Mathematical models and analysis tools for risk assessment of unnatural epidemics
A scoping review
Bibliographic Data
| ID | 22089786 |
|---|---|
| Authors | Ji Li (0000-0002-6720-0125, Tianjin University), Yue Li (0009-0004-9365-9070, Tianjin University), Zihan Mei (0000-0002-8552-0034, Tianjin University), Zhengkun Liu (0000-0002-6481-835X, Tianjin University), Gaofeng Zou (0000-0002-5823-7786, Tianjin University, corresponding author), Chunxia Cao (0000-0003-4857-425X, Tianjin University, corresponding author) |
| Year | 2024 |
| Volume | 12 |
| Pages | 1381328-1381328 |
| Publication date | 2024-05-02 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2024.1381328 |
| PMID | 38799686 |
| OpenAlex | W4396599250 |
| Language | EN |
| References cited | 77 |
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
Computer security · Data science · Machine learning · Management science · MEDLINE · Predictive modelling · Risk assessment · Scopus · Warning system · Computer Science · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Engineering · Medicine · Viral Infections and Outbreaks Research
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| Citation velocity | historical |
|---|---|
| Highly cited | No |