Mathematical models and analysis tools for risk assessment of unnatural epidemics: a scoping review

Predicting, issuing early warnings, and assessing risks associated with unnatural epidemics (UEs) present significant challenges. These tasks also represent key areas of focus within the field of prevention and control research for UEs. A scoping review was conducted using databases such as PubMed,...

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Bibliographic Details
Published in:Frontiers in public health 2024-05, Vol.12, p.1381328-1381328
Main Authors: Li, Ji, Li, Yue, Mei, Zihan, Liu, Zhengkun, Zou, Gaofeng, Cao, Chunxia
Format: Article
Language:English
Subjects:
analysis tools
Epidemics
Humans
machine learning
mathematical models
Models, Theoretical
risk assessment
Risk Assessment - methods
unnatural epidemics
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Summary:Predicting, issuing early warnings, and assessing risks associated with unnatural epidemics (UEs) present significant challenges. These tasks also represent key areas of focus within the field of prevention and control research for UEs. A scoping review was conducted using databases such as PubMed, Web of Science, Scopus, and Embase, from inception to 31 December 2023. Sixty-six studies met the inclusion criteria. Two types of models (data-driven and mechanistic-based models) and a class of analysis tools for risk assessment of UEs were identified. The validation part of models involved calibration, improvement, and comparison. Three surveillance systems (event-based, indicator-based, and hybrid) were reported for monitoring UEs. In the current study, mathematical models and analysis tools suggest a distinction between natural epidemics and UEs in selecting model parameters and warning thresholds. Future research should consider combining a mechanistic-based model with a data-driven model and learning to pursue time-varying, high-precision risk assessment capabilities.
ISSN:2296-2565
2296-2565
DOI:10.3389/fpubh.2024.1381328