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@article{abu-raddad06,
title = {Dual Infection with {{HIV}} and Malaria Fuels the Spread of Both Diseases in Sub-{{Saharan Africa}}.},
author = {{Abu-Raddad}, Laith J and Patnaik, Padmaja and Kublin, James G},
year = {2006},
month = dec,
volume = {314},
pages = {1603--1606},
issn = {1095-9203},
doi = {10.1126/science.1132338},
abstract = {Mounting evidence has revealed pathological interactions between HIV and malaria in dually infected patients, but the public health implications of the interplay have remained unclear. A transient almost one-log elevation in HIV viral load occurs during febrile malaria episodes; in addition, susceptibility to malaria is enhanced in HIV-infected patients. A mathematical model applied to a setting in Kenya with an adult population of roughly 200,000 estimated that, since 1980, the disease interaction may have been responsible for 8,500 excess HIV infections and 980,000 excess malaria episodes. Co-infection might also have facilitated the geographic expansion of malaria in areas where HIV prevalence is high. Hence, transient and repeated increases in HIV viral load resulting from recurrent co-infection with malaria may be an important factor in promoting the spread of HIV in sub-Saharan Africa.},
chemicals = {Antimalarials},
citation-subset = {IM},
completed = {2006-12-19},
created = {2006-12-12},
issn-linking = {0036-8075},
journal = {Science (New York, N.Y.)},
keywords = {Adult,Africa South of the Sahara,Antimalarials,Biological,complications,Disease Susceptibility,drug therapy,Endemic Diseases,epidemiology,Falciparum,Female,HIV Infections,HIV-1,Humans,Kenya,Malaria,Male,Mathematics,Models,physiology,Prevalence,Recurrence,Sexual Behavior,therapeutic use,transmission,Viral Load,Viremia,virology,Virus Replication},
nationality = {United States},
nlm-id = {0404511},
number = {5805},
owner = {NLM},
pii = {314/5805/1603},
pmid = {17158329},
pubmodel = {Print},
pubstatus = {ppublish},
revised = {2007-11-14},
timestamp = {2017.09.08}
}
@article{albert05,
title = {Scale-Free Networks in Cell Biology},
author = {Albert, R.},
year = {2005},
month = nov,
volume = {118},
pages = {4947--4957},
issn = {0021-9533, 1477-9137},
doi = {10.1242/jcs.02714},
journal = {Journal of Cell Science},
language = {English},
number = {21},
owner = {andreas},
timestamp = {2017.08.08}
}
@article{altizer06,
title = {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 = apr,
volume = {9},
pages = {467--484},
issn = {1461-0248},
doi = {10.1111/j.1461-0248.2005.00879.x},
abstract = {Seasonal variations in temperature, rainfall and resource availability are ubiquitous and can exert strong pressures on population dynamics. Infectious diseases provide some of the best-studied examples of the role of seasonality in shaping population fluctuations. In this paper, we review examples from human and wildlife disease systems to illustrate the challenges inherent in understanding the mechanisms and impacts of seasonal environmental drivers. Empirical evidence points to several biologically distinct mechanisms by which seasonality can impact host-pathogen interactions, including seasonal changes in host social behaviour and contact rates, variation in encounters with infective stages in the environment, annual pulses of host births and deaths and changes in host immune defences. Mathematical models and field observations show that the strength and mechanisms of seasonality can alter the spread and persistence of infectious diseases, and that population-level responses can range from simple annual cycles to more complex multiyear fluctuations. From an applied perspective, understanding the timing and causes of seasonality offers important insights into how parasite-host systems operate, how and when parasite control measures should be applied, and how disease risks will respond to anthropogenic climate change and altered patterns of seasonality. Finally, by focusing on well-studied examples of infectious diseases, we hope to highlight general insights that are relevant to other ecological interactions.},
citation-subset = {IM},
completed = {2006-05-23},
created = {2006-04-20},
issn-linking = {1461-023X},
journal = {Ecology letters},
keywords = {Animal Diseases,Animals,Communicable Diseases,Disease Susceptibility,epidemiology,Host-Parasite Interactions,Models,Population Dynamics,Reproduction,Risk Factors,Seasons,Social Behavior,Theoretical,transmission,Wild},
nationality = {England},
nlm-id = {101121949},
number = {4},
owner = {NLM},
pii = {ELE879},
pmid = {16623732},
pubmodel = {Print},
pubstatus = {ppublish},
revised = {2007-11-15},
timestamp = {2017.02.25}
}
@article{altizer13,
title = {Climate Change and Infectious Diseases: {{From}} Evidence to a Predictive Framework.},
author = {Altizer, Sonia and Ostfeld, Richard S and Johnson, Pieter T J and Kutz, Susan and Harvell, C Drew},
year = {2013},
month = aug,
volume = {341},
pages = {514--519},
issn = {1095-9203},
doi = {10.1126/science.1239401},
abstract = {Scientists have long predicted large-scale responses of infectious diseases to climate change, giving rise to a polarizing debate, especially concerning human pathogens for which socioeconomic drivers and control measures can limit the detection of climate-mediated changes. Climate change has already increased the occurrence of diseases in some natural and agricultural systems, but in many cases, outcomes depend on the form of climate change and details of the host-pathogen system. In this review, we highlight research progress and gaps that have emerged during the past decade and develop a predictive framework that integrates knowledge from ecophysiology and community ecology with modeling approaches. Future work must continue to anticipate and monitor pathogen biodiversity and disease trends in natural ecosystems and identify opportunities to mitigate the impacts of climate-driven disease emergence.},
citation-subset = {IM},
completed = {2013-08-16},
created = {2013-08-02},
issn-linking = {0036-8075},
journal = {Science (New York, N.Y.)},
keywords = {Animals,Biodiversity,Biological,Climate Change,Communicable Diseases,epidemiology,Extinction,Health,Host-Parasite Interactions,Host-Pathogen Interactions,Humans,Prognosis,transmission},
nationality = {United States},
nlm-id = {0404511},
number = {6145},
owner = {NLM},
pii = {341/6145/514},
pmid = {23908230},
pubmodel = {Print},
pubstatus = {ppublish},
revised = {2013-12-02},
timestamp = {2017.09.08}
}
@article{anderson79,
title = {Population Biology of Infectious Diseases: {{Part I}}},
author = {Anderson, Roy M and May, Robert M and others},
year = {1979},
volume = {280},
pages = {361--367},
journal = {Nature},
number = {5721},
owner = {andreas},
timestamp = {2017.07.19}
}
@article{anderson90,
title = {Immunisation and Herd Immunity},
author = {Anderson, Roy M and May, Robert M},
year = {1990},
volume = {335},
pages = {641--645},
publisher = {{Elsevier}},
journal = {The Lancet},
number = {8690},
owner = {andreas},
timestamp = {2017.09.08}
}
@article{anderson91,
title = {Infectious Disease of Humans},
author = {Anderson, Roy M and May, RM},
year = {1991},
journal = {Dynamics and control},
owner = {andreas},
timestamp = {2017.03.25}
}
@article{antia03,
title = {The Role of Evolution in the Emergence of Infectious Diseases.},
author = {Antia, Rustom and Regoes, Roland R and Koella, Jacob C and Bergstrom, Carl T},
year = {2003},
month = dec,
volume = {426},
pages = {658--661},
issn = {1476-4687},
doi = {10.1038/nature02104},
abstract = {It is unclear when, where and how novel pathogens such as human immunodeficiency virus (HIV), monkeypox and severe acute respiratory syndrome (SARS) will cross the barriers that separate their natural reservoirs from human populations and ignite the epidemic spread of novel infectious diseases. New pathogens are believed to emerge from animal reservoirs when ecological changes increase the pathogen's opportunities to enter the human population and to generate subsequent human-to-human transmission. Effective human-to-human transmission requires that the pathogen's basic reproductive number, R(0), should exceed one, where R(0) is the average number of secondary infections arising from one infected individual in a completely susceptible population. However, an increase in R(0), even when insufficient to generate an epidemic, nonetheless increases the number of subsequently infected individuals. Here we show that, as a consequence of this, the probability of pathogen evolution to R(0) \textquestiondown{} 1 and subsequent disease emergence can increase markedly.},
citation-subset = {IM},
completed = {2004-01-05},
created = {2003-12-11},
issn-linking = {0028-0836},
journal = {Nature},
keywords = {Animals,Biological,Biological Evolution,Communicable Diseases,Ecosystem,epidemiology,genetics,Host-Parasite Interactions,Humans,Models,Mutation,Probability,Stochastic Processes,transmission},
nationality = {England},
nlm-id = {0410462},
number = {6967},
owner = {NLM},
pii = {nature02104},
pmid = {14668863},
pubmodel = {Print},
pubstatus = {ppublish},
revised = {2010-11-18},
timestamp = {2017.09.08}
}
@article{baggaley05,
title = {The Epidemiological Impact of Antiretroviral Use Predicted by Mathematical Models: {{A}} Review},
shorttitle = {The Epidemiological Impact of Antiretroviral Use Predicted by Mathematical Models},
author = {Baggaley, Rebecca F. and Ferguson, Neil M. and Garnett, Geoff P.},
year = {2005},
volume = {2},
pages = {9},
journal = {Emerging themes in epidemiology},
number = {1},
owner = {andreas},
timestamp = {2017.08.08}
}
@article{bansal07,
title = {When Individual Behaviour Matters: {{Homogeneous}} and Network Models in Epidemiology.},
author = {Bansal, Shweta and Grenfell, Bryan T. and Meyers, Lauren Ancel},
year = {2007},
month = oct,
volume = {4},
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.}},
doi = {10.1098/rsif.2007.1100},
abstract = {Heterogeneity in host contact patterns profoundly shapes population-level disease dynamics. Many epidemiological models make simplifying assumptions about the patterns of disease-causing interactions among hosts. In particular, homogeneous-mixing models assume that all hosts have identical rates of disease-causing contacts. In recent years, several network-based approaches have been developed to explicitly model heterogeneity in host contact patterns. Here, we use a network perspective to quantify the extent to which real populations depart from the homogeneous-mixing assumption, in terms of both the underlying network structure and the resulting epidemiological dynamics. We find that human contact patterns are indeed more heterogeneous than assumed by homogeneous-mixing models, but are not as variable as some have speculated. We then evaluate a variety of methodologies for incorporating contact heterogeneity, including network-based models and several modifications to the simple SIR compartmental model. We conclude that the homogeneous-mixing compartmental model is appropriate when host populations are nearly homogeneous, and can be modified effectively for a few classes of non-homogeneous networks. In general, however, network models are more intuitive and accurate for predicting disease spread through heterogeneous host populations.},
journal = {Journal of The Royal Society Interface},
keywords = {Biological,Disease Transmission,Epidemiologic Methods,Humans,Infectious,Models,Statistical},
language = {English},
medline-pst = {ppublish},
number = {16},
pii = {080KX13732480384},
pmc = {PMC2394553},
pmid = {17640863}
}
@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,
volume = {4},
pages = {478--489},
publisher = {{Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA. [email protected]}},
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.},
journal = {J Biol Dyn},
keywords = {Biological,Communicable Diseases,epidemiology,Humans,Interpersonal Relations,Models,Statistics as Topic},
language = {English},
medline-pst = {ppublish},
number = {5},
pmid = {22877143}
}
@article{bansal12,
title = {The Impact of Past Epidemics on Future Disease Dynamics.},
author = {Bansal, Shweta and Meyers, Lauren Ancel},
year = {2012},
month = sep,
volume = {309},
pages = {176--184},
