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Objective: Taking the COVID-19 data of the United States as an example, using software R to calculate of the serial interval (SI), basic reproduction number (R0), effective reproduction number (Re), doubling time and the number of COVID-19 using software R to provide a reference for the future epidemic response.
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Objective: To introduce the principle and method ofa-Sutte model, establish a a-Sutte model by using software R, compare the fitting and prediction effects of thea-Sutte model and multiple seasonal autoregressive integrated moving average model, SARIMA model and provides reference for the application of thea-Sutte model in epidemic prediction.
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WHAT IS ALREADY KNOWN ABOUT THIS TOPIC?: The key epidemiological parameters including serial interval, basic reproductive number (R 0), and effective reproductive number (R t) are crucial for coronavirus disease 2019 (COVID-19) control and prevention. Previous studies provided different estimations but were often flawed by some limitations such as insufficient sample size and selection bias. WHAT IS ADDED BY THIS REPORT?: In this study, a total of 116 infector-infectee pairs meeting strict inclusion criteria were selected for analysis. The mean serial interval of COVID-19 was 5.81 days (standard deviation: 3.24). The estimated mean with 95% confidence interval of R 0 was 3.39 (3.07-3.75) and 2.98 (2.62-3.38) using exponential growth (EG) and maximum likelihood (ML) methods, respectively. The R t in the early phase of the epidemic was above 1 with the peak of 4.43 occurring on January 8, and then showing subsequent declines and approaching 1 on January 24. WHAT ARE THE IMPLICATIONS FOR PUBLIC HEALTH PRACTICES?: This study supports previous findings that COVID-19 has high transmissibility and that implementing comprehensive measures is effective in controlling the COVID-19 outbreak.
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Objective: To analyze the epidemiological characteristics of coronavirus disease 2019 (COVID-19) cases in Jingzhou, Hubei, and provide scientific basis for the prevention and control of similar epidemic in future.
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Objective: Cluster epidemic characteristics of COVID-19 in Jingzhou were analyzed to provide scientific basis for prevention and control.
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Objective • To explore the spatial distribution and spatial-temporal clustering of coronavirus disease 2019(COVID-19) in Jingzhou City. Methods • Data of COVID-19 cases in Jingzhou City from January 1 to March 12, 2020 were collected. Trend surface analysis, spatial autocorrelation and spatial-temporal scanning analysis were conducted to understand the spatial-temporal distribution of COVID-19 at town (street) level in Jingzhou City, and the spatial-temporal clustering characteristics of local cases and imported cases were compared. Results • Trend surface analysis showed that the incidence rate of COVID-19 in Jingzhou City was slightly "U" from west to east, slightly higher in the east, and inverted "U" from south to north, slightly higher in the south. Global autocorrelation showed that the incidence rate of COVID-19 in Jingzhou City was positively correlated (Moran's I=0.410, P=0.000). Local spatial autocorrelation analysis showed that the highly clustered areas and hot spot areas were mainly in Shashi District, Jingzhou District and the main urban area of Honghu City (Xindi Street) (P<0.05). Five clusters were found by spatial-temporal scanning of imported cases. The cluster time of the main cluster was from January 18 to February 3, 2020, and it was centered on Lianhe Street, covering 15 towns (streets) in Shashi District and Jingzhou District (LLR=174.944, RR=7.395, P=0.000). Five clusters were found by spatial-temporal scanning of local cases. The cluster time of the main cluster was from January 20 to February 24, 2020, which was located in Xindi Street, Honghu City (LLR=224.434, RR=16.133, P=0.000). Conclusion • Obvious spatialtemporal clustering of COVID-19 was found in Jingzhou City, and Shashi District, Jingzhou District and Honghu City were the most prevalent areas.