Basic Reproduction Number of the 2019 Novel Coronavirus Disease in the Major Endemic Areas of China
A Latent Profile Analysis
Bibliographic Data
Objective: The aim of this study is to analyze the latent class of basic reproduction number ( R 0 ) trends of the 2019 novel coronavirus disease (COVID-19) in the major endemic areas of China. Methods: The provinces that reported more than 500 cases of COVID-19 till February 18, 2020 were selected as the major endemic areas. The Verhulst model was used to fit the growth rate of cumulative confirmed cases. The R 0 of COVID-19 was calculated using the parameters of severe acute respiratory syndrome (SARS) and COVID-19. The latent class of R 0 was analyzed using the latent profile analysis (LPA) model. Results: The median R 0 calculated from the SARS and COVID-19 parameters were 1.84–3.18 and 1.74–2.91, respectively. The R 0 calculated from the SARS parameters was greater than that calculated from the COVID-19 parameters ( Z = −4.782 to −4.623, p < 0.01). Both R 0 can be divided into three latent classes. The initial value of R 0 in class 1 (Shandong Province, Sichuan Province, and Chongqing Municipality) was relatively low and decreased slowly. The initial value of R 0 in class 2 (Anhui Province, Hunan Province, Jiangxi Province, Henan Province, Zhejiang Province, Guangdong Province, and Jiangsu Province) was relatively high and decreased rapidly. Moreover, the initial R 0 value of class 3 (Hubei Province) was in the range between that of classes 1 and 2, but the higher R 0 level lasted longer and decreased slowly. Conclusion: The results indicated that the overall R 0 trend is decreased with the strengthening of comprehensive prevention and control measures of China for COVID-19, however, there are regional differences
Basic reproduction number · China · Disease · Environmental health · Geography · Latent class model · Population · Socioeconomics · Statistics · Veterinary medicine · COVID-19 epidemiological studies · COVID-19 Impact on Reproduction · COVID-19 Pandemic Impacts · Demography · Mathematics · Medicine · Internal Medicine
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