publisher = {{Center for Infectious Disease Dynamics, The Pennsylvania State University, 208 Mueller Lab, University Park, PA 16802, USA. [email protected]}},
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.},
journal = {Journal of Theoretical Biology},
keywords = {Biological,Disease,Epidemics,Immune Evasion,Immunity,immunology,Models,Seasons},
language = {English},
medline-pst = {ppublish},
pii = {S0022-5193(12)00299-8},
pmid = {22721993}
}
@inproceedings{bartlett56,
title = {Deterministic and Stochastic Models for Recurrent Epidemics},
booktitle = {Proceedings of the Third {{Berkeley}} Symposium on Mathematical Statistics and Probability},
author = {Bartlett, MS},
year = {1956},
volume = {4},
pages = {109},
owner = {andreas},
timestamp = {2017.09.08}
}
@article{bartlett57,
title = {Measles Periodicity and Community Size},
author = {Bartlett, Maurice S},
year = {1957},
volume = {120},
pages = {48--70},
publisher = {{JSTOR}},
journal = {Journal of the Royal Statistical Society. Series A (General)},
number = {1},
owner = {andreas},
timestamp = {2017.09.08}
}
@article{bartlett60,
title = {The {{Critical Community Size}} for {{Measles}} in the {{United States}}},
author = {Bartlett, M. S.},
year = {1960},
volume = {123},
pages = {37},
issn = {00359238},
doi = {10.2307/2343186},
journal = {Journal of the Royal Statistical Society. Series A (General)},
number = {1},
owner = {andreas},
timestamp = {2017.08.08}
}
@article{barton14,
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 = {2014},
month = may,
volume = {83},
pages = {566--575},
publisher = {{Odum School of Ecology, University of Georgia, Athens, GA, 30602, USA.}},
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 63.0\%. Simpson's Index in the same period varied more than fourfold from 0.22 to 0.93. 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.},
journal = {Journal of Animal Ecology},
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},
language = {English},
medline-pst = {ppublish},
number = {3},
pmc = {PMC4000580},
pmid = {24164627}
}
@article{basu13,
title = {Complexity in {{Mathematical Models}} of {{Public Health Policies}}: {{A Guide}} for {{Consumers}} of {{Models}}},
shorttitle = {Complexity in {{Mathematical Models}} of {{Public Health Policies}}},
author = {Basu, Sanjay and Andrews, Jason},
year = {2013},
month = oct,
volume = {10},
pages = {e1001540},
issn = {1549-1676},
doi = {10.1371/journal.pmed.1001540},
journal = {PLoS Medicine},
language = {English},
number = {10},
owner = {andreas},
timestamp = {2017.08.08}
}
@article{baumgartner12,
title = {Seasonality, {{Timing}}, and {{Climate Drivers}} of {{Influenza Activity Worldwide}}},
author = {Baumgartner, Eduardo and Dao, Christine N. and Nasreen, Sharifa and Bhuiyan, Mejbah Uddin and {Mah-E-Muneer}, Syeda and Mamun, Abdullah Al and Sharker, M. A. Yushuf and Zaman, Rashid Uz and Cheng, Po-Yung and Klimov, Alexander I. and Widdowson, Marc-Alain and Uyeki, Timothy M. and Luby, Stephen P. and Mounts, Anthony and Bresee, Joseph},
year = {2012},
month = sep,
volume = {206},
pages = {838--846},
issn = {1537-6613, 0022-1899},
doi = {10.1093/infdis/jis467},
journal = {The Journal of Infectious Diseases},
language = {English},
number = {6},
owner = {andreas},
timestamp = {2017.08.08}
}
@article{baym16,
title = {Multidrug Evolutionary Strategies to Reverse Antibiotic Resistance},
author = {Baym, M. and Stone, L. K. and Kishony, R.},
year = {2016},
month = jan,
volume = {351},
pages = {aad3292--aad3292},
issn = {0036-8075, 1095-9203},
doi = {10.1126/science.aad3292},
journal = {Science},
language = {English},
number = {6268},
owner = {andreas},
timestamp = {2017.08.08}
}
@article{begon02,
title = {A Clarification of Transmission Terms in Host-Microparasite Models: {{Numbers}}, Densities and Areas.},
author = {Begon, M. and Bennett, M. and Bowers, R. G. and French, N. P. and Hazel, S. M. and Turner, J.},
year = {2002},
month = aug,
volume = {129},
pages = {147--153},
publisher = {{ences, The University of Liverpool, UK.}},
abstract = {Transmission is the driving force in the dynamics of any infectious disease. A crucial element in understanding disease dynamics, therefore, is the 'transmission term' describing the rate at which susceptible hosts are 'converted' into infected hosts by their contact with infectious material. Recently, the conventional form of this term has been increasingly questioned, and new terminologies and conventions have been proposed. Here, therefore, we review the derivation of transmission terms, explain the basis of confusion, and provide clarification. The root of the problem has been a failure to include explicit consideration of the area occupied by a host population, alongside both the number of infectious hosts and their density within the population. We argue that the terms 'density-dependent transmission' and 'frequency-dependent transmission' remain valid and useful (though a 'fuller' transmission term for the former is identified), but that the terms 'mass action', 'true mass action' and 'pseudo mass action' are all unhelpful and should be dropped. Also, contrary to what has often been assumed, the distinction between homogeneous and heterogeneous mixing in a host population is orthogonal to the distinction between density- and frequency-dependent transmission modes.},
journal = {Epidemiology and Infection},
keywords = {Biological,Disease Transmission,Humans,Infectious,Models},
language = {English},
medline-pst = {ppublish},
number = {1},
owner = {andreas},
pmid = {12211582},
timestamp = {2016.09.01}
}
@article{beldomenico10,
title = {Disease Spread, Susceptibility and Infection Intensity: {{Vicious}} Circles?},
author = {Beldomenico, Pablo M and Begon, Michael},
year = {2010},
month = jan,
volume = {25},
pages = {21--27},
issn = {0169-5347},
doi = {10.1016/j.tree.2009.06.015},
