelmintic Resistance by the Faecal Egg Count Reduction Test: An Examination of Some of the Factors Affecting Performance and Interpretation},
  author = {McKenna, P. B.},
  year = {1990},
  journal = {New Zealand Veterinary Journal},
  volume = {38},
  number = {4},
  pages = {142--147},
  publisher = {{Taylor \& Francis}}
}

@article{miao11a,
  title = {{{ON IDENTIFIABILITY OF NONLINEAR ODE MODELS AND APPLICATIONS IN VIRAL DYNAMICS}}},
  author = {Miao, Hongyu and Xia, Xiaohua and Perelson, Alan S and Wu, Hulin},
  year = {2011},
  month = jan,
  journal = {SIAM Review. Society for Industrial and Applied Mathematics},
  volume = {53},
  number = {1},
  eprint = {21785515},
  eprinttype = {pubmed},
  pages = {3--39},
  issn = {0036-1445},
  doi = {10.1137/090757009},
  urldate = {2011-09-22},
  abstract = {Ordinary differential equations (ODE) are a powerful tool for modeling dynamic processes with wide applications in a variety of scientific fields. Over the last 2 decades, ODEs have also emerged as a prevailing tool in various biomedical research fields, especially in infectious disease modeling. In practice, it is important and necessary to determine unknown parameters in ODE models based on experimental data. Identifiability analysis is the first step in determing unknown parameters in ODE models and such analysis techniques for nonlinear ODE models are still under development. In this article, we review identifiability analysis methodologies for nonlinear ODE models developed in the past one to two decades, including structural identifiability analysis, practical identifiability analysis and sensitivity-based identifiability analysis. Some advanced topics and ongoing research are also briefly reviewed. Finally, some examples from modeling viral dynamics of HIV, influenza and hepatitis viruses are given to illustrate how to apply these identifiability analysis methods in practice.},
  pmid = {21785515}
}

@article{mills04,
  title = {Transmissibility of 1918 Pandemic Influenza.},
  author = {Mills, Christina E. and Robins, James M. and Lipsitch, Marc},
  year = {2004},
  month = dec,
  journal = {Nature},
  volume = {432},
  number = {7019},
  pages = {904--906},
  doi = {10.1038/nature03063},
  abstract = {The 1918 influenza pandemic killed 20-40 million people worldwide, and is seen as a worst-case scenario for pandemic planning. Like other pandemic influenza strains, the 1918 A/H1N1 strain spread extremely rapidly. A measure of transmissibility and of the stringency of control measures required to stop an epidemic is the reproductive number, which is the number of secondary cases produced by each primary case. Here we obtained an estimate of the reproductive number for 1918 influenza by fitting a deterministic SEIR (susceptible-exposed-infectious-recovered) model to pneumonia and influenza death epidemic curves from 45 US cities: the median value is less than three. The estimated proportion of the population with A/H1N1 immunity before September 1918 implies a median basic reproductive number of less than four. These results strongly suggest that the reproductive number for 1918 pandemic influenza is not large relative to many other infectious diseases. In theory, a similar novel influenza subtype could be controlled. But because influenza is frequently transmitted before a specific diagnosis is possible and there is a dearth of global antiviral and vaccine stores, aggressive transmission reducing measures will probably be required.},
  langid = {english},
  pmid = {15602562},
  keywords = {20th Century,Biological,Cities,Communicable Disease Control,epidemiology,History,history/mortality/prevention /\&/ control/transmission,Human,Humans,Influenza,Models,Probability,Survival Analysis,Time Factors,United States}
}

@article{milwid2016,
  title = {Toward {{Standardizing}} a {{Lexicon}} of {{Infectious Disease Modeling Terms}}},
  author = {Milwid, Rachael and Steriu, Andreea and Arino, Julien and Heffernan, Jane and Hyder, Ayaz and Schanzer, Dena and Gardner, Emma and {Haworth-Brockman}, Margaret and {Isfeld-Kiely}, Harpa and Langley, Joanne M. and Moghadas, Seyed M.},
  year = {2016},
  month = sep,
  journal = {Frontiers in Public Health},
  volume = {4},
  issn = {2296-2565},
  doi = {10.3389/fpubh.2016.00213},
  urldate = {2017-08-08},
  keywords = {community of practice,infectious disease modeling,lexicon of terms,public health,reproduction number},
  file = {C\:\\Users\\andre\\Zotero\\storage\\N34WXREU\\milwid16fph - Toward Standardizing a Lexicon of Infectious Disease Modeling Terms.pdf}
}

