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

Ji Li, Yue Li, Zihan Mei, Zhengkun Liu, Gaofeng Zou, Chunxia Cao
Author Information
  1. Ji Li: Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China.
  2. Yue Li: College of Management and Economics, Tianjin University, Tianjin, China.
  3. Zihan Mei: Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China.
  4. Zhengkun Liu: Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China.
  5. Gaofeng Zou: College of Management and Economics, Tianjin University, Tianjin, China.
  6. Chunxia Cao: Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China.

Abstract

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.

Keywords

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MeSH Term

Humans
Epidemics
Models, Theoretical
Risk Assessment

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