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

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials 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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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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