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一种基于大数据和FRAM的传染病流行风险分析模型。

A Big Data and FRAM-Based Model for Epidemic Risk Analysis of Infectious Diseases.

作者信息

Zhu Junhua, Zhuang Yue, Li Wenjing

机构信息

School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, People's Republic of China.

出版信息

Risk Manag Healthc Policy. 2024 Aug 29;17:2067-2081. doi: 10.2147/RMHP.S476794. eCollection 2024.

DOI:10.2147/RMHP.S476794
PMID:39224172
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11368406/
Abstract

PURPOSE

The use of multi-source precursor data to predict the epidemic risk level would aid in the early and timely identification of the epidemic risk of infectious diseases. To achieve this, a new comprehensive big data fusion assessment method must be developed.

METHODS

With the help of the Functional Resonance Analysis Method (FRAM) model, this paper proposes a risk portrait for the whole process of a pandemic spreading. Using medical, human behaviour, internet and geo-meteorological data, a hierarchical multi-source dataset was developed with three function module tags, ie, Basic Risk Factors (BRF), the Spread of Epidemic Threats (SET) and Risk Influencing Factors (RIF).

RESULTS

Using the dynamic functional network diagram of the risk assessment functional module, the FRAM portrait was applied to pandemic case analysis in Wuhan in 2020. This new-format FRAM portrait model offers a potential early and rapid risk assessment method that could be applied in future acute public health events.

摘要

目的

利用多源前驱数据预测疫情风险水平，将有助于早期及时识别传染病的疫情风险。要实现这一点，必须开发一种新的综合大数据融合评估方法。

方法

借助功能共振分析方法（FRAM）模型，本文提出了大流行传播全过程的风险画像。利用医学、人类行为、互联网和地理气象数据，开发了一个具有三个功能模块标签的分层多源数据集，即基本风险因素（BRF）、疫情威胁传播（SET）和风险影响因素（RIF）。

结果

利用风险评估功能模块的动态功能网络图，将FRAM画像应用于2020年武汉的大流行病例分析。这种新形式的FRAM画像模型提供了一种潜在的早期快速风险评估方法，可应用于未来的急性公共卫生事件。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6c/11368406/301a6656a5d8/RMHP-17-2067-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6c/11368406/4f82e145f8b4/RMHP-17-2067-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6c/11368406/566214c53341/RMHP-17-2067-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6c/11368406/e03a846e6df8/RMHP-17-2067-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6c/11368406/5725f9a49717/RMHP-17-2067-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6c/11368406/e9bc905a319e/RMHP-17-2067-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6c/11368406/23a1358a7642/RMHP-17-2067-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6c/11368406/45713ff5b31c/RMHP-17-2067-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6c/11368406/0731bcd56a14/RMHP-17-2067-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6c/11368406/301a6656a5d8/RMHP-17-2067-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6c/11368406/4f82e145f8b4/RMHP-17-2067-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6c/11368406/566214c53341/RMHP-17-2067-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6c/11368406/e03a846e6df8/RMHP-17-2067-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6c/11368406/5725f9a49717/RMHP-17-2067-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6c/11368406/e9bc905a319e/RMHP-17-2067-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6c/11368406/23a1358a7642/RMHP-17-2067-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6c/11368406/45713ff5b31c/RMHP-17-2067-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6c/11368406/0731bcd56a14/RMHP-17-2067-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd6c/11368406/301a6656a5d8/RMHP-17-2067-g0009.jpg

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Trust in the scientific research community predicts intent to comply with COVID-19 prevention measures: An analysis of a large-scale international survey dataset.
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Epidemiol Infect. 2022 Feb 8;150:e36. doi: 10.1017/S0950268822000255.
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