abstract = {Epidemiological models and studies of disease ecology typically ignore the role of host condition and immunocompetence when trying to explain the distribution and dynamics of infections and their impact on host dynamics. Recent research, however, indicates that host susceptibility should be considered carefully if we are to understand the mechanism by which parasite dynamics influence host dynamics and vice versa. Studies in insects, fish, amphibians and rodents show that infection occurrence and intensity are more probable and more severe in individuals with an underlying poor condition. Moreover, infection itself results in further deterioration of the host and a 'vicious circle' is created. We argue that this potential synergy between host susceptibility and infection should be more widely acknowledged in disease ecology research.},
citation-subset = {IM},
completed = {2010-03-18},
created = {2010-02-01},
issn-linking = {0169-5347},
journal = {Trends in ecology \& evolution},
keywords = {Animals,Disease Susceptibility,Disease Transmission,Host-Pathogen Interactions,Immunocompetence,Infectious},
nationality = {England},
nlm-id = {8805125},
number = {1},
owner = {NLM},
pii = {S0169-5347(09)00228-6},
pmid = {19782425},
pubmodel = {Print-Electronic},
pubstatus = {ppublish},
revised = {2011-05-03},
timestamp = {2017.02.20}
}
@article{bellan12,
title = {How to Make Epidemiological Training Infectious.},
author = {Bellan, Steve E and Pulliam, Juliet R C and Scott, James C and Dushoff, Jonathan and Committee, MMED Organizing},
year = {2012},
volume = {10},
pages = {e1001295},
issn = {1545-7885},
doi = {10.1371/journal.pbio.1001295},
abstract = {Modern infectious disease epidemiology builds on two independently developed fields: classical epidemiology and dynamical epidemiology. Over the past decade, integration of the two fields has increased in research practice, but training options within the fields remain distinct with few opportunities for integration in the classroom. The annual Clinic on the Meaningful Modeling of Epidemiological Data (MMED) at the African Institute for Mathematical Sciences has begun to address this gap. MMED offers participants exposure to a broad range of concepts and techniques from both epidemiological traditions. During MMED 2010 we developed a pedagogical approach that bridges the traditional distinction between classical and dynamical epidemiology and can be used at multiple educational levels, from high school to graduate level courses. The approach is hands-on, consisting of a real-time simulation of a stochastic outbreak in course participants, including realistic data reporting, followed by a variety of mathematical and statistical analyses, stemming from both epidemiological traditions. During the exercise, dynamical epidemiologists developed empirical skills such as study design and learned concepts of bias while classical epidemiologists were trained in systems thinking and began to understand epidemics as dynamic nonlinear processes. We believe this type of integrated educational tool will prove extremely valuable in the training of future infectious disease epidemiologists. We also believe that such interdisciplinary training will be critical for local capacity building in analytical epidemiology as Africa continues to produce new cohorts of well-trained mathematicians, statisticians, and scientists. And because the lessons draw on skills and concepts from many fields in biology\textendash from pathogen biology, evolutionary dynamics of host\textendash pathogen interactions, and the ecology of infectious disease to bioinformatics, computational biology, and statistics\textendash this exercise can be incorporated into a broad array of life sciences courses.},
citation-subset = {IM},
completed = {2012-08-07},
created = {2012-04-17},
investigator = {Bellan, Steve E and Pulliam, Juliet R C and Scott, James C and Dushoff, Jonathan and Porco, Travis C and Williams, Brian G and Hargrove, John W and Welte, Alex and Delva, Wim and Hitchcock, Gavin and Adams, Bibi and Blows, Linsay and Fanucchi, Dario and Irunde, Jacob Ismail and Nezar Gennrich, Jessica and Jones, Piet and Maluta, Eric and Marutla, Geoffrey and Mazinu, Cynthia and Mudimu, Edinah and Nakakawa, Juliet and Nelufule, Nthatheni Norman and Ngwenya, Olina and Pascal, Dany and Reddy, Tarylee and Sekgobela, Wilcan and Serumula, Valrie Mabu and Seuneu, Milaine and Shabangu, Patrick and Van Rensburg, Marinel Janse and Wilson, Ben},
issn-linking = {1544-9173},
journal = {PLoS biology},
keywords = {Biological,Data Interpretation,education,Epidemics,Epidemiologic Factors,Epidemiology,Humans,Models,Problem-Based Learning,Statistical,statistics \& numerical data},
nationality = {United States},
nlm = {PMC3317897},
nlm-id = {101183755},
number = {4},
owner = {NLM},
pii = {PBIOLOGY-D-11-04141},
pmc = {PMC3317897},
pmid = {22509129},
pubmodel = {Print-Electronic},
pubstatus = {ppublish},
revised = {2017-02-24},
timestamp = {2017.07.19}
}
@article{bellan15,
title = {Statistical Power and Validity of {{Ebola}} Vaccine Trials in {{Sierra Leone}}: {{A}} Simulation Study of Trial Design and Analysis.},
author = {Bellan, Steven E and Pulliam, Juliet R C and Pearson, Carl A B and Champredon, David and Fox, Spencer J and Skrip, Laura and Galvani, Alison P and Gambhir, Manoj and Lopman, Ben A and Porco, Travis C and Meyers, Lauren Ancel and Dushoff, Jonathan},
year = {2015},
month = jun,
volume = {15},
pages = {703--710},
issn = {1474-4457},
doi = {10.1016/S1473-3099(15)70139-8},
