<strong>Objectives:</strong> The objective of this study was to examine the impact of large scale non-pharmaceutical interventions on COVID-19 pandemic. <strong>Methods:</strong> We used interr...<strong>Objectives:</strong> The objective of this study was to examine the impact of large scale non-pharmaceutical interventions on COVID-19 pandemic. <strong>Methods:</strong> 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. <strong>Results:</strong> 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. <strong>Conclusion:</strong> 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.展开更多
<strong>Introduction:</strong> More than a million people are reported to have been infected with COVID in India, since the beginning of the pandemic. However, the epidemic is not the same across the count...<strong>Introduction:</strong> More than a million people are reported to have been infected with COVID in India, since the beginning of the pandemic. However, the epidemic is not the same across the country. Though there are state-level variations rapidly changing disease dynamics and the response has created uncertainty towards appropriate use of models to project for the future. <strong>Method:</strong> This paper aims at using a validated semi-mechanistic stochastic model to generate short term forecasts. This analysis used data available at the respective state government bulletins for four states. The analysis used a simplified transmission model using Markov Chain Monte Carlo simulation with Metropolis-Hastings updating. <strong>Results:</strong> Two weeks were used to compare the results with the actual data. The forecasted results are well within the 25<sup>th</sup> and 75<sup>th</sup> percentile of the actual cases reported by the respective states. The results indicate a reliable method for a real-time short term forecasting of COVID-19 cases. The 1st week projected interquartile range and actual;reported cases for the state of Kerala, Tamil Nadu, Andhra Pradesh and Odisha were (1064 - 2532) 2234, (17,503 - 50,125) 27,214, (5225 - 11,003) 9563, (2559 - 4461) 3925, respectively. Similarly, the 2<sup>nd</sup> week projected interquartile range and actual;reported cases were (1055 - 7803) 4221, (18,298 - 73,952) 31,488, (4705 - 23,224) 13,357, (2701 - 9037) 4175 respectively. <strong>Conclusion:</strong> This real-time forecast can be used as an early warning tool for projecting the changes in the epidemic in the near future triggering proactive management steps.展开更多
文摘<strong>Objectives:</strong> The objective of this study was to examine the impact of large scale non-pharmaceutical interventions on COVID-19 pandemic. <strong>Methods:</strong> 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. <strong>Results:</strong> 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. <strong>Conclusion:</strong> 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.
文摘<strong>Introduction:</strong> More than a million people are reported to have been infected with COVID in India, since the beginning of the pandemic. However, the epidemic is not the same across the country. Though there are state-level variations rapidly changing disease dynamics and the response has created uncertainty towards appropriate use of models to project for the future. <strong>Method:</strong> This paper aims at using a validated semi-mechanistic stochastic model to generate short term forecasts. This analysis used data available at the respective state government bulletins for four states. The analysis used a simplified transmission model using Markov Chain Monte Carlo simulation with Metropolis-Hastings updating. <strong>Results:</strong> Two weeks were used to compare the results with the actual data. The forecasted results are well within the 25<sup>th</sup> and 75<sup>th</sup> percentile of the actual cases reported by the respective states. The results indicate a reliable method for a real-time short term forecasting of COVID-19 cases. The 1st week projected interquartile range and actual;reported cases for the state of Kerala, Tamil Nadu, Andhra Pradesh and Odisha were (1064 - 2532) 2234, (17,503 - 50,125) 27,214, (5225 - 11,003) 9563, (2559 - 4461) 3925, respectively. Similarly, the 2<sup>nd</sup> week projected interquartile range and actual;reported cases were (1055 - 7803) 4221, (18,298 - 73,952) 31,488, (4705 - 23,224) 13,357, (2701 - 9037) 4175 respectively. <strong>Conclusion:</strong> This real-time forecast can be used as an early warning tool for projecting the changes in the epidemic in the near future triggering proactive management steps.
