ArticleProceedings of the National Academy of Sciences of the United States of America2009
Host-pathogen time series data in wildlife support a transmission function between density and frequency dependence.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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Who cites it
58 citing papers in PubMed.
- When hosts gather: how extreme seasonal aggregation affects epidemiological outcomes.Infectious Disease Modelling · 2026Article
- Article
- Estimating epidemiological parameters of highly pathogenic avian influenza in common terns using exact Bayesian inference.The Journal of animal ecology · 2025Article
- Maintenance of Genetic Diversity Despite Population Fluctuations in the Lesser Prairie-Chicken (Ecology and evolution · 2025Article
- Efficient modelling of infectious diseases in wildlife: A case study of bovine tuberculosis in wild badgers.PLoS computational biology · 2024Article
- African swine fever in wild boar: investigating model assumptions and structure.Royal Society open science · 2024Article
- Raccoon rabies control and elimination in the northeastern U.S. and southern Québec, Canada.Epidemiology and infection · 2023Article
- Reservoir population ecology, viral evolution and the risk of emerging infectious disease.Proceedings. Biological sciences · 2022Article
- Frequency-dependent transmission of Batrachochytrium salamandrivorans in eastern newts.Transboundary and emerging diseases · 2022Article
- Within and between classroom transmission patterns of seasonal influenza among primary school students in Matsumoto city, Japan.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Article
- Personality affects dynamics of an experimental pathogen in little brown bats.Royal Society open science · 2020Article
- Article
- Stochasticity and Infectious Disease Dynamics: Density and Weather Effects on a Fungal Insect Pathogen.The American naturalist · 2020Article
- Article
- Host density drives viral, but not trypanosome, transmission in a key pollinator.Proceedings. Biological sciences · 2020Article
- Seroprevalence of Foot and Mouth Disease Virus Infection in Some Wildlife and Cattle in Bauchi State, Nigeria.Veterinary medicine international · 2020Article
- Host circadian rhythms are disrupted during malaria infection in parasite genotype-specific manners.Scientific reports · 2019Article
- The ecological dynamics of hantavirus diseases: From environmental variability to disease prevention largely based on data from China.PLoS neglected tropical diseases · 2019Review
- Evaluation of rodent control to fight Lassa fever based on field data and mathematical modelling.Emerging microbes & infections · 2019Article
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Authors and funding
7 authors.
Funding
Abstract
A key aim in epidemiology is to understand how pathogens spread within their host populations. Central to this is an elucidation of a pathogen's transmission dynamics. Mathematical models have generally assumed that either contact rate between hosts is linearly related to host density (density-dependent) or that contact rate is independent of density (frequency-dependent), but attempts to confirm either these or alternative transmission functions have been rare. Here, we fit infection equations to 6 years of data on cowpox virus infection (a zoonotic pathogen) for 4 natural populations to investigate which of these transmission functions is best supported by the data. We utilize a simple reformulation of the traditional transmission equations that greatly aids the estimation of the relationship between density and host contact rate. Our results provide support for an infection rate that is a saturating function of host density. Moreover, we find strong support for seasonality in both the transmission coefficient and the relationship between host contact rate and host density, probably reflecting seasonal variations in social behavior and/or host susceptibility to infection. We find, too, that the identification of an appropriate loss term is a key component in inferring the transmission mechanism. Our study illustrates how time series data of the host-pathogen dynamics, especially of the number of susceptible individuals, can greatly facilitate the fitting of mechanistic disease models.
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