|Year : 2020 | Volume
| Issue : 6 | Page : 142-146
Impact of nonpharmacological interventions on COVID-19 transmission dynamics in India
Purvi Patel1, Aditya Athotra2, TP Vaisakh1, Tanzin Dikid3, Sudhir Kumar Jain4, NCDC COVID Incident Management Team5
1 India Epidemic Intelligence Service Officer, National Centre for Disease Control, Delhi, India
2 Senior Statistical Officer, National Centre for Disease Control, Delhi, India
3 Joint Director, National Centre for Disease Control, Delhi, India
4 Additional Director, National Centre for Disease Control, Delhi, India
|Date of Submission||05-May-2020|
|Date of Decision||10-May-2020|
|Date of Acceptance||12-May-2020|
|Date of Web Publication||2-Jun-2020|
22, Sham Nath Marg, Delhi - 110 054
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: As of May 4, 2020, India has reported 42,836 confirmed cases and 1,389 deaths from COVID-19. India's multipronged response included nonpharmacological interventions (NPIs) like intensive case-based surveillance, expanding testing capacity, social distancing, health promotion, and progressive travel restrictions leading to a complete halt of international and domestic movements (lockdown). Objectives: We studied the impact of NPI on transmission dynamics of COVID-19 epidemic in India and estimated the minimum level of herd immunity required to halt it. Methods: We plotted time distribution, estimated basic (R0) and time-dependent effective (Rt) reproduction numbers using software R, and calculated doubling time, the growth rate for confirmed cases from January 30 to May 4, 2020. Herd immunity was estimated using the latest Rtvalue. Results: Time distribution showed a propagated epidemic with subexponential growth. Average growth rate, 21% in the beginning, reduced to 6% after an extended lockdown (May 3). Based on early transmission dynamics, R0was 2.38 (95% confidence interval [CI] =1.79–3.07). Early, unmitigated Rt= 2.51 (95% CI = 2.05–3.14) (March 15) reduced to 1.28 (95% CI = 1.22–1.32) and was 1.83 (95% CI = 1.71–1.93) at the end of lockdown Phase 1 (April 14) and 2 (May 3), respectively. Similarly, average early doubling time (4.3 days) (standard deviation [SD] = 1.86) increased to 5.4 days (SD = 1.03) and 10.9 days (SD = 2.19). Estimated minimum 621 million recoveries are required to halt COVID-19 spread if Rtremains below 2. Conclusion: India's early response, especially stringent lockdown, has slowed COVID-19 epidemic. Increased testing, intensive case-based surveillance and containment efforts, modulated movement restrictions while protecting the vulnerable population, and continuous monitoring of transmission dynamics should be a way forward in the absence of effective treatment, vaccine, and undetermined postinfection immunity.
Keywords: COVID-19, India, reproduction number, severe acute respiratory syndrome coronavirus 2
|How to cite this article:|
Patel P, Athotra A, Vaisakh T P, Dikid T, Jain SK, NCDC COVID Incident Management Team. Impact of nonpharmacological interventions on COVID-19 transmission dynamics in India. Indian J Public Health 2020;64, Suppl S2:142-6
|How to cite this URL:|
Patel P, Athotra A, Vaisakh T P, Dikid T, Jain SK, NCDC COVID Incident Management Team. Impact of nonpharmacological interventions on COVID-19 transmission dynamics in India. Indian J Public Health [serial online] 2020 [cited 2020 Oct 25];64, Suppl S2:142-6. Available from: https://www.ijph.in/text.asp?2020/64/6/142/285626
NCDC COVID Incident Management Team
Himanshu Chauhan, MBBS, MD, Joint Director
Aakash Shrivastava, MD, MPH, PhD, Joint Director
Anil D Patil, MSc (Statistics), Additional Director
Sujeet Singh, MBBS, MD, Director NCDC
| Introduction|| |
Severe acute respiratory syndrome (SARS) coronavirus 2 has led to 3.44 million confirmed COVID-19 cases and 0.24 million deaths globally as of May 4, 2020. In this same time period, India reported 42,836 confirmed cases and 1,389 deaths since its first confirmed case on January 30, 2020.
