Dynamic and classifier-based model SARS-CoV-2 Omicron spillover risk assessment in China
2025
Wei, Hongjie | Zhao, Yunkang | Qu, Huimin | Wang, Jing | Abudurusuli, Guzainuer | Chen, Qiuping | Zhao, Zeyu | Song, Wentao | Wang, Yao | Frutos, Roger | Chen, Tianmu
The coronavirus disease 2019 (COVID-19) continues to have a huge impact on health care and economic systems around the world. The first question to ponder is to understand the flow of COVID-19 in the spatial and temporal dimensions. We collected 7 Omicron clusters outbreaks in China since the outbreak of COVID-19 as of August 2022, selected outbreak cases from different provinces and cities, and collected variable indicators that affect spillover outcomes, such as distance, migration index, PHSM index, daily reported cases number and so on. First, variables influencing spillover outcome events were assessed and analyzed retrospectively by constructing an infectious disease dynamics model and a classifier model, and secondly, the association between explanatory variables and spillover outcome events was constructed by fitting a logistics function. This study incorporates 7 influencing factors and classifies the spillover risk level into 3 levels. If different outbreak sites could be classified into different levels of spillover, it may reduce the pressure of epidemic prevention in some districts due to the lack of a uniform standard, which might be more conducive to achieving the goal of "dynamic zero".
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