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Susceptible-Infected-Recovered (SIR) models have long formed the basis for exploring epidemiological dynamics in a range of contexts, including infectious disease spread in human populations. Classic SIR models take a mean-field assumption, such that a susceptible individual has an equal chance of catching the disease from any infected individual in the population. In reality, spatial and social structure will drive most instances of disease transmission. Here we explore the impacts of including spatial structure in a simple SIR model. In particular we assume individuals live on a square lattice and that contacts can be \'local\' (neighour-to-neighbour) or \'global\' or a mix of the two. We combine an approximate mathematical model (using a pair approximation) and stochastic simulations to consider the impact of increasingly local interactions on the epidemic. We find that there is a strongly non-linear response, with small degrees of local interaction having little impact, but epidemics with susbtantially lower and later epidemics once interactions are predominantly local. We also show how intervention strategies to impose local interactions on a population must be introduced early if significant impacts are to be seen.
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?:doi
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10.1101/2020.11.24.20237651
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document_parses/pdf_json/7cbce7693122d60f53aaca54a4de94e76f514dd7.json
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How local interactions impact the dynamics of an epidemic
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