abstract = {Safe and effective vaccines could help to end the ongoing Ebola virus disease epidemic in parts of west Africa, and mitigate future outbreaks of the virus. We assess the statistical validity and power of randomised controlled trial (RCT) and stepped-wedge cluster trial (SWCT) designs in Sierra Leone, where the incidence of Ebola virus disease is spatiotemporally heterogeneous, and is decreasing rapidly. We projected district-level Ebola virus disease incidence for the next 6 months, using a stochastic model fitted to data from Sierra Leone. We then simulated RCT and SWCT designs in trial populations comprising geographically distinct clusters at high risk, taking into account realistic logistical constraints, and both individual-level and cluster-level variations in risk. We assessed false-positive rates and power for parametric and non-parametric analyses of simulated trial data, across a range of vaccine efficacies and trial start dates. For an SWCT, regional variation in Ebola virus disease incidence trends produced increased false-positive rates (up to 0{$\cdot$}15 at {$\alpha$}=0{$\cdot$}05) under standard statistical models, but not when analysed by a permutation test, whereas analyses of RCTs remained statistically valid under all models. With the assumption of a 6-month trial starting on Feb 18, 2015, we estimate the power to detect a 90\% effective vaccine to be between 49\% and 89\% for an RCT, and between 6\% and 26\% for an SWCT, depending on the Ebola virus disease incidence within the trial population. We estimate that a 1-month delay in trial initiation will reduce the power of the RCT by 20\% and that of the SWCT by 49\%. Spatiotemporal variation in infection risk undermines the statistical power of the SWCT. This variation also undercuts the SWCT's expected ethical advantages over the RCT, because an RCT, but not an SWCT, can prioritise vaccination of high-risk clusters. US National Institutes of Health, US National Science Foundation, and Canadian Institutes of Health Research.},
chemicals = {Ebola Vaccines},
citation-subset = {IM},
completed = {2015-08-03},
created = {2015-05-26},
issn-linking = {1473-3099},
journal = {The Lancet. Infectious diseases},
keywords = {administration \& dosage,Biomedical Research,Biostatistics,Ebola,Ebola Vaccines,Hemorrhagic Fever,Humans,immunology,methods,prevention \& control,Randomized Controlled Trials as Topic,Sierra Leone},
mid = {NIHMS754200},
nationality = {United States},
nlm = {PMC4815262},
nlm-id = {101130150},
number = {6},
owner = {NLM},
pii = {S1473-3099(15)70139-8},
pmc = {PMC4815262},
pmid = {25886798},
pubmodel = {Print-Electronic},
pubstatus = {ppublish},
revised = {2017-02-20},
timestamp = {2017.07.19}
}
@article{birger15,
title = {The Potential Impact of Coinfection on Antimicrobial Chemotherapy and Drug Resistance.},
author = {Birger, Ruthie B and Kouyos, Roger D and Cohen, Ted and Griffiths, Emily C and Huijben, Silvie and Mina, Michael J and Volkova, Victoriya and Grenfell, Bryan and Metcalf, C Jessica E},
year = {2015},
month = sep,
volume = {23},
pages = {537--544},
issn = {1878-4380},
doi = {10.1016/j.tim.2015.05.002},
abstract = {Across a range of pathogens, resistance to chemotherapy is a growing problem in both public health and animal health. Despite the ubiquity of coinfection, and its potential effects on within-host biology, the role played by coinfecting pathogens on the evolution of resistance and efficacy of antimicrobial chemotherapy is rarely considered. In this review, we provide an overview of the mechanisms of interaction of coinfecting pathogens, ranging from immune modulation and resource modulation, to drug interactions. We discuss their potential implications for the evolution of resistance, providing evidence in the rare cases where it is available. Overall, our review indicates that the impact of coinfection has the potential to be considerable, suggesting that this should be taken into account when designing antimicrobial drug treatments.},
chemicals = {Immunologic Factors},
citation-subset = {IM},
completed = {2016-06-28},
created = {2015-09-05},
issn-linking = {0966-842X},
journal = {Trends in microbiology},
keywords = {Animals,Biological,coinfection,Coinfection,drug resistance,Drug Resistance,drug therapy,genetics,Host-Pathogen Interactions,Humans,immune modulation,Immunologic Factors,immunology,Microbial,Microbial Interactions,microbiology,Models,parasite interactions,parasitology,physiology,resource competition},
mid = {NIHMS773026},
nationality = {England},
nlm = {PMC4835347},
nlm-id = {9310916},
number = {9},
owner = {NLM},
pmc = {PMC4835347},
pmid = {26028590},
pubmodel = {Print-Electronic},
pubstatus = {ppublish},
revised = {2017-07-18},
timestamp = {2017.09.08}
}
@book{bjornstad18,
title = {Epidemics: {{Models}} and {{Data}} Using {{R}}},
shorttitle = {Epidemics},
author = {Bj{\o}rnstad, Ottar N.},
year = {2018},
publisher = {{Springer International Publishing}},
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},
language = {English},
series = {Use {{R}}!}
}
@article{black12,
title = {Stochastic Formulation of Ecological Models and Their Applications},
author = {Black, Andrew J. and McKane, Alan J.},
year = {2012},
month = jun,
volume = {27},
pages = {337--345},
issn = {01695347},
doi = {10.1016/j.tree.2012.01.014},
journal = {Trends in Ecology \& Evolution},
language = {English},
number = {6},
owner = {andreas},
timestamp = {2017.08.08}
}
@article{black66,
title = {Measles Endemicity in Insular Populations: {{Critical}} Community Size and Its Evolutionary Implication.},
author = {Black, F L},
year = {1966},
month = jul,
volume = {11},
pages = {207--211},
issn = {0022-5193},
citation-subset = {IM},
completed = {1967-07-26},
created = {1967-07-26},
issn-linking = {0022-5193},
journal = {Journal of theoretical biology},
keywords = {Atlantic Islands,Bermuda,Biological Evolution,epidemiology,Hawaii,Humans,Iceland,Measles,Pacific Islands,Population Surveillance},
nationality = {England},
nlm-id = {0376342},
number = {2},
owner = {NLM},
pii = {0022-5193(66)90161-5},
pmid = {5965486},
pubmodel = {Print},
pubstatus = {ppublish},
revised = {2010-11-18},
timestamp = {2017.02.20}
}
@article{blower04,
title = {An Attempt at a New Analysis of the Mortality Caused by Smallpox and of the Advantages of Inoculation to Prevent It. 1766.},
author = {Blower, Sally and Bernoulli, Daniel},
year = {2004},
volume = {14},
pages = {275--288},
issn = {1052-9276},
chemicals = {Smallpox Vaccine},
citation-subset = {IM},
completed = {2004-12-03},
created = {2004-8-30},
issn-linking = {1052-9276},
journal = {Reviews in medical virology},
journal-abbreviation = {Rev Med Virol},
keywords = {16th Century,17th Century,18th Century,19th Century,20th Century,administration \& dosage,Biological,history,History,Humans,Models,mortality,prevention \& control,Smallpox,Smallpox Vaccine,Vaccination},
nationality = {England},
nlm-id = {9112448},
number = {5},
owner = {NLM},
pmid = {15334536},
pubmodel = {Print},
pubstatus = {ppublish},
revised = {2004-8-30},
