﻿TY - JOUR AU - Saurabh, Suman AU - Verma, Mahendra Kumar AU - Gautam, Vaishali AU - Kumar, Nitesh AU - Goel, Akhil Dhanesh AU - Gupta, Manoj Kumar AU - Bhardwaj, Pankaj AU - Misra, Sanjeev PY - 2020 DA - 2020/10/15 TI - Transmission Dynamics of the COVID-19 Epidemic at the District Level in India: Prospective Observational Study JO - JMIR Public Health Surveill SP - e22678 VL - 6 IS - 4 KW - Epidemiology KW - SARS-CoV-2 KW - COVID-19 KW - serial interval KW - basic reproduction number KW - projection KW - outbreak response KW - India KW - mathematical modeling KW - infectious disease AB - 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 (R0) 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 R0 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. 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. SN - 2369-2960 UR - http://publichealth.jmir.org/2020/4/e22678/ UR - https://doi.org/10.2196/22678 UR - http://www.ncbi.nlm.nih.gov/pubmed/33001839 DO - 10.2196/22678 ID - info:doi/10.2196/22678 ER -