@article{mossong2008,
  title = {Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases},
  author = {Mossong, Jo{\"e}l and Hens, Niel and Jit, Mark and Beutels, Philippe and Auranen, Kari and Mikolajczyk, Rafael and Massari, Marco and Salmaso, Stefania and Tomba, Gianpaolo Scalia and Wallinga, Jacco and others},
  year = {2008},
  journal = {PLoS medicine},
  volume = {5},
  number = {3},
  pages = {e74},
  urldate = {2017-08-08},
  keywords = {Adolescent,Adult,Aged,Child,Cross-Sectional Studies,epidemiology,Europe,Female,Humans,Infant,Male,Middle Aged,Models,Preschool,Respiratory Tract Infections,Social Support,Theoretical,transmission},
  file = {C\:\\Users\\andre\\Zotero\\storage\\EHKHWRGF\\mossong08plosmed - edmunds - Social contacts and mixing patterns relevant to the spread of infectious diseases.pdf}
}

@book{newman13,
  title = {Networks: {{An}} Introduction},
  author = {Newman, M. E. J.},
  year = {2013},
  edition = {Reprint. with corr.},
  publisher = {{Oxford Univ. Press}},
  address = {{Oxford [u.a.]}},
  isbn = {978-0-19-920665-0},
  note = {Hier auch sp\"ater erschienene, unver\"anderte Nachdrucke
\par
Hier auch sp\"ater erschienene, unver\"anderte Nachdrucke
\par
Hier auch sp\"ater erschienene, unver\"anderte Nachdrucke
\par
Hier auch sp\"ater erschienene, unver\"anderte Nachdrucke
\par
Hier auch sp\"ater erschienene, unver\"anderte Nachdrucke
\par
Hier auch sp\"ater erschienene, unver\"anderte Nachdrucke
\par
Hier auch sp\"ater erschienene, unver\"anderte Nachdrucke
\par
Hier auch sp\"ater erschienene, unver\"anderte Nachdrucke}
}

@article{olveda2014,
  title = {The Chronic Enteropathogenic Disease Schistosomiasis},
  author = {Olveda, David U. and Olveda, Remigio M. and McManus, Donald P. and Cai, Pengfei and Chau, Thao N. P. and Lam, Alfred K. and Li, Yuesheng and Harn, Donald A. and Vinluan, Marilyn L. and Ross, Allen G. P.},
  year = {2014},
  journal = {International Journal of Infectious Diseases},
  volume = {28},
  pages = {193--203},
  publisher = {{Elsevier}}
}

@article{pepin14,
  title = {Using Quantitative Disease Dynamics as a Tool for Guiding Response to Avian Influenza in Poultry in the {{United States}} of {{America}}.},
  author = {Pepin, K. M. and Spackman, E. and Brown, J. D. and Pabilonia, K. L. and Garber, L. P. and Weaver, J. T. and Kennedy, D. A. and Patyk, K. A. and Huyvaert, K. P. and Miller, R. S. and Franklin, A. B. and Pedersen, K. and Bogich, T. L. and Rohani, P. and Shriner, S. A. and Webb, C. T. and Riley, S.},
  year = {2014},
  month = mar,
  journal = {Prev Vet Med},
  volume = {113},
  number = {4},
  pages = {376--397},
  doi = {10.1016/j.prevetmed.2013.11.011},
  abstract = {Wild birds are the primary source of genetic diversity for influenza A viruses that eventually emerge in poultry and humans. Much progress has been made in the descriptive ecology of avian influenza viruses (AIVs), but contributions are less evident from quantitative studies (e.g., those including disease dynamic models). Transmission between host species, individuals and flocks has not been measured with sufficient accuracy to allow robust quantitative evaluation of alternate control protocols. We focused on the United States of America (USA) as a case study for determining the state of our quantitative knowledge of potential AIV emergence processes from wild hosts to poultry. We identified priorities for quantitative research that would build on existing tools for responding to AIV in poultry and concluded that the following knowledge gaps can be addressed with current empirical data: (1) quantification of the spatio-temporal relationships between AIV prevalence in wild hosts and poultry populations, (2) understanding how the structure of different poultry sectors impacts within-flock transmission, (3) determining mechanisms and rates of between-farm spread, and (4) validating current policy-decision tools with data. The modeling studies we recommend will improve our mechanistic understanding of potential AIV transmission patterns in USA poultry, leading to improved measures of accuracy and reduced uncertainty when evaluating alternative control strategies.},
  langid = {english},
  pmid = {24462191},
  keywords = {Animal Husbandry,Animals,Birds,Disease Reservoirs,epidemiology/prevention /\&/ control/transmission,Influenza A virus,Influenza in Birds,legislation /\&/ jurisprudence/organization /\&/ administration,physiology,Poultry,Poultry Diseases,United States,veterinary}
}

