Uncertainty and error in SARS-CoV-2 epidemiological parameters inferred from population-level epidemic models

Whittaker, Dominic G.; Herrera-Reyes, Alejandra D.; Hendrix, Maurice; Owen, Markus R.; Band, Leah R.; Mirams, Gary R.; Bolton, Kirsty J.; Preston, Simon P.

Abstract

During the SARS-CoV2 pandemic, epidemic models have been central to policy-making. Public health responses have been shaped by model-based projections and inferences, especially related to the impact of various non-pharmaceutical interventions. Accompanying this has been increased scrutiny over model performance , model assumptions, and the way that uncertainty is incorporated and presented. Here we consider a population-level model, focusing on how distributions representing host infectiousness and the infection-to-death times are modelled, and particularly on the impact of inferred epidemic characteristics if these distributions are misspecified. We introduce an SIR-type model with the infected population structured by 'infected age', i.e. the number of days since first being infected, a formulation that enables distributions to be incorporated that are consistent with clinical data. We show that inference based on simpler models without infected age, which implicitly misspecify these distributions, leads to substantial errors in inferred quantities relevant to policy-making, such as the reproduction number and the impact of interventions. We consider uncertainty quantification via a Bayesian approach, implementing this for both synthetic and real data focusing on UK data in the period 15 Feb-14 Jul 2020, and emphasising circumstances where it is misleading to neglect uncertainty. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Citation

Whittaker, D. G., Herrera-Reyes, A. D., Hendrix, M., Owen, M. R., Band, L. R., Mirams, G. R., …Preston, S. P. (2023). Uncertainty and error in SARS-CoV-2 epidemiological parameters inferred from population-level epidemic models. Journal of Theoretical Biology, 558, Article 111337. https://doi.org/10.1016/j.jtbi.2022.111337

• Journal Article Type: Article
• Acceptance Date: Oct 27, 2022
• Online Publication Date: Nov 6, 2022
• Publication Date: Feb 7, 2023
• Deposit Date: Nov 3, 2022
• Publicly Available Date: Nov 7, 2023
• Journal: Journal of Theoretical Biology
• Print ISSN: 0022-5193
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 558
• Article Number: 111337
• DOI: https://doi.org/10.1016/j.jtbi.2022.111337
• Keywords: Applied Mathematics; General Agricultural and Biological Sciences; General Immunology and Microbiology; General Biochemistry, Genetics and Molecular Biology; Modeling and Simulation; General Medicine; Statistics and Probability
• Public URL: https://nottingham-repository.worktribe.com/output/13175740
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0022519322003289?via%3Dihub