How Local Interactions Impact the Dynamics of an Epidemic View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-11-13

AUTHORS

Lydia Wren, Alex Best

ABSTRACT

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. We combine an approximate mathematical model (using a pair approximation) and stochastic simulations to consider the impact of increasingly local interactions on the epidemic. Our key development is to allow not just extremes of 'local' (neighbour-to-neighbour) or 'global' (random) transmission, but all points in between. We find that even medium degrees of local interactions produce epidemics highly similar to those with entirely global interactions, and only once interactions are predominantly local do epidemics become substantially lower and later. We also show how intervention strategies to impose local interactions on a population must be introduced early if significant impacts are to be seen. More... »

PAGES

124

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11538-021-00961-w

DOI

http://dx.doi.org/10.1007/s11538-021-00961-w

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1142563927

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/34773169


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