key: cord-0877052-nney9kuq authors: Singh, B. B.; Lowerison, M.; Lewinson, R. T.; Vallerand, I. A.; Deardon, R.; Gill, J. P.; Gill, B. S.; Barkema, H. W. title: Public health interventions in India slowed the spread of COVID-19 epidemic dynamics date: 2020-06-07 journal: nan DOI: 10.1101/2020.06.06.20123893 sha: 3820b849eea1da4304b067f52b7b95ffbfabcfae doc_id: 877052 cord_uid: nney9kuq Background The government of India implemented social distancing interventions to contain the COVID-19 epidemic. However, effects on epidemic dynamics are yet to be understood. Methods Rates of laboratory-confirmed COVID-19 infections per day and effective reproduction number (Rt) were estimated for 4 periods (Pre-lockdown and Lockdown Phases 1 to 3) according to nationally implemented phased interventions. Adoption of these interventions was estimated using Google mobility data. Estimates at the national level and for 12 Indian states most affected by COVID-19 are presented. Findings Daily case rates ranged from 0.03 to 30.05/10 million people across 4 discrete periods in India. From May 4-17, 2020, the National Capital Territory (NCT) of Delhi had the highest case rate (222/10 million people/day), whereas Kerala had the lowest (2.18/10 million/day). Average Rt was 1.99 (95% CI 1.93-2.06) for India; it ranged from 1.38 to 2.78, decreasing over time. Median mobility in India decreased in all contact domains, with the lowest being 21% in retail/recreation (95% CI 13-46%), except home which increased to 129% (95% CI 117-132%) compared to the 100% baseline value. Interpretation The Indian government imposed strict contact mitigation, followed by a phased relaxation, which slowed the spread of COVID-19 epidemic progression in India. The identified daily COVID-19 case rates and Rt will aid national and state governments in formulating ongoing COVID-19 containment plans. Furthermore, these findings may inform COVID-19 public health policy in developing countries with similar settings to India. Funding Non-funded. Team, http://www.r-project.org). Epidemic curves were plotted, based on laboratory 1 8 0 diagnosis date and intervention periods described. Choropleth maps describing geographical 1 8 1 distributions of COVID-19 case rates for the country and 12 Indian states through 4 1 8 2 intervention periods were generated by R software. The R t for 12 Indian states and for the entire country were calculated as per the 1 8 4 method developed by Cori et al. 13 . This method estimates R t from the incidence time-series 1 8 5 and incorporates uncertainty in the distribution of the serial interval. We used the daily 1 8 6 number of reported COVID-19 cases from the above-mentioned official data sources. The 5-d moving average was used to estimate R t and its 95% credible interval on each day. The first locally transmitted COVID-19 case is thought to have occurred in India on 1 9 1 March 5, 2020 and the Indian government closed international borders and air travel on and enables presumption of a closed population and ensures an appropriate total case count 13 . Therefore, the R t was estimated from March 22 to May 24 and results of initial burn-in period 1 9 6 (when both imported and locally transmitted cases were reported; Fig. 1 ) are not presented. For the 12 states, R t was estimated for the whole period, but was presented from the day when 1 9 8 50 cumulative cases were reported (or from March 22 onwards, whichever is later) given the Descriptive analyses to report changes in mobility index were conducted according to The COVID-19 epidemic started in India on January 30, 2020 with the first imported reported from 33 of the 36 states/union territories of the country 16 . Number of cases were minimal during the Pre-Lockdown Period but continued to rise 2 1 1 in subsequent Lockdown periods (Phases 1-3), with approximately 5,000 cases/day reported 2 1 2 at the end of Lockdown Phase 3 (Fig. 1 ). There were obvious differences among states in 2 1 3 epidemic progression (Fig. 2 The case rate was 0·03, 3·50, 12·87, and 30·05 per 10 million people per day across 2 2 3 the 4 discrete periods in India. Overall, the case rate was 7 per 10 million people per day from 2 2 4 30 January 2020 to 17 May 2020. The rate of increase was maximal from Pre-lockdown 2 2 5 Period through Lockdown Phase 1 and minimal from Lockdown Phase 2 through Phase 3 in 2 2 6 the country (Table 1) . . