Natural factors and non-pharmaceutical interventions (NPIs) have effect
on COVID-19 transmission, but it’s difficult to separate these two
factors. The Compound natural factor (CNF) model is proposed to deal
with this problem. In this model, the weight of single natural factors
(SNFs) could be expressed the coupling relationship (CR) among them.
Then, CR is iteratively optimized by Elitism-based compact genetic
algorithms (ECGAs). Considering optimal coupling relationship of SNFs,
CNF has a strong correlation (r=0.56) with the COVID-19 infection rate.
However, CNF does not have much correlation with mortality (r=-0.25) and
recovery rate (r=-0.46), due to slight change of weather in hospitals.
Therefore, a linear weighted CNF model is constructed to forecast the
impending infection rate. As a result, NPIs effect have been eliminated
in the predicted infection rate by the CNF model, which is only the
result of climate change. If China ignored NPIs, COVID-19 virus would
transmit in the CNF forecast way as climate changes. This model built on
Chinese cases provides a new perspective to forecast the global
infection rate which is only under the intervention of natural factors.