status = {MEDLINE},
timestamp = {2016.10.19}
}
@article{boerglum03,
title = {Possible Parent-of-Origin Effect of {{Dopa}} Decarboxylase in Susceptibility to Bipolar Affective Disorder.},
author = {B{\o}rglum, A D and Kirov, G and Craddock, N and Mors, O and Muir, W and Murray, V and McKee, I and Collier, D A and Ewald, H and Owen, M J and Blackwood, D and Kruse, T A},
year = {2003},
month = feb,
volume = {117B},
pages = {18--22},
issn = {1552-4841},
doi = {10.1002/ajmg.b.10030},
abstract = {Dopa decarboxylase (DDC) catalyses the synthesis of both dopamine and serotonin as well as trace amines suggested to possess neuromodulating capabilities. We have previously reported evidence suggesting an association between DDC and bipolar affective disorder (BPAD) [B\o rglum et al., 1999]. To further investigate the possible role of DDC in BPAD, we analyzed a 1- and a 4-bp deletion variant-both of putative functional significance-in two new samples: a case-control sample with 140 cases and 204 controls, and 100 case-parents trios. We also tested for association in subjects with familial disease in both the new and the previously investigated samples. The previously reported association was not replicated in either of the new samples. However, a preponderance of the 1-bp deletion was increased by analysis of the familial cases separately for all case-control samples investigated, indicating a possible association with familial disease (combined analysis, P = 0.02). In the trio sample, a preferential paternal transmission of the 4-bp deletion was observed (P = 0.006). DDC is located next to the imprinted gene GRB10, which is expressed specifically from the paternal allele in fetal brains. Increased transmission of paternal DDC alleles has also been suggested in attention deficit hyperactivity disorder. We suggest that DDC might confer susceptibility to BPAD predominantly when paternally transmitted.},
chemicals = {Dopa Decarboxylase},
citation-subset = {IM},
completed = {2003-11-06},
created = {2003-01-29},
issn-linking = {1552-4841},
journal = {American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics},
keywords = {Bipolar Disorder,Case-Control Studies,Dopa Decarboxylase,enzymology,Family Health,Frameshift Mutation,Gene Frequency,Genetic Predisposition to Disease,genetics,Genomic Imprinting,Genotype,Humans,Inheritance Patterns,Scotland,Sequence Deletion,Wales},
nationality = {United States},
nlm-id = {101235742},
number = {1},
owner = {NLM},
pmid = {12555230},
pubmodel = {Print},
pubstatus = {ppublish},
revised = {2008-05-21},
timestamp = {2017.08.14}
}
@article{brankston07,
title = {Transmission of Influenza {{A}} in Human Beings},
author = {Brankston, Gabrielle and Gitterman, Leah and Hirji, Zahir and Lemieux, Camille and Gardam, Michael},
year = {2007},
volume = {7},
pages = {257--265},
journal = {The Lancet infectious diseases},
number = {4},
owner = {andreas},
timestamp = {2017.08.08}
}
@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,
volume = {5},
pages = {e1000346},
publisher = {{Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America. [email protected]}},
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.},
journal = {PLoS computational biology},
keywords = {Animals,Biological,Birds,Disease Outbreaks,Environmental Exposure,Influenza A virus,Influenza in Birds,Models,pathogenicity/physiology,transmission/virology,veterinary},
language = {English},
medline-pst = {ppublish},
number = {4},
pmc = {PMC2660440},
pmid = {19360126}
}
@article{breban10,
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,
volume = {264},
pages = {729--736},
publisher = {{Odum School of Ecology, University of Georgia, Athens, GA 30602, USA. [email protected]}},
doi = {10.1016/j.jtbi.2010.03.005},
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.},
journal = {Journal of Theoretical Biology},
keywords = {Algorithms,Animals,Communicable Diseases,Disease Reservoirs,Endemic Diseases,epidemiology/transmission,Humans,Models,Population Dynamics,prevention /\&/ control,Theoretical},
language = {English},
medline-pst = {ppublish},
number = {3},
pii = {S0022-5193(10)00128-1},
pmid = {20211630}
}
@article{breiman01,
title = {Statistical Modeling: {{The}} Two Cultures (with Comments and a Rejoinder by the Author)},
author = {Breiman, Leo and others},
year = {2001},
volume = {16},
pages = {199--231},
publisher = {{Institute of Mathematical Statistics}},
journal = {Statistical science},
number = {3}
}
@article{brett17,
title = {Anticipating the Emergence of Infectious Diseases},
author = {Brett, Tobias S and Drake, John M and Rohani, Pejman},
year = {2017},
volume = {14},
pages = {20170115},
publisher = {{The Royal Society}},
journal = {Journal of The Royal Society Interface},
number = {132},
owner = {andreas},
timestamp = {2017.09.13}
}
@article{britton10,
title = {Stochastic Epidemic Models: {{A}} Survey},
shorttitle = {Stochastic Epidemic Models},
author = {Britton, Tom},
year = {2010},
month = may,
volume = {225},
pages = {24--35},
issn = {00255564},
doi = {10.1016/j.mbs.2010.01.006},
journal = {Mathematical Biosciences},
language = {English},
number = {1},
owner = {andreas},
timestamp = {2017.08.08}
}
@article{brown10,
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,
volume = {46},
pages = {896--911},
publisher = {{Department of Population Health, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA. jubrown1mailto:@uga.edu}},
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.},
journal = {Journal of Wildlife Diseases},
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},
language = {English},
medline-pst = {ppublish},
number = {3},
pii = {46/3/896},
pmid = {20688695}
}
@article{brown12,
title = {The Consequences of Climate Change at an Avian Influenza 'Hotspot'.},
author = {Brown, V. L. and Rohani, Pejman},
year = {2012},
month = dec,
volume = {8},
pages = {1036--1039},
publisher = {{Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA. [email protected]}},
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.},
journal = {Biology letters},
