Prediction modeling with data fusion and prevention strategy analysis for the COVID-19 outbreak / 中华流行病学杂志

ABSTRACT

Since December 2019, the outbreak of COVID-19 in Wuhan has spread rapidly due to population movement during the Spring Festival holidays. Since January 23rd, 2020, the strategies of containment and contact tracing followed by quarantine and isolation has been implemented extensively in mainland China, and the rates of detection and confirmation have been continuously increased, which have effectively suppressed the rapid spread of the epidemic. In the early stage of the outbreak of COVID-19, it is of great practical significance to analyze the transmission risk of the epidemic and evaluate the effectiveness and timeliness of prevention and control strategies by using mathematical models and combining with a small amount of real-time updated multi-source data. On the basis of our previous research, we systematically introduce how to establish the transmission dynamic models in line with current Chinese prevention and control strategies step by step, according to the different epidemic stages and the improvement of the data. By summarized our modelling and assessing ideas, the model formulations vary from autonomous to non-autonomous dynamic systems, the risk assessment index changes from the basic regeneration number to the effective regeneration number, and the epidemic development and assessment evolve from the early SEIHR transmission model-based dynamics to the recent dynamics which are mainly associated with the variation of the isolated and suspected population sizes.