@article{roberts2007,
  title = {The Pluses and Minuses of {{R0}}},
  author = {Roberts, M.G},
  year = {2007},
  month = oct,
  journal = {Journal of The Royal Society Interface},
  volume = {4},
  number = {16},
  pages = {949--961},
  doi = {10.1098/rsif.2007.1031},
  urldate = {2011-09-26},
  abstract = {The concept of the basic reproduction number (0) occupies a central place in epidemic theory. The value of 0 determines the proportion of the population that becomes infected over the course of a (modelled) epidemic. In many models, (i) an endemic infection can persist only if 0{$>$}1, (ii) the value of 0 provides a direct measure of the control effort required to eliminate the infection, and (iii) pathogens evolve to maximize their value of 0. These three statements are not universally true. In this paper, some exceptions to them are discussed, based on the extensions of the SIR model.},
  keywords = {Animal Diseases,Animals,Biological,Communicable Diseases,Disease Transmission,epidemiology,Infectious,Models,Statistical,statistics \& numerical data,Wild},
  file = {C\:\\Users\\andre\\Zotero\\storage\\6T2K8T8R\\roberts07jrsi - The pluses and minuses of R0 - review.pdf;C\:\\Users\\andre\\Zotero\\storage\\ZCZWTQVJ\\Roberts - 2007 - The pluses and minuses of 0.pdf;C\:\\Users\\andre\\Zotero\\storage\\AZTINRSG\\949.html}
}

@article{roche11,
  ids = {roche2011,rocheAgentbasedModelStudy2011a},
  title = {An Agent-Based Model to Study the Epidemiological and Evolutionary Dynamics of {{Influenza}} Viruses.},
  author = {Roche, Benjamin and Drake, John M. and Rohani, Pejman},
  year = {2011},
  journal = {BMC Bioinformatics},
  volume = {12},
  pages = {87},
  publisher = {{Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA. benjamin.roche@ird.fr}},
  institution = {{Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA. benjamin.roche@ird.fr}},
  doi = {10.1186/1471-2105-12-87},
  __markedentry = {[bmckay52:6]},
  abstract = {Influenza A viruses exhibit complex epidemiological patterns in a number of mammalian and avian hosts. Understanding transmission of these viruses necessitates taking into account their evolution, which represents a challenge for developing mathematical models. This is because the phrasing of multi-strain systems in terms of traditional compartmental ODE models either requires simplifying assumptions to be made that overlook important evolutionary processes, or leads to complex dynamical systems that are too cumbersome to analyse.Here, we develop an Individual-Based Model (IBM) in order to address simultaneously the ecology, epidemiology and evolution of strain-polymorphic pathogens, using Influenza A viruses as an illustrative example.We carry out careful validation of our IBM against comparable mathematical models to demonstrate the robustness of our algorithm and the sound basis for this novel framework. We discuss how this new approach can give critical insights in the study of influenza evolution.},
  langid = {english},
  medline-pst = {epublish},
  pii = {1471-2105-12-87},
  pmc = {PMC3078862},
  pmid = {21450071},
  keywords = {Biological,Biological Evolution,epidemiology/transmission/virology,genetics,Human,Humans,Influenza,Influenza A virus,Models,Reproducibility of Results}
}

@article{roche11a,
  title = {The Curse of the {{Pharaoh}} Revisited: Evolutionary Bi-Stability in Environmentally Transmitted Pathogens.},
  author = {Roche, Benjamin and Drake, John M. and Rohani, Pejman},
  year = {2011},
  month = jun,
  journal = {Ecol Lett},
  volume = {14},
  number = {6},
  pages = {569--575},
  doi = {10.1111/j.1461-0248.2011.01619.x},
  abstract = {It is increasingly evident that for a number of high-profile pathogens, transmission involves both direct and environmental pathways. Much of the distinguished evolutionary theory has, however, focused on each of transmission component separately. Herein, we use the framework of adaptive dynamics to study the evolutionary consequences of mixed transmission. We find that environmental transmission can select for increased virulence when direct transmission is low. Increasing the efficiency of direct transmission gives rise to an evolutionary bi-stability, with coexistence of different levels of virulence. We conclude that the overlooked contribution of environmental transmission may explain the curious appearance of high virulence in pathogens that are typically only moderately pathogenic, as observed for avian influenza viruses and cholera.},
  langid = {english},
  pmid = {21496194},
  keywords = {Biological,Biological Evolution,Disease Transmission,Environment,Genetic,genetics,genetics/pathogenicity,Infectious,Influenza A virus,Models,Population Dynamics,Selection,Vibrio cholerae,Virulence}
}