keywords = {Animals,Biological,Charadriiformes,Climate Change,Delaware,epidemiology,epidemiology/transmission,Horseshoe Crabs,Influenza in Birds,Models,physiology,physiology/virology,Population Dynamics,Prevalence,Seasons},
language = {English},
medline-pst = {ppublish},
number = {6},
pii = {rsbl.2012.0635},
pmc = {PMC3497130},
pmid = {22933039}
}
@article{brown13,
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,
volume = {10},
pages = {20120804},
publisher = {{Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA. [email protected]}},
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.},
journal = {Journal of The Royal Society Interface},
keywords = {Animal,Animal Migration,Animals,Biological,Bird Diseases,Charadriiformes,Delaware,Ducks,epidemiology,epidemiology/transmission,epidemiology/transmission/virology,immunology/virology,Influenza in Birds,Models,physiology,Prevalence,Seasons,Sexual Behavior,Species Specificity,Wild},
language = {English},
medline-pst = {ppublish},
number = {79},
pii = {rsif.2012.0804},
pmc = {PMC3565696},
pmid = {23173198}
}
@article{brown14,
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},
volume = {9},
pages = {e88817},
publisher = {{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.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)\textendash 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.},
journal = {PLoS One},
keywords = {Animals,Birds,Delaware,immunology,immunology/virology,Influenza A virus,Influenza in Birds,Species Specificity},
language = {English},
medline-pst = {epublish},
number = {2},
pii = {PONE-D-12-39950},
pmc = {PMC3934864},
pmid = {24586401}
}
@article{cao07,
title = {Adaptive Explicit-Implicit Tau-Leaping Method with Automatic Tau Selection},
author = {Cao, Yang and Gillespie, Daniel T. and Petzold, Linda R.},
year = {2007},
month = jun,
volume = {126},
pages = {224101},
issn = {0021-9606, 1089-7690},
doi = {10.1063/1.2745299},
journal = {The Journal of Chemical Physics},
language = {English},
number = {22},
owner = {andreas},
timestamp = {2017.08.08}
}
@manual{chang17,
title = {Shiny: {{Web}} Application Framework for r},
author = {Chang, Winston and Cheng, Joe and Allaire, JJ and Xie, Yihui and McPherson, Jonathan},
year = {2017},
owner = {andreas},
timestamp = {2017.07.19},
type = {Manual}
}
@article{chen14,
title = {{{HCV}} and {{HIV}} Co-Infection: {{Mechanisms}} and Management},
author = {Chen, Jennifer Y and Feeney, Eoin R and Chung, Raymond T},
year = {2014},
volume = {11},
pages = {362--371},
publisher = {{Nature Research}},
journal = {Nature reviews Gastroenterology \& hepatology},
number = {6}
}
@article{christakis13,
title = {Social Contagion Theory: {{Examining}} Dynamic Social Networks and Human Behavior},
shorttitle = {Social Contagion Theory},
author = {Christakis, Nicholas A. and Fowler, James H.},
year = {2013},
month = feb,
volume = {32},
pages = {556--577},
issn = {02776715},
doi = {10.1002/sim.5408},
journal = {Statistics in Medicine},
language = {English},
number = {4},
owner = {andreas},
timestamp = {2017.08.08}
}
@article{christie69,
title = {Infectious Diseases: {{Epidemiology}} and Clinical Practice.},
author = {Christie, Andrew Barnett and others},
year = {1969},
publisher = {{E. \& S. Livingstone Ltd.}},
journal = {Infectious diseases: epidemiology and clinical practice.},
owner = {andreas},
timestamp = {2017.07.19}
}
@article{chubb10,
title = {Mathematical Modeling and the Epidemiological Research Process},
author = {Chubb, Mikayla C. and Jacobsen, Kathryn H.},
year = {2010},
month = jan,
volume = {25},
pages = {13--19},
issn = {0393-2990, 1573-7284},
doi = {10.1007/s10654-009-9397-9},
journal = {European Journal of Epidemiology},
language = {English},
number = {1},
owner = {andreas},
timestamp = {2017.08.08}
}
@article{cochran00,
title = {Infectious Causation of Disease: {{An}} Evolutionary Perspective.},
author = {Cochran, G M and Ewald, P W and Cochran, K D},
year = {2000},
volume = {43},
pages = {406--448},
issn = {0031-5982},
citation-subset = {IM},
completed = {2000-11-03},
created = {2000-11-03},
issn-linking = {0031-5982},
journal = {Perspectives in biology and medicine},
keywords = {16th Century,19th Century,20th Century,Alleles,Biological Evolution,Causality,complications,Disease,Environment,etiology,Genetic,Genetic Predisposition to Disease,Genetics,history,History,Humans,Infection,Mutagenesis,Population,Selection,transmission},
nationality = {United States},
nlm-id = {0401132},
number = {3},
owner = {NLM},
pmid = {10893730},
pubmodel = {Print},
pubstatus = {ppublish},
revised = {2010-11-18},
season = {Spring},
timestamp = {2017.08.14}
}
@article{codeco01,
title = {Endemic and Epidemic Dynamics of Cholera: {{The}} Role of the Aquatic Reservoir.},
author = {Code{\c c}o, C T},
year = {2001},
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},
journal = {BMC infectious diseases},
keywords = {Biological,Brazil,Cholera,Disease Outbreaks,Endemic Diseases,epidemiology,Humans,Models,Prevalence,Theoretical,Water Pollution,Water Supply},
nationality = {England},
nlm = {PMC29087},
nlm-id = {100968551},
owner = {NLM},
pmc = {PMC29087},
pmid = {11208258},
pubmodel = {Print-Electronic},
pubstatus = {ppublish},
revised = {2014-06-13},
timestamp = {2017.02.20}
}
@article{contagionmovie,
title = {Contagion},
year = {2011},
howpublished = {Warner Bros},
owner = {andreas},
timestamp = {2017.12.17}
}
@article{cooper02,
title = {Timing of Transmission and the Evolution of Virulence of an Insect Virus.},
author = {Cooper, Vaughn S and Reiskind, Michael H and Miller, Jonathan A and Shelton, Kirsten A and Walther, Bruno A and Elkinton, Joseph S and Ewald, Paul W},
year = {2002},
month = jun,
volume = {269},
pages = {1161--1165},
issn = {0962-8452},
doi = {10.1098/rspb.2002.1976},
