A susceptible-exposed-infected-quarantined-recovered (SEIQR) model for predicting the trajectory of the COVID-19 epidemic

In recent days, the outbreak of COVID-19 poses a great threat to global public health. While great efforts have been made to combat the virus by medical workers of various countries, considerable attention has also been paid to forecast the trend of the epidemic by the researchers, which may help th...

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Bibliographic Details
Published in2020 Chinese Automation Congress (CAC) pp. 58 - 63
Main Authors Li, Baoxue, Liu, Zihang, Zhao, Chuihui, Sun, Youxian
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.11.2020
Subjects
ABC-SMC algorithm
Computational modeling
COVID-19
Data models
Epidemics
Mathematical model
Numerical models
Predictive models
quarantine
SEIQR model
SEIR model
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Summary:In recent days, the outbreak of COVID-19 poses a great threat to global public health. While great efforts have been made to combat the virus by medical workers of various countries, considerable attention has also been paid to forecast the trend of the epidemic by the researchers, which may help the government formulate corresponding policies. In this paper, we proposed an improved SEIR epidemiological model, named SEIQR (susceptible-exposed-infected-quarantined-recovered). Quarantine group and several time-varying parameters were considered to make the model better approximate the real situation. Besides, ABC-SMC algorithm was introduced to tackle parameter inference problem and ABC-SMC model selection framework was employed to demonstrate the superiority of SEIQR over traditional methods. Extensive experiments have shown that the proposed SEIQR model can perform well in identifying epidemic trends and predicting future development.
ISSN:2688-0938
DOI:10.1109/CAC51589.2020.9327667