@article{roche14,
  ids = {rocheAdaptiveEvolutionEnvironmental2014a},
  title = {Adaptive Evolution and Environmental Durability Jointly Structure Phylodynamic Patterns in Avian Influenza Viruses.},
  author = {Roche, Benjamin and Drake, John M. and Brown, Justin and Stallknecht, David E. and Bedford, Trevor and Rohani, Pejman},
  year = {2014},
  month = aug,
  journal = {PLoS Biol},
  volume = {12},
  number = {8},
  pages = {e1001931},
  publisher = {{Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America; Center for the Study of Complex Systems, University of Michigan, Ann Arbor, Michigan, United States of America; Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America.}},
  institution = {{Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America; Center for the Study of Complex Systems, University of Michigan, Ann Arbor, Michigan, United States of America; Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America.}},
  doi = {10.1371/journal.pbio.1001931},
  __markedentry = {[bmckay52:6]},
  abstract = {Avian influenza viruses (AIVs) have been pivotal to the origination of human pandemic strains. Despite their scientific and public health significance, however, there remains much to be understood about the ecology and evolution of AIVs in wild birds, where major pools of genetic diversity are generated and maintained. Here, we present comparative phylodynamic analyses of human and AIVs in North America, demonstrating (i) significantly higher standing genetic diversity and (ii) phylogenetic trees with a weaker signature of immune escape in AIVs than in human viruses. To explain these differences, we performed statistical analyses to quantify the relative contribution of several potential explanations. We found that HA genetic diversity in avian viruses is determined by a combination of factors, predominantly subtype-specific differences in host immune selective pressure and the ecology of transmission (in particular, the durability of subtypes in aquatic environments). Extending this analysis using a computational model demonstrated that virus durability may lead to long-term, indirect chains of transmission that, when coupled with a short host lifespan, can generate and maintain the observed high levels of genetic diversity. Further evidence in support of this novel finding was found by demonstrating an association between subtype-specific environmental durability and predicted phylogenetic signatures: genetic diversity, variation in phylogenetic tree branch lengths, and tree height. The conclusion that environmental transmission plays an important role in the evolutionary biology of avian influenza viruses-a manifestation of the "storage effect"-highlights the potentially unpredictable impact of wildlife reservoirs for future human pandemics and the need for improved understanding of the natural ecology of these viruses.},
  langid = {english},
  medline-pst = {epublish},
  pii = {PBIOLOGY-D-14-00514},
  pmc = {PMC4130664},
  pmid = {25116957},
  keywords = {Adaptation,Animals,Base Sequence,Birds,Environment,Genetic Variation,genetics,Hemagglutinin Glycoproteins,Humans,Influenza A virus,Influenza Virus,Models,Molecular,North America,Phylogeny,Physiological,Regression Analysis,virology}
}

@article{rohani09,
  title = {Environmental Transmission of Low Pathogenicity Avian Influenza Viruses and Its Implications for Pathogen Invasion.},
  author = {Rohani, Pejman and Breban, Romulus and Stallknecht, David E and Drake, John M},
  year = {2009},
  month = jun,
  journal = {Proc Natl Acad Sci U S A},
  volume = {106},
  number = {25},
  pages = {10365--10369},
  doi = {http://dx.doi.org/10.1073/pnas.0809026106},
  abstract = {Understanding the transmission dynamics and persistence of avian influenza viruses (AIVs) in the wild is an important scientific and public health challenge because this system represents both a reservoir for recombination and a source of novel, potentially human-pathogenic strains. The current paradigm locates all important transmission events on the nearly direct fecal/oral bird-to-bird pathway. In this article, on the basis of overlooked evidence, we propose that an environmental virus reservoir gives rise to indirect transmission. This transmission mode could play an important epidemiological role. Using a stochastic model, we demonstrate how neglecting environmentally generated transmission chains could underestimate the explosiveness and duration of AIV epidemics. We show the important pathogen invasion implications of this phenomenon: the nonnegligible probability of outbreak even when direct transmission is absent, the long-term infectivity of locations of prior outbreaks, and the role of environmental heterogeneity in risk.},
  keywords = {Animals,Biological,Birds,Environment,epidemiology/transmission/virology,Humans,Influenza in Birds,Models,{Models, Biological},Orthomyxoviridae,pathogenicity,virology},
  note = {Added to JabRef: 2009.07.15
\par
Owner: andreas}
}

