Evidence map›Paper›PMID 38799686›Full record

ArticleFrontiers in public health2024

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

Abstract readScoping Review
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ji Li *Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China.
Yue Li *College of Management and Economics, Tianjin University, Tianjin, China.
Zihan MeiInstitute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China.
Zhengkun LiuInstitute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China.
Gaofeng ZouCollege of Management and Economics, Tianjin University, Tianjin, China.
Chunxia CaoInstitute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

EpidemicsModels, TheoreticalHumansRisk Assessmentanalysis toolsmachine learningmathematical modelsrisk assessmentunnatural epidemics

Identifiers

PMID38799686
PMCPMC11122901

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.