Dynamic survival analysis for non-Markovian epidemic models.

Autor:	Di Lauro F; Big Data Institute, University of Oxford, Oxford, OX3 7LF, UK., KhudaBukhsh WR; Department of Mathematics, University of Nottingham, Nottingham, NG7 2RD, UK., Kiss IZ; Department of Mathematics, University of Sussex, Brighton, BN1 9RH, UK., Kenah E; Department of Biostatistics, The Ohio State University, Columbus, OH 43210, USA., Jensen M; Department of Mathematics, University of Sussex, Brighton, BN1 9RH, UK., Rempała GA; Department of Biostatistics, The Ohio State University, Columbus, OH 43210, USA.
Jazyk:	angličtina
Zdroj:	Journal of the Royal Society, Interface [J R Soc Interface] 2022 Jun; Vol. 19 (191), pp. 20220124. Date of Electronic Publication: 2022 Jun 01.
DOI:	10.1098/rsif.2022.0124
Abstrakt:	We present a new method for analysing stochastic epidemic models under minimal assumptions. The method, dubbed dynamic survival analysis (DSA), is based on a simple yet powerful observation, namely that population-level mean-field trajectories described by a system of partial differential equations may also approximate individual-level times of infection and recovery. This idea gives rise to a certain non-Markovian agent-based model and provides an agent-level likelihood function for a random sample of infection and/or recovery times. Extensive numerical analyses on both synthetic and real epidemic data from foot-and-mouth disease in the UK (2001) and COVID-19 in India (2020) show good accuracy and confirm the method's versatility in likelihood-based parameter estimation. The accompanying software package gives prospective users a practical tool for modelling, analysing and interpreting epidemic data with the help of the DSA approach.
Databáze:	MEDLINE
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