@book{saltelli2004,
  title = {Sensitivity {{Analysis}} in {{Practice}}: {{A Guide}} to {{Assessing Scientific Models}}},
  shorttitle = {Sensitivity {{Analysis}} in {{Practice}}},
  author = {Saltelli, A. and Tarantola, Stefano and Campolongo, Francesca and Ratto, Marco},
  year = {2004},
  month = apr,
  edition = {1},
  publisher = {{Wiley}},
  isbn = {0-470-87093-1}
}

@article{shaman10,
  ids = {shamanAbsoluteHumiditySeasonal2010a},
  title = {Absolute Humidity and the Seasonal Onset of Influenza in the Continental {{United States}}.},
  author = {Shaman, Jeffrey and Pitzer, Virginia E. and Viboud, C{\'e}cile and Grenfell, Bryan T. and Lipsitch, Marc},
  year = {2010},
  month = feb,
  journal = {PLoS Biol},
  volume = {8},
  number = {2},
  pages = {e1000316},
  publisher = {{College of Oceanic and Atmospheric Sciences, Oregon State University, Corvallis, Oregon, United States of America.}},
  institution = {{College of Oceanic and Atmospheric Sciences, Oregon State University, Corvallis, Oregon, United States of America.}},
  doi = {10.1371/journal.pbio.1000316},
  __markedentry = {[bmckay52:6]},
  abstract = {Much of the observed wintertime increase of mortality in temperate regions is attributed to seasonal influenza. A recent reanalysis of laboratory experiments indicates that absolute humidity strongly modulates the airborne survival and transmission of the influenza virus. Here, we extend these findings to the human population level, showing that the onset of increased wintertime influenza-related mortality in the United States is associated with anomalously low absolute humidity levels during the prior weeks. We then use an epidemiological model, in which observed absolute humidity conditions temper influenza transmission rates, to successfully simulate the seasonal cycle of observed influenza-related mortality. The model results indicate that direct modulation of influenza transmissibility by absolute humidity alone is sufficient to produce this observed seasonality. These findings provide epidemiological support for the hypothesis that absolute humidity drives seasonal variations of influenza transmission in temperate regions.},
  langid = {english},
  medline-pst = {epublish},
  owner = {andreas},
  pmid = {20186267},
  keywords = {epidemiology/transmission/virology,Human,Humans,Humidity,Influenza,Models,Seasons,Statistical,United States},
  timestamp = {2016.08.04}
}

@article{thapa2017,
  title = {Survival and Development of Chicken Ascarid Eggs in Temperate Pastures},
  author = {Thapa, Sundar and Thamsborg, Stig M. and Meyling, Nicolai V. and Dhakal, Suraj and Mejer, Helena},
  year = {2017},
  journal = {Parasitology},
  volume = {144},
  number = {9},
  pages = {1243--1252},
  publisher = {{Cambridge University Press}}
}

@article{tognotti2013,
  title = {Lessons from the {{History}} of {{Quarantine}}, from {{Plague}} to {{Influenza A}}},
  author = {Tognotti, Eugenia},
  year = {2013},
  month = feb,
  journal = {Emerging Infectious Diseases},
  volume = {19},
  number = {2},
  pages = {254--259},
  issn = {1080-6040, 1080-6059},
  doi = {10.3201/eid1902.120312},
  urldate = {2017-08-08},
  keywords = {17th Century,18th Century,19th Century,20th Century,21st Century,Disease Outbreaks,epidemiology,Europe,history,History,Human,Humans,Influenza,Influenza A virus,Medieval,Plague,prevention \& control,Quarantine},
  file = {C\:\\Users\\andre\\Zotero\\storage\\BV7PTTGZ\\tognotti13eid - Lessons from the History of Quarantine, from Plague to Influenza A.pdf}
}

