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• W4298002857 abstract "The dynamic transmission of asymptomatic and symptomatic COVID-19 infections is difficult to quantify because asymptomatic infections are not readily recognized or self-identified. To address this issue, we collected data on asymptomatic and symptomatic infections from four Chinese regions (Beijing, Dalian, Xinjiang, and Guangzhou). These data were considered reliable because the government had implemented large-scale multiple testing during the outbreak in the four regions. We modified the classical susceptible-exposure-infection-recovery model and combined it with mathematical tools to quantitatively analyze the number of infections caused by asymptomatic and symptomatic infections during dynamic transmission, respectively. The results indicated that the ratios of the total number of asymptomatic to symptomatic infections were 0.13:1, 0.48:1, 0.29:1, and 0.15:1, respectively, in the four regions. However, the ratio of the total number of infections caused by asymptomatic and symptomatic infections were 4.64:1, 6.21:1, 1.49:1, and 1.76:1, respectively. Furthermore, the present study describes the daily number of healthy people infected by symptomatic and asymptomatic transmission and the dynamic transmission process. Although there were fewer asymptomatic infections in the four aforementioned regions, their infectivity was found to be significantly higher, implying a greater need for timely screening and control of infections, particularly asymptomatic ones, to contain the spread of COVID-19." @default.
• W4298002857 created "2022-10-01" @default.
• W4298002857 creator A5044684896 @default.
• W4298002857 creator A5059037542 @default.
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• W4298002857 date "2022-09-29" @default.
• W4298002857 modified "2023-10-05" @default.
• W4298002857 title "Quantifying the dynamic transmission of COVID-19 asymptomatic and symptomatic infections: Evidence from four Chinese regions" @default.
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• W4298002857 doi "https://doi.org/10.3389/fpubh.2022.925492" @default.
• W4298002857 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36249263" @default.
• W4298002857 hasPublicationYear "2022" @default.
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