A Big Data and FRAM-Based Model for Epidemic Risk Analysis of Infectious Diseases.
Risk Manag Healthc Policy
; 17: 2067-2081, 2024.
Article
in En
| MEDLINE
| ID: mdl-39224172
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Risk Manag Healthc Policy
Year:
2024
Document type:
Article
Country of publication:
United kingdom