Rapid spread of COVID-19 has overwhelmed local critical health-care services leading to unprecedented case fatalities in affected countries. In the absence of any known drug treatment or vaccine, unclear transmission dynamics, possible presymptomatic transmission, and case fatality rate up to 6% have driven countries to adopt drastic nonpharmacological interventions (NPIs) to slow down the virus transmission. These NPIs include active case-based surveillance, enhanced testing and isolation of cases, contact tracing, social distancing measures, and progressive travel restrictions leading to complete internal mobility restrictions (lockdown) to reduce contact rates. Many of these public health interventions were informed from previous influenza and SARS pandemics and lacked clarity of efficacy in responding to COVID-19 epidemic in different sociocultural contexts.,,, We studied the impact of NPIs implemented on changes in transmission dynamics of COVID-19 epidemic in India.
| Materials and Methods|| |
Confirmed COVID-19 case was defined as laboratory confirmation of COVID-19 infection in a person with reverse transcription-polymerase chain reaction test. Nation-wide data were being collected by special active surveillance setup by the Integrated Disease Surveillance Programme. We recorded number of daily confirmed cases till May 4, 2020 (day 96 since epidemic start) and prepared an epidemic curve. We reviewed available documents on India's COVID-19 response from websites of Ministry of Health and Family Welfare and National Centre for Disease Control, Government of India, and classified various public health measures into surveillance strengthening, testing strategy, containment activities, travel and mobility restrictions, social distancing, and health promotion.
We estimated basic reproduction number (R0) at the beginning of the outbreak, when cumulative cases reached 100, and time-dependent effective reproduction number (Rt), doubling time, and growth rate during the outbreak. R0, the average number of secondary infections directly generated by an infected case in a completely susceptible population, is a static yet context-dependent indicator of transmission during an outbreak and a determinant for herd immunity threshold. An inbuilt library (R0) in statistical software R (version Rx 188.8.131.52) was used to provide a standardized approach. We used maximum likelihood (ML) estimation to calculate the R0 value on the assumption that the number of secondary cases caused by an index case is Poisson distributed with expected value R. Rt takes into account both susceptible and nonsusceptible populations. We used an inbuilt function given in software R (version Rx 184.108.40.206), “est.R0.TD” to generate Rt. This function uses incidence data in combination with mean generation time (based on serial interval data) to generate Rt values based on the beginning and ending dates of simulation. Confidence interval (CI) was computed by multinomial simulations at each time step with the expected value of R. The time-dependant method computes reproduction numbers by averaging overall transmission networks compatible with observations. We used mean serial interval (SI) = 6 days with its standard deviation (SD) = 3.8 days based on the assessment of reported COVID-19 clusters in India till April 30, 2020. Epidemic doubling time, a time interval at which cumulative incidence doubles, is measured as (ln2)/r where r is growth rate. We measured doubling time at 7-day interval continuously since March 15, 2020. Growth rate was measured as a 7-day moving average of daily percentage increase in cumulative confirmed COVID-19 cases. We estimated the minimum level of population immunity (Pcrit) required, as recoveries from COVID-19 infection, to stop further transmission of it, using formula Pcrit= 1 − (1/Rt).
| Results|| |
The time distribution of confirmed COVID-19 cases showed a propagated source epidemic with subexponential growth [Figure 1]. Initially, single confirmed cases were reported on January 30 and February 2 and 4, 2020. Following that, no case was reported till March 2, 2020. Confirmed cases reached 100 on March 15; 1000 on March 29, and 10,000 on April 14, 2020. Maximum daily cases (2,573) were reported on May 4, 2020. Cases began to increase steadily after March 3, 2020, with fluctuating 7-day moving growth rate (10%–62%) till March 8, 2020. The growth rate was 21% on March 24, 12% on April 14, and steadily reduced to 6% as of May 4, 2020.
