Disease spreading modeling and analysis: a survey

Abstract Motivation The control of the diffusion of diseases is a critical subject of a broad research area, which involves both clinical and political aspects. It makes wide use of computational tools, such as ordinary differential equations, stochastic simulation frameworks and graph theory, and i...

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Bibliographic Details
Published inBriefings in bioinformatics Vol. 23; no. 4
Main Authors Hiram Guzzi, Pietro, Petrizzelli, Francesco, Mazza, Tommaso
Format Journal Article
LanguageEnglish
Published England Oxford University Press 18.07.2022
Oxford Publishing Limited (England)
Subjects
Computer applications
Coronaviruses
COVID-19
Differential equations
Disease control
Epidemic models
Epidemiology
Graph theory
Pandemics
Software
Viral diseases
Disease Modeling
Graph Theory
Epidemiology
Network Models
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Summary:Abstract Motivation The control of the diffusion of diseases is a critical subject of a broad research area, which involves both clinical and political aspects. It makes wide use of computational tools, such as ordinary differential equations, stochastic simulation frameworks and graph theory, and interaction data, from molecular to social granularity levels, to model the ways diseases arise and spread. The coronavirus disease 2019 (COVID-19) is a perfect testbench example to show how these models may help avoid severe lockdown by suggesting, for instance, the best strategies of vaccine prioritization. Results Here, we focus on and discuss some graph-based epidemiological models and show how their use may significantly improve the disease spreading control. We offer some examples related to the recent COVID-19 pandemic and discuss how to generalize them to other diseases.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbac230