Abstract
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. 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). 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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