|Figure 1: Time distribution of confirmed COVID-19 cases and growth rate (7-day moving average, %), India, January 30–May 4, 2020.|
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India's COVID-19 response began with preventive public health measures as early as January 17, 2020 [supplementary table 1]. Major surveillance strengthening measures were– thermal screening and distribution of symptom self-declaration forms for passengers coming from China at selected airports (January 17) which expanded to other airports and travelers from other countries as the epidemic grew internationally. In addition, mandatory quarantine of passengers for 14 days (March 13), and rolling out of digital contact tracing application (April 2) was initiated. Guidelines and trainings on COVID-19 surveillance and treatment were provided throughout. The initial testing strategy included all symptomatic patients with travel history and their contacts (March 9) which were expanded to symptomatic health-care workers caring for COVID-19 cases (March 17) and hospitalized severe acute respiratory infection (SARI) cases and asymptomatic high-risk contacts of cases (March 21). Pooled sample testing (April 13) in containment zones was also added. A cluster-containment strategy was rolled out to contain local transmission (March 02). Travel restrictions began with an advisory to avoid nonessential travel to China (February 5), expanded to other affected countries, and culminated in avoidance of all international travels (March 10). These followed by international land border check-post closure (March 15) and complete stoppage of incoming international flights (March 22). Social distancing measures began with advisory against mass gathering (March 5) followed by school, sports, and entertainment centers closure and social distancing advisory at work place and commercial establishments (March 16). Complete internal mobility restriction for 21 days (lockdown) began on March 25 (Phase 1) and extended for 19 days from April 15, 2020 (Phase 2). From May 4, it was further extended (Phase 3) till May 17, 2020. Unlike Phase 1, conditional relaxation in mobility within areas not designated as COVID-19 hotspots by the respective state or district administrations was allowed during Phase 2, from April 20, 2020. In Phase 3, based on number of cases reported during the past 21 days, the country was divided into three color-coded zones and restriction was lifted accordingly. Implementation of all these measures was further enforced by the invocation of Epidemic Diseases Act 1897 and Disaster Management Act, 2005 (March 11). Intense health promotion efforts were done using print and digital mass media.
The R0 was estimated to be 2.38 (95% CI = 1.79–3.07) based on the early transmission dynamics of the first 100 cases. The estimated Rt was 1.53 (95% CI = 1–2) at the beginning of the outbreak [Figure 2]. A single, sudden peak was noted at Rt= 10.36 (95% CI = 8–12.5) on March 3 (day 34) which reduced to 1.67 (95% CI = 2.06–1.27) in the next 2 days. India reported the first 100 cases on March 15, corresponding with the estimated Rt = 2.51 (95% CI = 2.06–3.14). On April 2, 8 days after Phase 1 lockdown, the estimated Rt decreased to 1.91 (95% CI = 1.80–2.02). At the end of Phase 1 (April 14) and Phase 2 (May 3) of lockdown, Rt was 1.28 (95% CI = 1.22–1.32) and 1.83 (95% CI = 1.71–1.93). As of May 4, 2020, latest Rt = 2.04 (95% CI = 1.83–2.21) was estimated.
|Figure 2: Estimates of time-dependent effective reproduction number (Rt) for COVID-19 epidemic, India, January 30–May 4, 2020.|
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Doubling time varied (2–9.5 days) in the initial days due to imported cases. Average doubling time increased from 4.3 days (SD = 1.86) before lockdown to 5.4 days (SD = 1.03) and 10.9 days (SD = 2.19) during Phase 1 and Phase 2 of lockdown, respectively. It was 13.6 on May 4, 2020. Based on the Rt value at the end of Phase 2, we estimated Pcrit= 0.45. It amounts to about 621 million COVID-19 recoveries in the Indian population to achieve herd immunity.
| Discussion|| |
We report the transmission dynamics of confirmed COVID-19 cases in India following the implementation of NPIs in the epidemic trajectory. We demonstrate that India has been able to slow down the COVID-19 epidemic progression through these interventions.
