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

被引:0
|
作者
Li, Ji [1 ]
Li, Yue [2 ]
Mei, Zihan [1 ]
Liu, Zhengkun [1 ]
Zou, Gaofeng [2 ]
Cao, Chunxia [1 ]
机构
[1] Tianjin Univ, Inst Disaster & Emergency Med, Tianjin, Peoples R China
[2] Tianjin Univ, Coll Management & Econ, Tianjin, Peoples R China
基金
国家重点研发计划;
关键词
unnatural epidemics; mathematical models; risk assessment; machine learning; analysis tools; SYNDROMIC SURVEILLANCE; PUBLIC-HEALTH; OUTBREAK-DETECTION; DIFFERENTIATION METHOD; AIRBORNE PATHOGENS; BIOLOGICAL WARFARE; INFECTIOUS-DISEASE; BIOTERRORISM; SYSTEM; TIME;
D O I
10.3389/fpubh.2024.1381328
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
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.
引用
收藏
页数:13
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