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

IF 2.7 4区医学 Q2 HEALTH CARE SCIENCES & SERVICES

Risk Management and Healthcare Policy Pub Date : 2024-08-29 eCollection Date: 2024-01-01 DOI:10.2147/RMHP.S476794

Junhua Zhu, Yue Zhuang, Wenjing Li

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引用次数: 0

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.

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基于大数据和 FRAM 的传染病流行风险分析模型。

目的：利用多源前兆数据预测疫情风险水平有助于及早、及时地识别传染病的疫情风险。为此，必须开发一种新的综合性大数据融合评估方法：本文借助功能共振分析法（FRAM）模型，提出了疫情传播全过程的风险画像。方法：本文借助功能共振分析法（FRAM）模型，提出了大流行病传播全过程的风险画像。利用医疗、人类行为、互联网和地理气象数据，开发了一个分层多源数据集，其中包含三个功能模块标签，即基本风险因素（BRF）、流行病威胁传播（SET）和风险影响因素（RIF）：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 年武汉大流行病例分析。这种新形式的 FRAM 肖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中。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Risk Management and Healthcare Policy Medicine-Public Health, Environmental and Occupational Health

CiteScore

6.20

自引率

2.90%

发文量

242

审稿时长

16 weeks

期刊介绍： Risk Management and Healthcare Policy is an international, peer-reviewed, open access journal focusing on all aspects of public health, policy and preventative measures to promote good health and improve morbidity and mortality in the population. Specific topics covered in the journal include: Public and community health Policy and law Preventative and predictive healthcare Risk and hazard management Epidemiology, detection and screening Lifestyle and diet modification Vaccination and disease transmission/modification programs Health and safety and occupational health Healthcare services provision Health literacy and education Advertising and promotion of health issues Health economic evaluations and resource management Risk Management and Healthcare Policy focuses on human interventional and observational research. The journal welcomes submitted papers covering original research, clinical and epidemiological studies, reviews and evaluations, guidelines, expert opinion and commentary, and extended reports. Case reports will only be considered if they make a valuable and original contribution to the literature. The journal does not accept study protocols, animal-based or cell line-based studies.