Time distribution of the confirmed cases indicated a propagated source epidemic from an emergent respiratory pathogen with person-to-person transmission pattern. Early transmission dynamics represented by R0, suggests the possibility of an exponential rise in COVID-19 cases in India if unmitigated. This value is within the known range from Wuhan (1.4–5.7) and Italy (2.43–3.10) but lower than that of Iran (4.4 and 3.5).,, Measured over time, Rt provides insight into temporal changes as a combined effect of transmission potential and control measures., We observed a decline in Rt following NPI implementation, confirming subexponential growth akin to a mitigated epidemic; however, Rt>1 indicated ongoing disease transmission.
India was one of the top 20 countries at risk of COVID-19 importation risk from China in spite of cordon sanitaire of Wuhan. At least 43 of the first 100, including the initial three confirmed cases, were travelers from affected countries. India started entry screening for air travelers starting January 17 before the WHO declared COVID-19 as a Public Health Emergency of International Concern on January 30. Implementation of these travel restrictions was able to minimize the importation of cases till early March. In early March, at least 20 imported cases were confirmed within a few days, reflected as a sudden, major peak in Rt. This period corresponded with a rapid spread of COVID-19 cases to Europe and North America. Subsequent introduction of social distancing measures such as mass gathering restrictions, closure of schools and universities, and cluster containment measures with contact tracing reduced Rt in the range of 1.98–2.75 till beginning of Phase 1 of lockdown on March 25. During Phase 1, a continuous decline in Rt was seen till the end (April 14). These findings compare with results of various modeling studies that had shown that nonpharmacological public health interventions (social distancing, closure of schools and universities, isolation of cases, and quarantine of contacts) combined with enhanced surveillance for case identification can slow down virus transmission in the community., Similar decline in Rt following lockdown was reported from Wuhan and on cruise ship Diamond Princess after strict isolation. It may be largely due to mobility restrictions, but its synergistic effect on quicker contact tracing, isolation of cases, and better monitoring may not be ruled out for India. Rt in Phase 2 varied (1.27–1.83) but remained below 2. Minor fluctuations during these phases may be attributed to super-spreader events or expanding testing strategy., However, a rise in Rt since April 25 may be explained by partial relaxation in mobility restrictions.
India is among the countries that implemented most stringent public health measures early, before day 60 of epidemic and <700 confirmed COVID-19 cases. Our analysis provides evidence that these measures have had an impact on slowing epidemic progression and reducing critical care demand. Concurrently, India also rapidly increased its public health capacity for testing, isolation beds for COVID-19 cases, and critical care facilities. As of April 30, 2020, India conducted 602 laboratory tests per million people and marked 586 dedicated COVID-19 hospitals with 0.01 million isolation beds and 11,500 critical care beds., So far, India's health-care system seems to be handling COVID-19 cases adequately; however, in some hotspot districts, cases are growing rapidly. In addition, infection in health-care workers may strain this equilibrium in future.
In the absence of a vaccine in the near future, a gradual buildup of herd immunity in the community due to natural infection may also aid in impeding the spread of COVID-19. A gradual phase-wise lockdown exits with initiation of economic activities in nonhotspot districts may facilitate buildup of herd immunity without overwhelming health-care facilities. However, stringent measures will have to be reintroduced when case counts start to rebound. Our estimate for herd immunity at 45% is dependent on maintaining Rt of 1.83 which was achieved at the end of extended lockdown. Rt may keep increasing when the lockdown is lifted, translating into a higher proportion requirement to be recovered from COVID-19 to achieve herd immunity. It should be deliberated extremely cautiously considering critical care demand and fatalities, nature and length of immunity developed after infections, and the possibility of viral mutations.
We report limitations in Rt estimates due to the unavailability of data on symptom onset for all the cases to achieve a serial interval distribution overtime. Its interpretation should be considered against the evolving testing strategy. We have also assumed that measures and advisories issued by the government were executed timely, uniformly, and successfully throughout the country, which could not be verified independently.
| Conclusion|| |
India's health system responded to COVID-19 pandemic with swiftness. Thus far, the fallouts of the pandemic have been less severe than countries with more robust health systems. The nationwide lockdown has shown the desired effect of slowing the epidemic. As we transit through phase-wise exit strategy, we should continue to consolidate on our gains by way of increased testing, persistent contact tracing and isolating, modulating movement restrictions while protecting the vulnerable population, and continuous monitoring of transmission dynamics.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Khafaie MA, Rahim F. Cross-country comparison of case fatality rates of COVID-19/SARS-COV-2. Osong Public Health Res Perspect 2020;11:74-80.