abstract = {We used the nuclear polyhedrosis virus of the gypsy moth, Lymantria dispar, to investigate whether the timing of transmission influences the evolution of virulence. In theory, early transmission should favour rapid replication and increase virulence, while late transmission should favour slower replication and reduce virulence. We tested this prediction by subjecting one set of 10 virus lineages to early transmission (Early viruses) and another set to late transmission (Late viruses). Each lineage of virus underwent nine cycles of transmission. Virulence assays on these lineages indicated that viruses transmitted early were significantly more lethal than those transmitted late. Increased exploitation of the host appears to come at a cost, however. While Early viruses initially produced more progeny, Late viruses were ultimately more productive over the entire duration of the infection. These results illustrate fitness trade-offs associated with the evolution of virulence and indicate that milder viruses can obtain a numerical advantage when mild and harmful strains tend to infect separate hosts.},
citation-subset = {IM},
completed = {2002-12-27},
created = {2002-06-13},
issn-linking = {0962-8452},
journal = {Proceedings. Biological sciences},
keywords = {Analysis of Variance,Animals,Biological Evolution,growth \& development,Host-Parasite Interactions,Moths,Nucleopolyhedrovirus,pathogenicity,physiology,Time Factors,transmission,virology,Virulence,Virus Diseases,Virus Replication},
nationality = {England},
nlm = {PMC1691001},
nlm-id = {101245157},
number = {1496},
owner = {NLM},
pmc = {PMC1691001},
pmid = {12061960},
pubmodel = {Print},
pubstatus = {ppublish},
revised = {2017-02-19},
timestamp = {2017.08.14}
}
@article{cortez13,
title = {Distinguishing between {{Indirect}} and {{Direct Modes}} of {{Transmission Using Epidemiological Time Series}}},
author = {Cortez, Michael H. and Weitz, Joshua S.},
year = {2013},
month = feb,
volume = {181},
pages = {E43--E52},
issn = {0003-0147, 1537-5323},
doi = {10.1086/668826},
journal = {The American Naturalist},
language = {English},
number = {2},
owner = {andreas},
timestamp = {2017.08.08}
}
@article{davis12,
title = {Influenza and Community-Acquired Pneumonia Interactions: {{The}} Impact of Order and Time of Infection on Population Patterns.},
author = {Davis, Brian M and Aiello, Allison E and Dawid, Suzanne and Rohani, Pejman and Shrestha, Sourya and Foxman, Betsy},
year = {2012},
month = mar,
volume = {175},
pages = {363--367},
issn = {1476-6256},
doi = {10.1093/aje/kwr402},
abstract = {Discoveries made during the 1918 influenza A pandemic and reports of severe disease associated with coinfection during the 2009 hemagglutinin type 1 and neuraminidase type 1 (commonly known as H1N1 or swine flu) pandemic have renewed interest in the role of coinfection in disease pathogenesis. The authors assessed how various timings of coinfection with influenza virus and pneumonia-causing bacteria could affect the severity of illness at multiple levels of interaction, including the biologic and population levels. Animal studies most strongly support a single pathway of coinfection with influenza inoculation occurring approximately 7 days before inoculation with Streptococcus pneumoniae, but less-examined pathways of infection also may be important for human disease. The authors discussed the implications of each pathway for disease prevention and what they would expect to see at the population level if there were sufficient data available. Lastly, the authors identified crucial gaps in the study of timing of coinfection and proposed related research questions.},
citation-subset = {IM},
completed = {2012-05-22},
created = {2012-02-21},
issn-linking = {0002-9262},
journal = {American journal of epidemiology},
keywords = {Coinfection,Community-Acquired Infections,complications,Human,Humans,Influenza,Influenza A virus,microbiology,pathogenicity,Pneumococcal,Pneumonia,Streptococcus pneumoniae,Time Factors,transmission},
nationality = {United States},
nlm-id = {7910653},
number = {5},
owner = {NLM},
pii = {kwr402},
pmid = {22247048},
pubmodel = {Print-Electronic},
pubstatus = {ppublish},
revised = {2012-02-21},
timestamp = {2017.09.08}
}
@article{day01,
title = {Parasite Transmission Modes and the Evolution of Virulence.},
author = {Day, T},
year = {2001},
month = dec,
volume = {55},
pages = {2389--2400},
issn = {0014-3820},
abstract = {A mathematical model is presented that explores the relationship between transmission patterns and the evolution of virulence for horizontally transmitted parasites when only a single parasite strain can infect each host. The model is constructed by decomposing parasite transmission into two processes, the rate of contact between hosts and the probability of transmission per contact. These transmission rate components, as well as the total parasite mortality rate, are allowed to vary over the course of an infection. A general evolutionarily stable condition is presented that partitions the effects of virulence on parasite fitness into three components: fecundity benefits, mortality costs, and morbidity costs. This extension of previous theory allows us to explore the evolutionary consequences of a variety of transmission patterns. I then focus attention on a special case in which the parasite density remains approximately constant during an infection, and I demonstrate two important ways in which transmission modes can affect virulence evolution: by imposing different morbidity costs on the parasite and by altering the scheduling of parasite reproduction during an infection. Both are illustrated with examples, including one that examines the hypothesis that vector-borne parasites should be more virulent than non-vector-borne parasites (Ewald 1994). The validity of this hypothesis depends upon the way in which these two effects interact, and it need not hold in general.},
citation-subset = {IM},
completed = {2002-07-24},
created = {2002-02-07},
issn-linking = {0014-3820},
journal = {Evolution; international journal of organic evolution},
keywords = {Animals,Biological Evolution,Humans,Models,Parasites,Parasitic Diseases,pathogenicity,Theoretical,transmission,Virulence},
nationality = {United States},
nlm-id = {0373224},
number = {12},
owner = {NLM},
pmid = {11831655},
pubmodel = {Print},
pubstatus = {ppublish},
revised = {2010-11-18},
timestamp = {2017.08.14}
}
@article{day02,
title = {The Evolution of Virulence in Vector-Borne and Directly Transmitted Parasites.},
author = {Day, Troy},
year = {2002},
month = sep,
volume = {62},