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Effective population size in simple infectious disease models

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Almost all models used in analysis of infectious disease outbreaks contain some notion of population size, usually taken as the census population size of the community in question. In many settings, however, the census population is not equivalent to the population likely to be exposed, for example if there are population structures, outbreak controls or other heterogeneities. Although these factors may be taken into account in the model: adding compartments to a compartmental model, variable mixing rates and so on, this makes fitting more challenging, especially if the population complexities are not fully known. In this work we consider the concept of effective population size in outbreak modelling, which we define as the size of the population involved in an outbreak, as an alternative to use of more complex models. Effective population size is an important quantity in genetics for estimation of genetic diversity loss in populations, but it has not been widely applied in epidemiology. Through simulation studies and application to data from outbreaks of COVID-19 in China, we find that simple SIR models with effective population size can provide a good fit to data which are not themselves simple or SIR.

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Data and relevant code for this research work are available on GitHub at (Yerlanov 2021).


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This work was supported by the Federal Government of Canada’s Canada 150 Research Chair program.

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Correspondence to Jessica E. Stockdale.

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Online Resource 1 contains information and results concerning outbreak simulation and COVID-19 data selection criteria. (pdf 1,838KB)

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Yerlanov, M., Agarwal, P., Colijn, C. et al. Effective population size in simple infectious disease models. J. Math. Biol. 87, 80 (2023).

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