Systematic selection between age and household structure for models aimed at emerging epidemic predictions

Lorenzo Pellis (), Simon Cauchemez, Neil M. Ferguson and Christophe Fraser
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Lorenzo Pellis: University of Manchester
Simon Cauchemez: Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS
Neil M. Ferguson: MRC Centre for Global Infectious Disease Analysis, J-IDEA, School of Public Health, Imperial College
Christophe Fraser: Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: Abstract Numerous epidemic models have been developed to capture aspects of human contact patterns, making model selection challenging when they fit (often-scarce) early epidemic data equally well but differ in predictions. Here we consider the invasion of a novel directly transmissible infection and perform an extensive, systematic and transparent comparison of models with explicit age and/or household structure, to determine the accuracy loss in predictions in the absence of interventions when ignoring either or both social components. We conclude that, with heterogeneous and assortative contact patterns relevant to respiratory infections, the model’s age stratification is crucial for accurate predictions. Conversely, the household structure is only needed if transmission is highly concentrated in households, as suggested by an empirical but robust rule of thumb based on household secondary attack rate. This work serves as a template to guide the simplicity/accuracy trade-off in designing models aimed at initial, rapid assessment of potential epidemic severity.

Date: 2020
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DOI: 10.1038/s41467-019-14229-4

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