AUTHOR=Li Ji , Li Yue , Mei Zihan , Liu Zhengkun , Zou Gaofeng , Cao Chunxia 

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

JOURNAL=Frontiers in Public Health

VOLUME=Volume 12 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2024.1381328

DOI=10.3389/fpubh.2024.1381328

ISSN=2296-2565

ABSTRACT=Prediction, early warning, and risk assessment for unnatural epidemics (UEs) is a challenge, as well as the focus of attention on the prevention and control research of UEs. A scoping review was conducted through databases, including PubMed, Web of Science, Scopus, and Embase, from inception to December 31, 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 are identified. The validation part of models involves 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 the selection of model parameters and warning thresholds. Future research should consider combining a mechanistic-based model with a data-driven model and learning in the pursuit of time-varying, high-precision risk assessment capabilities.