%0期刊文章%@ 2369-2960 %I JMIR出版物%V 6% 卡塔尔世界杯8强波胆分析N 4% P e22678 %T印度地区一级COVID-19流行病的传播动态:前瞻性观察研究A Saurabh,Suman %A Verma,Mahendra Kumar %A Gautam,Vaishali %A Kumar,Nitesh %A Goel,Akhil Dhanesh %A Gupta,Manoj Kumar %A Bhardwaj,Pankaj %A Misra,Sanjeev %+社区医学和家庭医学部，全印度医学科学研究所，学术大楼二楼，342005,Jodhpur，印度，91 7766906623，drsumansaurabh@gmail.com %K流行病学%K SARS-CoV-2 %K COVID-19 %K序列间隔%K基本复制数%K预测%K疫情响应%K印度%K数学建模%K传染病%D 2020 %7 15.10.2020 %9原始论文%J JMIR公共卫生监测%G英文%X背景:2020年3月9日，印度西北拉贾斯坦邦的热特布尔报告了第一例COVID-19病例。在地方一级了解COVID-19的流行病学对于指导控制大流行的措施变得越来越重要。目的:为了解新冠肺炎疫情在区级的传播动态，估算其序列间隔和基本复制数(R0)。我们使用标准数学建模方法评估这些因素在确定COVID-19应对措施有效性和预测疫情规模方面的效用。方法:对SARS-CoV-2感染者进行接触追踪，获取序列间隔。SARS-CoV-2序列区间的中位数和第95百分位值由与weibull、log-normal、log-logistic、gamma和广义gamma分布的最佳拟合获得。利用R软件中的EarlyR和EpiEstim包，采用不同的方法推导聚合和瞬时R0值。结果:序列间隔的中位数和第95百分位值分别为5.23天(95% CI 4.72-5.79)和13.20天(95% CI 10.90-18.18)。 R0 during the first 30 days of the outbreak was 1.62 (95% CI 1.07-2.17), which subsequently decreased to 1.15 (95% CI 1.09-1.21). The peak instantaneous R0 values obtained using a Poisson process developed by Jombert et al were 6.53 (95% CI 2.12-13.38) and 3.43 (95% CI 1.71-5.74) for sliding time windows of 7 and 14 days, respectively. The peak R0 values obtained using the method by Wallinga and Teunis were 2.96 (95% CI 2.52-3.36) and 2.92 (95% CI 2.65-3.22) for sliding time windows of 7 and 14 days, respectively. R0 values of 1.21 (95% CI 1.09-1.34) and 1.12 (95% CI 1.03-1.21) for the 7- and 14-day sliding time windows, respectively, were obtained on July 6, 2020, using method by Jombert et al. Using the method by Wallinga and Teunis, values of 0.32 (95% CI 0.27-0.36) and 0.61 (95% CI 0.58-0.63) were obtained for the 7- and 14-day sliding time windows, respectively. The projection of cases over the next month was 2131 (95% CI 1799-2462). Reductions of transmission by 25% and 50% corresponding to reasonable and aggressive control measures could lead to 58.7% and 84.0% reductions in epidemic size, respectively. Conclusions: The projected transmission reductions indicate that strengthening control measures could lead to proportionate reductions of the size of the COVID-19 epidemic. Time-dependent instantaneous R0 estimation based on the process by Jombart et al was found to be better suited for guiding COVID-19 response at the district level than overall R0 or instantaneous R0 estimation by the Wallinga and Teunis method. A data-driven approach at the local level is proposed to be useful in guiding public health strategy and surge capacity planning. %M 33001839 %R 10.2196/22678 %U http://publichealth.www.mybigtv.com/2020/4/e22678/ %U https://doi.org/10.2196/22678 %U http://www.ncbi.nlm.nih.gov/pubmed/33001839