Abstract

Models of disease transmission in a population with changing densities must assume a relation between infectious contacts and density. Typically, a choice is made between a constant (frequency-dependence) and a linear (density-dependence) contact–density function, but it is becoming increasingly clear that intermediate, nonlinear functions are more realistic. It is currently not clear, however, what the exact consequences would be of different contact–density functions in fluctuating populations. By combining field data on rodent host (Mastomys natalensis) demography, experimentally derived contact–density data, and laboratory and field data on Morogoro virus infection dynamics, we explored the effects of different contact–density function shapes on transmission dynamics and invasion/persistence. While invasion and persistence were clearly affected by the shape of the function, the effects on outbreak characteristics such as infection prevalence and seroprevalence were less obvious. This means that it may be difficult to distinguish between the different shapes based on how well models fit to real data. The shape of the transmission–density function should therefore be chosen with care, and is ideally based on existing information such as a previously quantified contact– or transmission–density relationship or the underlying biology of the host species in relation to the infectious agent.

Highlights

  • Models of disease transmission in a population with changing densities must assume a relation between infectious contacts and density

  • By combining field data on rodent host (Mastomys natalensis) demography, experimentally derived contact– density data, and laboratory and field data on Morogoro virus infection dynamics, we explored the effects of different contact–density function shapes on transmission dynamics and invasion/persistence

  • The simulated infection dynamics in this study are based on those of Morogoro virus (MORV), which naturally occurs in M. natalensis in Tanzania, and of which the transmission ecology [35] and patterns of infectivity [27,36] have been documented in detail; it has a latent period of about 3 days between infection and excretion, an infectious period of 30–40 days, and presumably lifelong immunity

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Summary

Introduction

Models of disease transmission in a population with changing densities must assume a relation between infectious contacts and density. A choice is made between a constant (frequency-dependence) and a linear (density-dependence) contact–density function, but it is becoming increasingly clear that intermediate, nonlinear functions are more realistic It is currently not clear, what the exact consequences would be of different contact–density functions in fluctuating populations. In its simplest form, R0 = βN, where N is the population size and β is the transmission coefficient that consists of the rate p of becoming infected through contact with an infectious individual, multiplied by the contact–density function that equals cN/A (where A is area) when linear (density-dependent) and c when constant (frequency-dependent), and random homogeneous mixing is assumed [10]. The main approach in these studies has been to measure disease prevalence in a field or experimental setting in which densities are manipulated or vary naturally, followed by fitting models with different transmission–density functions to the data

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