Prem K, Liu Y, Russell TW, Kucharski AJ, Eggo RM, Davies N, et al.
The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: A modelling study. Lancet Public Health 2020;5:e261-e270.
Ferguson NM, Cummings DA, Cauchemez S, Fraser C, Riley S, Meeyai A, et al
. Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 2005;437:209-14.
Hellewell J, Abbott S, Gimma A, Bosse NI, Jarvis CI, Russell TW, et al
. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Glob Health 2020;8:e488-96.
Obadia T, Haneef R, Boëlle PY. The R0 package: A toolbox to estimate reproduction numbers for epidemic outbreaks. BMC Med Inform Decis Mak 2012;12:147.
COVID-19 Cluster Investigation Reports from Agra, Delhi, Mumbai, Pune, Kerala. National Centre for Disease Control. Forthcoming 2020.
On Kwok K, Lai F, In Wei W, Shan Wong SY, Tang JW. Herd immunity – Estimating the level required to halt the COVID-19 epidemics in affected countries. J Infect 2020. pii: S0163-4453(20) 30154-7 2020.
Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al
. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N
Engl J Med 2020;382:1199-207.
D'Arienzo M, Coniglio A. Assessment of the SARS-CoV-2 basic reproduction number, R0, based on the early phase of COVID-19 outbreak in Italy. Biosaf Heal. April 2020. Available from: [doi:10.1016/j.bsheal.2020.03.004].
Muniz-Rodriguez K, Fung IC, Ferdosi SR, Ofori SK, Lee Y, Tariq A. et al
. Severe Acute Respiratory Syndrome Coronavirus 2 Transmission Potential, Iran, 2020 [published online ahead of print, 2020 Apr 22]. Emerg Infect Dis 2020;26:10.3201/eid2608.200536. Available from: https://wwwnc.cdc.gov/eid/article/26/8/20-0536_articl
Cowling BJ, Ali ST, Ng TWY, Tsang TK, Li JCM, Fong MW, et al
. Impact assessment of non-pharmaceutical interventions against coronavirus disease 2019 and influenza in Hong Kong: An observational study. Lancet Public Health 2020;5:e279-e288.
Cori A, Ferguson NM, Fraser C, Cauchemez S. A new framework and software to estimate time-varying reproduction numbers during epidemics. Am J Epidemiol 2013;178:1505-12.
Chinazzi M, Davis JT, Ajelli M, Gioannini C, Litvinova M, Merler S, et al
. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science 2020;368:395-400.
Mandal S, Bhatnagar T, Arinaminpathy N, Agarwal A, Chowdhury A, Murhekar M, et al
. Prudent public health intervention strategies to control the coronavirus disease 2019 transmission in India: A mathematical model-based approach. Indian J Med Res 2020;151:190-9.
] [Full text]
Kucharski AJ, Russell TW, Diamond C, Liu Y, Edmunds J, Funk S, et al
. Early dynamics of transmission and control of COVID-19: A mathematical modelling study. Lancet Infect Dis 2020;20:553-8.
Rocklöv J, Sjödin H, Wilder-Smith MA. COVID-19 outbreak on the Diamond Princess cruise ship: Estimating the epidemic potential and effectiveness of public health countermeasures. J Travel Med 2020;2020:1-7.
Hale T, Angrist N, Kira B, Petherick A, Phillips T, Webster S. Variation in Government Responses to COVID-19.; 2020. Available from: http://www.bsg.oxac.uk/covidtracker
. [Last accessed on 2020 May 01].
New Delhi: Indian Council of Medical Research. Available from: https://www.icmr.gov.in/
. [Last accessed on 2020 May 03].
[Figure 1], [Figure 2]