印度区级 COVID-19 疫情传播动态：前瞻性观察研究。
Transmission Dynamics of the COVID-19 Epidemic at the District Level in India: Prospective Observational Study.
机构信息
Department of Community Medicine and Family Medicine, All India Institute of Medical Sciences, Jodhpur, India.
All India Institute of Medical Sciences, Jodhpur, India.
出版信息
JMIR Public Health Surveill. 2020 Oct 15;6(4):e22678. doi: 10.2196/22678.
BACKGROUND
On March 9, 2020, the first COVID-19 case was reported in Jodhpur, Rajasthan, in the northwestern part of India. Understanding the epidemiology of COVID-19 at a local level is becoming increasingly important to guide measures to control the pandemic.
OBJECTIVE
The aim of this study was to estimate the serial interval and basic reproduction number (R) to understand the transmission dynamics of the COVID-19 outbreak at a district level. We used standard mathematical modeling approaches to assess the utility of these factors in determining the effectiveness of COVID-19 responses and projecting the size of the epidemic.
METHODS
Contact tracing of individuals infected with SARS-CoV-2 was performed to obtain the serial intervals. The median and 95th percentile values of the SARS-CoV-2 serial interval were obtained from the best fits with the weibull, log-normal, log-logistic, gamma, and generalized gamma distributions. Aggregate and instantaneous R values were derived with different methods using the EarlyR and EpiEstim packages in R software.
RESULTS
The median and 95th percentile values of the serial interval were 5.23 days (95% CI 4.72-5.79) and 13.20 days (95% CI 10.90-18.18), respectively. R 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 R 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 R 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. R 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 R 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 R or instantaneous R 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.
背景
2020 年 3 月 9 日，印度西北部拉贾斯坦邦的焦特布尔报告了首例 COVID-19 病例。了解 COVID-19 在当地的流行病学情况对于指导控制大流行的措施变得越来越重要。
目的
本研究旨在估计序列间隔和基本繁殖数（R），以了解 COVID-19 疫情在地区层面的传播动态。我们使用标准的数学建模方法来评估这些因素在确定 COVID-19 应对措施的有效性和预测疫情规模方面的作用。
方法
对感染 SARS-CoV-2 的个体进行接触者追踪，以获得序列间隔。通过 Weibull、对数正态、对数逻辑、伽马和广义伽马分布的最佳拟合，得出 SARS-CoV-2 序列间隔的中位数和 95%置信区间的第 95 百分位数值。使用 R 软件中的 EarlyR 和 EpiEstim 包，使用不同的方法得出聚合和瞬时 R 值。
结果
序列间隔的中位数和 95%置信区间的第 95 百分位数分别为 5.23 天（95%置信区间 4.72-5.79）和 13.20 天（95%置信区间 10.90-18.18）。疫情爆发的前 30 天，R 值为 1.62（95%置信区间 1.07-2.17），随后降至 1.15（95%置信区间 1.09-1.21）。使用 Jombert 等人开发的泊松过程得到的瞬时 R 值峰值分别为 6.53（95%置信区间 2.12-13.38）和 3.43（95%置信区间 1.71-5.74），滑动时间窗口分别为 7 天和 14 天。使用 Wallinga 和 Teunis 方法得到的峰值 R 值分别为 2.96（95%置信区间 2.52-3.36）和 2.92（95%置信区间 2.65-3.22），滑动时间窗口分别为 7 天和 14 天。2020 年 7 月 6 日，使用 Jombert 等人的方法，分别获得 7 天和 14 天滑动时间窗口的 R 值为 1.21（95%置信区间 1.09-1.34）和 1.12（95%置信区间 1.03-1.21）。使用 Wallinga 和 Teunis 方法，分别获得 7 天和 14 天滑动时间窗口的 R 值为 0.32（95%置信区间 0.27-0.36）和 0.61（95%置信区间 0.58-0.63）。预计下个月的病例数为 2131 例（95%置信区间为 1799-2462）。如果采取合理和积极的控制措施，将传播减少 25%和 50%，可分别使疫情规模减少 58.7%和 84.0%。
结论
预测的传播减少表明，加强控制措施可使 COVID-19 疫情规模相应减少。基于 Jombart 等人的过程进行的时间依赖瞬时 R 估计比整体 R 或 Wallinga 和 Teunis 方法的瞬时 R 估计更适合指导 COVID-19 疫情在地区层面的反应。提出了一种基于本地数据的方法，以帮助指导公共卫生策略和应急能力规划。
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