Recent approaches in computational modelling for controlling pathogen threats.

Lees, John A; Russell, Timothy W; Shaw, Liam P; Hellewell, Joel

Abstract

In this review, we assess the status of computational modelling of pathogens. We focus on three disparate but interlinked research areas that produce models with very different spatial and temporal scope. First, we examine antimicrobial resistance (AMR). Many mechanisms of AMR are not well understood. As a result, it is hard to measure the current incidence of AMR, predict the future incidence, and design strategies to preserve existing antibiotic effectiveness. Next, we look at how to choose the finite number of bacterial strains that can be included in a vaccine. To do this, we need to understand what happens to vaccine and non-vaccine strains after vaccination programmes. Finally, we look at within-host modelling of antibody dynamics. The SARS-CoV-2 pandemic produced huge amounts of antibody data, prompting improvements in this area of modelling. We finish by discussing the challenges that persist in understanding these complex biological systems.

Citation

Lees, J. A., Russell, T. W., Shaw, L. P., & Hellewell, J. (2024). Recent approaches in computational modelling for controlling pathogen threats. Life Science Alliance, 7(9), Article e202402666. https://doi.org/10.26508/lsa.202402666

• Journal Article Type: Article
• Acceptance Date: Jun 13, 2024
• Online Publication Date: Jun 21, 2024
• Publication Date: Sep 1, 2024
• Deposit Date: Jul 10, 2024
• Publicly Available Date: Jul 10, 2024
• Journal: Life Science Alliance
• Print ISSN: 2575-1077
• Electronic ISSN: 2575-1077
• Publisher: Cold Spring Harbor Laboratory Press
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Issue: 9
• Article Number: e202402666
• DOI: https://doi.org/10.26508/lsa.202402666
• Keywords: Humans, Anti-Bacterial Agents, Drug Resistance, Microbial, Computer Simulation, Pandemics, COVID-19, SARS-CoV-2
• Public URL: https://durham-repository.worktribe.com/output/2520540
• PMID: 38906676