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted June 7, 2020. . https: //doi.org/10.1101 //doi.org/10. /2020 There were large differences in incidence rates across 12 Indian states and across time 2 2 8 periods (Table 1; Fig. 3 ). Among all states, the highest case rate was 53 per 10 million people 2 2 9 per day from NCT of Delhi, whereas the lowest case rate was 2 per 10 million people per day 2 3 0 from Kerala from 30 January 2020 to 17 May 2020 (Table 1) . to Phase 3 (Fig. 3) . The R t differed across all 4 periods. Average R t was 1·99 (95% CI 1·93-2·06) for the Strong geographic differences were identified in R t across discrete periods in the West Bengal (1·92) state from 30 January 2020 to 17 May 2020. The R t was < 1·5 for Kerala Median mobility was 21% for retail and recreation, 53% at the grocery and pharmacy, 2 5 1 42% at parks, 34% at transit stations, 38% at workplaces and 129% at residential places from (Table 2 ). Mobility at grocery and pharmacy (median 37%), to 14 April 2020, during Lockdown Phase 1 (Fig. 4) . Interestingly, mobility for retail and 2 5 5 recreation (median 15%) and Parks (median 38%) was lowest from 15 April 2020 to 3 May 2 5 6 2020, during Lockdown Phase 2 (representing relaxation). Note that these values were 2 5 7 derived in comparison to a 100% baseline mobility in the country/state value when no such 2 5 8 interventions were imposed. The NCT of Delhi had the lowest mobility among the 12 states when compared to 2 6 0 100% baseline value at the retail and recreation (median 14%), grocery and pharmacy 2 6 1 3 0 1 derive R t estimates, so these estimates could not be compared. The mobility index highlighted the adoption level of the public health contact important role to contain COVID-19 epidemic in the country and will perhaps explain in part 3 0 8 why our calculated R t for India was substantially lower than many European countries and 3 0 9 the United States where mobility did not decrease as drastically as in India. The current study had some limitations. Overall testing rate has not been very high in epidemic curve of COVID-19 28 . Therefore, an underestimated case rate in the initial stage of 3 1 8 the epidemic cannot be ruled out. Additionally, migration of COVID-19 cases between states 3 1 9 cannot be excluded. As per the census of India (2011), 29·9% of total human population are 3 2 0 migrants and 13·8% of the total population migrates between states, possibly due to social, . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 7, 2020. . https://doi.org/10.1101/2020.06.06.20123893 doi: medRxiv preprint not included for Tamil Nadu estimates, such data were not available from other states. Therefore, under or overestimation at the state level cannot be ruled out. Many other epidemiologic variables such as symptom-onset date, proportion of 3 2 9 asymptomatic or undiagnosed cases, as well as diagnostic testing patterns, remain India. Availability of additional data, hopefully in the near future, are expected to further 3 3 5 improve these efforts. The Indian government imposed strict contact mitigation followed by phased 3 3 8 relaxation, which slowed the spread of COVID-19 epidemic progression in India. These The study was not funded and had no external influence in study design, data collection, data 3 4 7 analysis, data interpretation, or writing of the report. The corresponding author had full The authors acknowledge India's National and State Health departments for collecting the 17 May 2020). . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 7, 2020. . https://doi.org/10.1101/2020.06.06.20123893 doi: medRxiv preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 7, 2020. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 7, 2020. . It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 7, 2020. Affairs, Government of India (https://www.mha.gov.in/media/whats-new). . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 7, 2020. . https://doi.org/10.1101/2020.06.06.20123893 doi: medRxiv preprint . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 7, 2020. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 7, 2020. . https://doi.org/10.1101/2020.06.06.20123893 doi: medRxiv preprint Grocery and pharmacy Grocery and pharmacy Grocery and pharmacy Grocery and pharmacy Grocery and pharmacy Grocery and pharmacy