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

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.