Machine learning forecasts for seasonal epidemic peaks: Lessons learnt from an atypical respiratory syncytial virus season.

Morbey, Roger A, Todkill, Daniel, Watson, Conall and Elliot, Alex J ORCID: 0000-0002-6414-3065 (2023) Machine learning forecasts for seasonal epidemic peaks: Lessons learnt from an atypical respiratory syncytial virus season. PloS one, 18 (9). e0291932-e0291932.

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Official URL: http://dx.doi.org/10.1371/journal.pone.0291932

Abstract

Seasonal peaks in infectious disease incidence put pressures on health services. Therefore, early warning of the timing and magnitude of peak activity during seasonal epidemics can provide information for public health practitioners to take appropriate action. Whilst many infectious diseases have predictable seasonality, newly emerging diseases and the impact of public health interventions can result in unprecedented seasonal activity. We propose a Machine Learning process for generating short-term forecasts, where models are selected based on their ability to correctly forecast peaks in activity, and can be useful during atypical seasons. We have validated our forecasts using typical and atypical seasonal activity, using respiratory syncytial virus (RSV) activity during 2019-2021 as an example. During the winter of 2020/21 the usual winter peak in RSV activity in England did not occur but was 'deferred' until the Spring of 2021. We compare a range of Machine Learning regression models, with alternate models including different independent variables, e.g. with or without seasonality or trend variables. We show that the best-fitting model which minimises daily forecast errors is not the best model for forecasting peaks when the selection criterion is based on peak timing and magnitude. Furthermore, we show that best-fitting models for typical seasons contain different variables to those for atypical seasons. Specifically, including seasonality in models improves performance during typical seasons but worsens it for the atypical seasons.

Item Type:	Article
Uncontrolled Keywords:	Respiratory Syncytial Virus, Human, Seasons, England, Epidemics, Machine Learning
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Population Health
Depositing User:	Symplectic Admin
Date Deposited:	01 Feb 2024 09:38
Last Modified:	01 Feb 2024 09:38
DOI:	10.1371/journal.pone.0291932
Open Access URL:	https://doi.org/10.1371/journal.pone.0291932
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3178236