D2EA: Depict the Epidemic Picture of COVID-19

doi:10.1007/s12204-020-2170-7

摘要/Abstract

摘要： The outbreak of coronavirus disease 2019 (COVID-19) has aroused a global alert. To release social panic and guide future schedules, this article proposes a novel mathematical model, the Delay Differential Epidemic Analyzer (D2EA), to analyze the dynamics of epidemic and forecast its future trends. Based on the traditional Susceptible-Exposed-Infectious-Recovered (SEIR) model, the D2EA model innovatively introduces a set of quarantine states and applies both ordinary differential equations and delay differential equations to describe the transition between two states. Potential variations of practical factors are further considered to reveal the true epidemic picture. In the experiment part, we use the D2EA model to simulate the epidemic in Hubei Province. Fitting to the collected real data as non-linear optimization, the D2EA model forecasts that the accumulated confirmed infected cases in Hubei Province will reach the peak at the end of February and then steady down. We also evaluate the effectiveness of the quarantine measures and schedule the date to reopen Hubei Province.

关键词: coronavirus disease 2019 (COVID-19), epidemic model, quarantine states, Susceptible-Exposed- Infectious-Recovered (SEIR), delay differential equation, non-linear optimization

Abstract: The outbreak of coronavirus disease 2019 (COVID-19) has aroused a global alert. To release social panic and guide future schedules, this article proposes a novel mathematical model, the Delay Differential Epidemic Analyzer (D2EA), to analyze the dynamics of epidemic and forecast its future trends. Based on the traditional Susceptible-Exposed-Infectious-Recovered (SEIR) model, the D2EA model innovatively introduces a set of quarantine states and applies both ordinary differential equations and delay differential equations to describe the transition between two states. Potential variations of practical factors are further considered to reveal the true epidemic picture. In the experiment part, we use the D2EA model to simulate the epidemic in Hubei Province. Fitting to the collected real data as non-linear optimization, the D2EA model forecasts that the accumulated confirmed infected cases in Hubei Province will reach the peak at the end of February and then steady down. We also evaluate the effectiveness of the quarantine measures and schedule the date to reopen Hubei Province.

Key words: coronavirus disease 2019 (COVID-19), epidemic model, quarantine states, Susceptible-Exposed- Infectious-Recovered (SEIR), delay differential equation, non-linear optimization

中图分类号:

O 29

LIU Chenzhengyi (刘陈正轶), ZHAO Jingwei (赵经纬), LIU Guohang (刘国航), GAO Yuanning (高远宁. D2EA: Depict the Epidemic Picture of COVID-19[J]. J Shanghai Jiaotong Univ Sci, 2020, 25(2): 165-176.

LIU Chenzhengyi (刘陈正轶), ZHAO Jingwei (赵经纬), LIU Guohang (刘国航), GAO Yuanning (高远宁), GAO Xiaofeng (高晓沨). D2EA: Depict the Epidemic Picture of COVID-19[J]. J Shanghai Jiaotong Univ Sci, 2020, 25(2): 165-176.

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