@Article{info:doi/10.2196/22678,
author="Saurabh, Suman
and Verma, Mahendra Kumar
and Gautam, Vaishali
and Kumar, Nitesh
and Goel, Akhil Dhanesh
and Gupta, Manoj Kumar
and Bhardwaj, Pankaj
and Misra, Sanjeev",
title="Transmission Dynamics of the COVID-19 Epidemic at the District Level in India: Prospective Observational Study",
journal="JMIR Public Health Surveill",
year="2020",
month="Oct",
day="15",
volume="6",
number="4",
pages="e22678",
keywords="Epidemiology; SARS-CoV-2; COVID-19; serial interval; basic reproduction number; projection; outbreak response; India; mathematical modeling; infectious disease",
abstract="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. ",
issn="2369-2960",
doi="10.2196/22678",
url="http://publichealth.jmir.org/2020/4/e22678/",
url="https://doi.org/10.2196/22678",
url="http://www.ncbi.nlm.nih.gov/pubmed/33001839"
}