@article{viboud06,
  ids = {viboud2006,viboudTransmissibilityMortalityImpact2006a,viboudTransmissibilityMortalityImpact2006c},
  title = {Transmissibility and Mortality Impact of Epidemic and Pandemic Influenza, with Emphasis on the Unusually Deadly 1951 Epidemic.},
  author = {Viboud, C{\'e}cile and Tam, Theresa and Fleming, Douglas and Handel, Andreas and Miller, Mark A. and Simonsen, Lone},
  year = {2006},
  month = nov,
  journal = {Vaccine},
  volume = {24},
  number = {44-46},
  pages = {6701--6707},
  publisher = {{Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA. viboudc@mail.nih.gov}},
  institution = {{Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA. viboudc@mail.nih.gov}},
  doi = {10.1016/j.vaccine.2006.05.067},
  __markedentry = {[bmckay52:6]},
  abstract = {There are important gaps in our current understanding of the influenza virus behavior. In particular, it remains unclear why some inter-pandemic seasons are associated with unusually high mortality impact, sometimes comparable to that of pandemics. Here we compare the epidemiological patterns of the unusually deadly 1951 influenza epidemic (A/H1N1) in England and Wales and Canada with those of surrounding epidemic and pandemic seasons, in terms of overall mortality impact and transmissibility. Based on the statistical and mathematical analysis of vital statistics and morbidity epidemic curves in these two countries, we show that the 1951 epidemic was associated with both higher mortality impact and higher transmissibility than the 1957 and 1968 pandemics. Surprisingly in Liverpool, considered the 'epicenter' of the severe 1951 epidemic, the mortality impact and transmissibility even surpassed the 1918 pandemic.},
  langid = {english},
  medline-pst = {ppublish},
  owner = {andreas},
  pii = {S0264-410X(06)00648-7},
  pmid = {16806596},
  keywords = {20th Century,Biological,Communicable Disease Control,Disease Outbreaks,epidemiology/history/mortality/transmission,H1N1 Subtype,History,history/statistics /\&/ numerical data,Human,Humans,Influenza,Influenza A Virus,Models},
  timestamp = {2015.09.11},
  file = {C\:\\Users\\andre\\Zotero\\storage\\F78R8BWD\\Viboud et al. - 2006 - Transmissibility and mortality impact of epidemic .pdf}
}

@article{vyas07,
  title = {Behavioral Changes Induced by {{Toxoplasma}} Infection of Rodents Are Highly Specific to Aversion of Cat Odors},
  author = {Vyas, Ajai and Kim, Seon-Kyeong and Giacomini, Nicholas and Boothroyd, John C. and Sapolsky, Robert M.},
  year = {2007},
  journal = {Proceedings of the National Academy of Sciences},
  volume = {104},
  number = {15},
  pages = {6442--6447},
  publisher = {{National Acad Sciences}},
  __markedentry = {[bmckay52:6]}
}

@book{vynnycky10,
  ids = {vynnycky10a,vynnyckyIntroductionInfectiousDisease2010a,vynnyckyIntroductionInfectiousDisease2010b},
  title = {An Introduction to Infectious Disease Modelling},
  author = {Vynnycky, Emilia and White, Richard},
  year = {2010},
  publisher = {{Oxford University Press}},
  __markedentry = {[bmckay52:6]},
  owner = {andreas},
  timestamp = {2017.03.25}
}

@inproceedings{walker2005,
  title = {Neuroparasitic Infections: Cestodes, Trematodes, and Protozoans},
  booktitle = {Seminars in Neurology},
  author = {Walker, M. D. and Zunt, Joseph R.},
  year = {2005},
  volume = {25},
  pages = {262},
  publisher = {{NIH Public Access}}
}

@article{wallinga04,
  title = {Different Epidemic Curves for Severe Acute Respiratory Syndrome Reveal Similar Impacts of Control Measures},
  author = {Wallinga, Jacco and Teunis, Peter},
  year = {2004},
  month = sep,
  journal = {Am J Epidemiol},
  volume = {160},
  number = {6},
  pages = {509--516},
  doi = {10.1093/aje/kwh255},
  pmid = {15353410}
}

