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Predicting COVID-19 epidemiological trend by applying population mobility data in two-stage modeling
Yuanqing Ye1, Hao Lei1, Chen Chen1
1Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases，Zhejiang University，Hangzhou 310003，China.
Summary
Mathematical modeling accurately predicted the COVID-19 outbreak size in Yueqing by integrating population mobility and epidemic data. This approach offers reliable forecasting for infectious disease trends.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Infectious Disease Dynamics
Background:
- Coronavirus disease 2019 (COVID-19) presented a significant public health challenge.
- Understanding disease transmission dynamics and predicting epidemiological trends are crucial for effective control.
Purpose of the Study:
- To predict the epidemiological trend of COVID-19 in Yueqing, Zhejiang province.
- To develop and validate a two-stage mathematical model integrating population mobility and epidemic control measures.
Main Methods:
- Collected data on 151 confirmed COVID-19 cases in Yueqing up to February 8, 2020.
- Developed a two-stage mathematical model incorporating population mobility between Wuhan and Yueqing.
- Utilized susceptible-exposed-infectious-recovered (SEIR) modeling principles and symptom onset dates for case importation analysis.
Main Results:
- Early COVID-19 imported cases positively correlated with travel volume from Wuhan 0, 6, and 9 days prior.
- Predicted final outbreak size was 170 cases using population travel data and SEIR modeling.
- Alternative prediction using reported imported cases yielded 165 cases, closely matching the observed 170 cases by April 27, 2020.
Conclusions:
- The two-stage modeling approach demonstrated high accuracy in predicting the COVID-19 epidemiological trend.
- Mathematical modeling integrating population mobility is a valuable tool for forecasting infectious disease outbreaks.
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