Estimating the effects of asymptomatic and imported patients on COVID‐19 epidemic using mathematical modeling. Issue 10 (10th May 2020)

• Record Type: Journal Article
• Authors: Sun, Tingzhe; Weng, Dan
• Other Names: Luo Guangxiang (George) guestEditor.; Ly Hinh guestEditor.; Gao Shou‐Jiang guestEditor.
• Is Part Of: Journal of medical virology. Volume 92:Issue 10(2020)
• Page Start: 1995
• Page End: 2003
• Publication Date: 2020-05-10
• Subjects: computer modeling -- coronavirus -- epidemiology; Virology -- Periodicals; 616
• DOI: 10.1002/jmv.25939
• Languages: English
• ISSNs: 0146-6615

Abstract

Abstract: The epidemic of Coronavirus Disease 2019 has been a serious threat to public health worldwide. Data from 23 January to 31 March at Jiangsu and Anhui provinces in China were collected. We developed an adjusted model with two novel features: the asymptomatic population and threshold behavior in recovery. Unbiased parameter estimation identified faithful model fitting. Our model predicted that the epidemic for asymptomatic patients (ASP) was similar in both provinces. The latent periods and outbreak sizes are extremely sensitive to strongly controlled interventions such as isolation and quarantine for both asymptomatic and imported cases. We predicted that ASP serve as a more severe factor with faster outbreaks and larger outbreak sizes compared with imported patients. Therefore, we argued that the currently strict interventions should be continuously implemented, and unraveling the asymptomatic pool is critically important before preventive strategy such as vaccines. Highlights: A mathematical model with threshold behavior in recovery and asymptomatic patients. Asymptomatic infections may trigger faster outbreak with larger outbreak size. COVID‐19 outbreak size and latent period are sensitive to mutual contacts. Strict interventions should be implemented to restrict potential outbreak.