Recent approaches in computational modelling for controlling pathogen threats.

Autor:	Lees JA; https://ror.org/02catss52 European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK., Russell TW; https://ror.org/00a0jsq62 Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK., Shaw LP; Department of Biology, University of Oxford, Oxford, UK.; Department of Biosciences, University of Durham, Durham, UK., Hellewell J; https://ror.org/02catss52 European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK joel@ebi.ac.uk.
Jazyk:	angličtina
Zdroj:	Life science alliance [Life Sci Alliance] 2024 Jun 21; Vol. 7 (9). Date of Electronic Publication: 2024 Jun 21 (Print Publication: 2024).
DOI:	10.26508/lsa.202402666
Abstrakt:	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. (© 2024 Lees et al.)
Databáze:	MEDLINE
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