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

Year: 2020

Type: article

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.

Source: Nature Communications

DOI: 10.1038/s41467-019-14229-4

Authors: Lorenzo Pellis, Simon Cauchemez, Neil M. Ferguson, Christophe Fraser

Institutions: University of Manchester, University of Warwick, Imperial College London, Centre National de la Recherche Scientifique, Institut Pasteur, Génomique évolutive, modélisation et santé, University of Oxford

Language: English

Topic: Mathematical and Theoretical Epidemiology and Ecology Models

Subfield: Public Health, Environmental and Occupational Health

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $5,380

Funders: Bill and Melinda Gates Foundation, Wellcome Trust, National Institute for Health Research Health Protection Research Unit, National Institute for Health and Care Research, Royal Society, Research Councils UK, University of Warwick, Agence Nationale de la Recherche, AXA Research Fund, Medical Research Council, Engineering and Physical Sciences Research Council, National Institute of General Medical Sciences

OpenAlex ID: W3005809335