Host-pathogen time series data in wildlife support a transmission function between density and frequency dependence

• Published in: Proceedings of the National Academy of Sciences - PNAS 2009-05, Vol.106 (19), p.7905-7909
• Main Authors: Smith, Matthew J, Telfer, Sandra, Kallio, Eva R, Burthe, Sarah, Cook, Alex R, Lambin, Xavier, Begon, Michael
• Format: Article
• Language: English
• Subjects: Animal Diseases - transmission; Animals; Arvicolinae; Biological Sciences; Cowpox - transmission; Cowpox - virology; Cowpox virus; Data transmission; Disease Outbreaks; Disease transmission; Epidemiology; Genetic Predisposition to Disease; Host-Pathogen Interactions; Infections; Mathematical models; Modeling; Models, Theoretical; Monte Carlo Method; Population Density; Population Dynamics; Population size; Seasons; Species Specificity; Time series; Voles
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.0809145106

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.