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Recent approaches in computational modelling for controlling pathogen threats.

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

Authors

John A Lees

Timothy W Russell

Joel Hellewell



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.

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

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