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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.
https://doi.org/10.3390/jcm9020596

has been cited by the following article:

• TITLE: An Interrupted Time Series Analysis of COVID-19 Positivity before, during and after Lockdown in Four States of India

  AUTHORS: Shailaja Tetali, Guru Rajesh Jammy, Edwin Sam Asirvatham, Bogam Ranjeeth Kumar, Lincoln Priyadarshi Choudhury

  KEYWORDS: Causality, Interrupted Time Series, COVID-19, Impact Evaluation

  JOURNAL NAME: Open Journal of Epidemiology, Vol.11 No.1, January 5, 2021

  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.