@article{wallinga2007,
  title = {How Generation Intervals Shape the Relationship between Growth Rates and Reproductive Numbers},
  author = {Wallinga, J and Lipsitch, M},
  year = {2007},
  month = feb,
  journal = {Proceedings. Biological Sciences / The Royal Society},
  volume = {274},
  number = {1609},
  eprint = {17476782},
  eprinttype = {pubmed},
  pages = {599--604},
  issn = {0962-8452},
  urldate = {2011-07-22},
  abstract = {Mathematical models of transmission have become invaluable management tools in planning for the control of emerging infectious diseases. A key variable in such models is the reproductive number R. For new emerging infectious diseases, the value of the reproductive number can only be inferred indirectly from the observed exponential epidemic growth rate r. Such inference is ambiguous as several different equations exist that relate the reproductive number to the growth rate, and it is unclear which of these equations might apply to a new infection. Here, we show that these different equations differ only with respect to their assumed shape of the generation interval distribution. Therefore, the shape of the generation interval distribution determines which equation is appropriate for inferring the reproductive number from the observed growth rate. We show that by assuming all generation intervals to be equal to the mean, we obtain an upper bound to the range of possible values that the reproductive number may attain for a given growth rate. Furthermore, we show that by taking the generation interval distribution equal to the observed distribution, it is possible to obtain an empirical estimate of the reproductive number.},
  pmid = {17476782},
  keywords = {Cohort Effect,Disease Outbreaks,Humans,{Influenza, Human},{Models, Biological}},
  file = {C\:\\Users\\andre\\Zotero\\storage\\NBD4CBH6\\wallinga07prsb - How generation intervals shape the relationship between growth rates and reproductive numbers .pdf}
}

@article{woelfel2020,
  title = {Clinical Presentation and Virological Assessment of Hospitalized Cases of Coronavirus Disease 2019 in a Travel-Associated Transmission Cluster},
  author = {Woelfel, Roman and Corman, Victor Max and Guggemos, Wolfgang and Seilmaier, Michael and Zange, Sabine and Mueller, Marcel A. and Niemeyer, Daniela and Vollmar, Patrick and Rothe, Camilla and Hoelscher, Michael and Bleicker, Tobias and Bruenink, Sebastian and Schneider, Julia and Ehmann, Rosina and Zwirglmaier, Katrin and Drosten, Christian and Wendtner, Clemens},
  year = {2020},
  month = mar,
  journal = {medRxiv},
  pages = {2020.03.05.20030502},
  publisher = {{Cold Spring Harbor Laboratory Press}},
  doi = {10.1101/2020.03.05.20030502},
  urldate = {2020-03-10},
  abstract = {{$<$}p{$>$}Background: In coronavirus disease 2019 (COVID-19), current case definitions presume mainly lower respiratory tract infection. However, cases seen outside the epicenter of the epidemic may differ in their overall clinical appearance due to more sensitive case finding. Methods: We studied viral load courses by RT-PCR in oro- and nasopharyngeal swabs, sputum, stool, blood, and urine in nine hospitalized cases. Infectious virus was detected by cell culture. Active replication was demonstrated by analysis of viral subgenomic replicative intermediates. Serology including neutralization testing was done to characterize immune response. Results: Seven cases had upper respiratory tract disease. Lower respiratory tract symptoms seen in two cases were limited. Clinical sensitivity of RT-PCR on swabs taken on days 1-5 of symptoms was 100\%, with no differences comparing swab and sputum samples taken simultaneously. Average viral load was 6.76x10E5 copies per swab during the first 5 days. Live virus isolates were obtained from swabs during the first week of illness. Proof of active viral replication in upper respiratory tract tissues was obtained by detection of subgenomic viral RNA. Shedding of viral RNA from sputum outlasted the end of symptoms. Seroconversion occurred after about one week. Conclusions: The present study shows that COVID-19 can often present as a common cold-like illness. SARS-CoV-2 can actively replicate in the upper respiratory tract, and is shed for a prolonged time after symptoms end, including in stool. These findings suggest adjustments of current case definitions and re-evaluation of the prospects of outbreak containment.{$<$}/p{$>$}},
  copyright = {\textcopyright{} 2020, Posted by Cold Spring Harbor Laboratory. The copyright holder for this pre-print is the author. All rights reserved. The material may not be redistributed, re-used or adapted without the author's permission.},
  langid = {english},
  note = {Publisher: Cold Spring Harbor Laboratory Press
\par
Publisher: Cold Spring Harbor Laboratory Press},
  file = {C\:\\Users\\andre\\Zotero\\storage\\J34VLEZL\\2020.03.05.html}
}

