OJEpi  Vol.11 No.1 , February 2021
An Interrupted Time Series Analysis of COVID-19 Positivity before, during and after Lockdown in Four States of India
Abstract: Objectives: The objective of this study was to examine the impact of large scale non-pharmaceutical interventions on COVID-19 pandemic. Methods: We used interrupted time series analysis (ITS), a quasi-experimental model to evaluate the effect of interventions in four states of India by comparing the COVID-19 positivity before lockdown, during lockdown and opening-up period. Results: The positivity in all the four states declined during lockdown and the trends reversed soon after the lockdown measures were relaxed as the states opened-up. The rate of reduction of positivity was significantly different between states. Between the lockdown and opening-up period, an increase in positivity was recorded in all the states with significant variation between states. Conclusion: The analysis provides conclusive evidence that the lockdown measures had a positive effect in reducing the burden of COVID-19 and establishes a causal relationship.
Cite this paper: Tetali, S. , Jammy, G. , Asirvatham, E. , Kumar, B. and Choudhury, L. (2021) An Interrupted Time Series Analysis of COVID-19 Positivity before, during and after Lockdown in Four States of India. Open Journal of Epidemiology, 11, 47-55. doi: 10.4236/ojepi.2021.111005.

[1]   Ministry of Home Affairs, Government of India (2020) Communication on Lockdown-Order. No: 40-3/2020-DM-I (A).

[2]   National Disaster Management Authority (2020) Order Policy and Plan Division.

[3]   Ministry of Home Affairs, Government of India (2020) Order on Extension of Lockdown Measures, Order No: 40-3/2020-DM-I(A) 14th April 2020.

[4]   Ivorra, B., Ferrández, M.R., Vela-Pérez, M. and Ramos, A.M. (2020) Mathematical Modeling of the Spread of the Coronavirus Disease 2019 (COVID-19) Taking into Account the Undetected Infections. The Case of China. Communications in Nonlinear Science and Numerical Simulation, 88, Article ID: 105303.

[5]   Chandra, S.K., Singh, A. and Bajpai, M.K. (2020) Mathematical Model with Social Distancing Parameter for Early Estimation of COVID-19 Spread. MedRxiv.

[6]   Prem, K., Liu, Y., Russell, T.W., Kucharski, A.J., Eggo, R.M., Davies, N., et al. (2020) The Effect of Control Strategies to Reduce Social Mixing on Outcomes of the COVID-19 Epidemic in Wuhan, China: A Modelling Study. The Lancet Public Health, 5, e261-e270.

[7]   Roosa, K., Lee, Y., Luo, R., Kirpich, A., Rothenberg, R., Hyman, J.M., et al. (2020) Short-Term Forecasts of the COVID-19 Epidemic in Guangdong and Zhejiang, China: February 13-23, 2020. Journal of Clinical Medicine, 9, 596.

[8]   Flaxman, S., Mishra, S., Gandy, A., Unwin, H.J.T., Mellan, T.A., Coupland, H., et al. (2020) Estimating the Effects of Non-Pharmaceutical Interventions on COVID-19 in Europe. Nature, 584, 257-261.

[9]   Davies, N.G., Kucharski, A.J., Eggo, R.M., Gimma, A., Edmunds, W.J., Jombart, T., et al. (2020) The Effects of Non-Pharmaceutical Interventions on COVID-19 Cases, Deaths, and Demand for Hospital Services in the UK: A Modelling Study. The Lancet Public Health, 5, e375-e385.

[10]   Mandal, S., Bhatnagar, T., Arinaminpathy, N., Agarwal, A., Chowdhury, A., Murhekar, M., et al. (2020) Prudent Public Health Intervention Strategies to Control the Coronavirus Disease 2019 Transmission in India: A Mathematical Model-Based Approach. Indian Journal of Medical Research, 151, 190-199.

[11]   Hill, A.B. (1965) The Environment and Disease: Association or Causation? Journal of the Royal Society of Medicine, 58, 295-300.

[12]   Gertler, P.J., Martinez, S., Premand, P., Rawlings, L.B. and Vermeersch, C.M.J. (2016) Impact Evaluation in Practice. 2nd Edition, Inter-American Development Bank and World Bank., Washington DC.

[13]   Organisation for Economic Co-Operation and Development (2020) Evaluation Criteria-OECD.

[14]   Government of Kerala (2020) Kerala: COVID-19 Battle.

[15]   Health Department Government of Odisha (2020) Detail Status of COVID-19.

[16]   Health & Family Welfare Department Government of Tamil Nadu (2020) Daily Bulletin—StopCoronaTN.

[17]   Health and Family Welfare Department, Government of Karnataka (2020) Home— COVID-19 Information Portal.

[18]   Johns Hopkins University & Medicine (2020) Track Testing Trends. Johns Hopkins Coronavirus Resource Center.

[19]   World Health Organisation (2020) Malaria Test Positivity Rate (%): The Global Health Observatory.

[20]   World Health Organisation (2020) Coronavirus Disease 2019 (COVID-19): Situation Report, 73.

[21]   Hindustan Times (2020) City to Breach 3k Covid-19 Cases Today as Health Department Clears Backlog of Samples-Gurugram—Hindustan Times.

[22]   The Times of India (2020) Coronavirus Symptoms: Almost 80% of COVID Cases in India Asymptomatic: Union Health Minister.

[23]   Kontopantelis, E., Doran, T., Springate, D.A., Buchan, I. and Reeves, D. (2015) Regression Based Quasi-Experimental Approach When Randomisation Is Not an Option: Interrupted Time Series Analysis. BMJ, 350, h2750.

[24]   Medeiros De Figueiredo, A., Daponte Codina, A., Moreira, D.C., Figueiredo, M., Saez, M. and Cabrera León, A. (2020) Impact of Lockdown on COVID-19 Incidence and Mortality in China: An Interrupted Time Series Study. The Bulletin of World Health Organisation, 98, 150.

[25]   Choudhury, L.P., Jammy, G.R. and Pant, R. (2020) Concurrent Impact Evaluation of Lockdown Measures on COVID-19 Positivity in Three States of India. International Journal of Community Medicine and Public Health, 7, 4028-4032.

[26]   R Core Team (2020) The R Project for Statistical Computing.

[27]   Ministry of Health and Family Welfare (India) (2020) Consolidated Travel Advisory for Novel Coronavirus Disease (COVID-19). Ministry of Health and Family Welfare (India), New Delhi.

[28]   Kucharski, A.J., Russell, T.W., Diamond, C., Liu, Y., Edmunds, J., Funk, S., et al. (2020) Early Dynamics of Transmission and Control of COVID-19: A Mathematical Modelling Study. The Lancet Infectious Diseases, 20, 553-558.

[29]   Jarvis, C.I., Van Zandvoort, K., Gimma, A., Prem, K., Auzenbergs, M., O’Reilly, K., et al. (2020) Quantifying the Impact of Physical Distance Measures on the Transmission of COVID-19 in the UK. BMC Medicine, 18, Article No. 124.