@article{yorke78,
  title = {Dynamics and Control of the Transmission of Gonorrhea.},
  author = {Yorke, J. A. and Hethcote, H. W. and Nold, A.},
  year = {1978},
  journal = {Sex Transm Dis},
  volume = {5},
  number = {2},
  pages = {51--56},
  abstract = {Calculations revealed that approximately a third of the reported cases of gonorrhea in women during 1973-1975 were discoveries of the screening program. Theoretical models and epidemiologic data showed that the prevalence of gonorrhea adjusts rapidly to changes in social behavior, medical treatment, and control programs, that prevalence oscillates seasonally around an equilibrium state determined by the current social and medical conditions, and that this equilibrium moves as epidemiologic conditions change. The incidence of gonorrhea is theoretically limited by saturation in a sexually active core population, and this core causes gonorrhea to remain endemic.},
  langid = {english},
  pmid = {10328031},
  keywords = {epidemiology,epidemiology/prevention /\&/ control/transmission,Female,Gonorrhea,Humans,Incidence,Male,Mass Screening,Prevalence,Seasons,Sex Distribution,United States},
  file = {C\:\\Users\\andre\\Zotero\\storage\\Q4AEG6KW\\young19fm - Recent Advances in the Vaccine Development Against Middle East Respiratory Syndrome-Coronavirus.pdf}
}

@article{zhang2020b,
  title = {Changes in Contact Patterns Shape the Dynamics of the {{COVID-19}} Outbreak in {{China}}},
  author = {Zhang, Juanjuan and Litvinova, Maria and Liang, Yuxia and Wang, Yan and Wang, Wei and Zhao, Shanlu and Wu, Qianhui and Merler, Stefano and Viboud, C{\'e}cile and Vespignani, Alessandro and Ajelli, Marco and Yu, Hongjie},
  year = {2020},
  month = jun,
  journal = {Science},
  volume = {368},
  number = {6498},
  pages = {1481--1486},
  publisher = {{American Association for the Advancement of Science}},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.abb8001},
  urldate = {2020-06-26},
  abstract = {Who and what next? The coronavirus 2019 (COVID-19) pandemic has brought tighter restrictions on the daily lives of millions of people, but we do not yet understand what measures are the most effective. Zhang et al. modeled virus transmission in Wuhan, China, in February 2020, investigating the effects of interventions ranging from patient management to social isolation. Age-mixing patterns were estimated by contact surveys conducted in Wuhan and Shanghai at the beginning of February 2020. Once people reduced their average daily contacts from 14 to 20 down to 2, transmission rapidly fell below the epidemic threshold. The model also showed that preemptive school closures helped to reduce transmission, although alone they would not prevent a COVID-19 outbreak. Limiting human mixing to within households appeared to be the most effective measure. Science, this issue p. 1481 Intense nonpharmaceutical interventions were put in place in China to stop transmission of the novel coronavirus disease 2019 (COVID-19). As transmission intensifies in other countries, the interplay between age, contact patterns, social distancing, susceptibility to infection, and COVID-19 dynamics remains unclear. To answer these questions, we analyze contact survey data for Wuhan and Shanghai before and during the outbreak and contact-tracing information from Hunan province. Daily contacts were reduced seven- to eightfold during the COVID-19 social distancing period, with most interactions restricted to the household. We find that children 0 to 14 years of age are less susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection than adults 15 to 64 years of age (odds ratio 0.34, 95\% confidence interval 0.24 to 0.49), whereas individuals more than 65 years of age are more susceptible to infection (odds ratio 1.47, 95\% confidence interval 1.12 to 1.92). Based on these data, we built a transmission model to study the impact of social distancing and school closure on transmission. We find that social distancing alone, as implemented in China during the outbreak, is sufficient to control COVID-19. Although proactive school closures cannot interrupt transmission on their own, they can reduce peak incidence by 40 to 60\% and delay the epidemic. Reducing social contacts on the scale achieved in Wuhan and Shanghai in early 2020 could be sufficient to bring a COVID-19 outbreak under control. Reducing social contacts on the scale achieved in Wuhan and Shanghai in early 2020 could be sufficient to bring a COVID-19 outbreak under control.},
  chapter = {Report},
  copyright = {Copyright \textcopyright{} 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. http://www.sciencemag.org/about/science-licenses-journal-article-reuseThis is an article distributed under the terms of the Science Journals Default License.},
  langid = {english},
  pmid = {32350060},
  file = {C\:\\Users\\andre\\Zotero\\storage\\AKNSDMTP\\1481.html}
}
