Subhankar Chattopadhyay, D. Ghosh, Raju Maiti, Samarjit Das, A. Biswas, Bibhas Chakraborty
{"title":"A study of the impact of policy interventions on daily COVID scenario in India using interrupted time series analysis","authors":"Subhankar Chattopadhyay, D. Ghosh, Raju Maiti, Samarjit Das, A. Biswas, Bibhas Chakraborty","doi":"10.1515/em-2022-0113","DOIUrl":null,"url":null,"abstract":"Abstract Objectives The rapid increase both in daily cases and daily deaths made the second wave of COVID-19 pandemic in India more lethal than the first wave. Record number of infections and casualties were reported all over India during this period. Delhi and Maharashtra are the two most affected places in India during the second wave. So in response to this, the Indian government implemented strict intervention policies (“lockdowns”, “social distancing” and “vaccination drive”) in every state during this period to prohibit the spread of this virus. The objective of this article is to conduct an interrupted time series (ITS) analysis to study the impact of the interventions on the daily cases and deaths. Methods We collect daily data for Delhi and Maharashtra before and after the intervention points with a 14-day (incubation period of COVID-19) observation window. A segmented linear regression analysis is done to study the post-intervention slopes as well as whether there were any immediate changes after the interventions or not. We also add the counterfactuals and delayed time effects in the analysis to investigate the significance of our ITS design. Results Here, we observe the post-intervention trends to be statistically significant and negative for both the daily cases and the daily deaths. We also find that there is no immediate change in trend after the start of intervention, and hence we study some delayed time effects which display how changes in the trends happened over time. And from the Counterfactuals in our study, we can have an idea what would have happened to the COVID scenario had the interventions not been implemented. Conclusions We statistically try to figure out different circumstances of COVID scenario for both Delhi and Maharashtra by exploring all possible ingredients of ITS design in our analysis in order to present a feasible design to show the importance of implementation of proper intervention policies for tackling this type of pandemic which can have various highly contagious variants.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Epidemiologic Methods","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/em-2022-0113","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract Objectives The rapid increase both in daily cases and daily deaths made the second wave of COVID-19 pandemic in India more lethal than the first wave. Record number of infections and casualties were reported all over India during this period. Delhi and Maharashtra are the two most affected places in India during the second wave. So in response to this, the Indian government implemented strict intervention policies (“lockdowns”, “social distancing” and “vaccination drive”) in every state during this period to prohibit the spread of this virus. The objective of this article is to conduct an interrupted time series (ITS) analysis to study the impact of the interventions on the daily cases and deaths. Methods We collect daily data for Delhi and Maharashtra before and after the intervention points with a 14-day (incubation period of COVID-19) observation window. A segmented linear regression analysis is done to study the post-intervention slopes as well as whether there were any immediate changes after the interventions or not. We also add the counterfactuals and delayed time effects in the analysis to investigate the significance of our ITS design. Results Here, we observe the post-intervention trends to be statistically significant and negative for both the daily cases and the daily deaths. We also find that there is no immediate change in trend after the start of intervention, and hence we study some delayed time effects which display how changes in the trends happened over time. And from the Counterfactuals in our study, we can have an idea what would have happened to the COVID scenario had the interventions not been implemented. Conclusions We statistically try to figure out different circumstances of COVID scenario for both Delhi and Maharashtra by exploring all possible ingredients of ITS design in our analysis in order to present a feasible design to show the importance of implementation of proper intervention policies for tackling this type of pandemic which can have various highly contagious variants.
日病例数和日死亡人数的快速增长使得印度第二波COVID-19大流行比第一波更具致命性。在此期间，印度各地报告的感染和伤亡人数创下了纪录。德里和马哈拉施特拉邦是印度第二波疫情中受灾最严重的两个地区。为此，印度政府在此期间在各邦实施了严格的干预政策(“封锁”、“保持社交距离”和“疫苗接种”)，以阻止病毒的传播。本文的目的是进行中断时间序列(ITS)分析，以研究干预措施对日常病例和死亡的影响。方法采用14 d (COVID-19潜伏期)观察窗，收集德里和马哈拉施特拉邦干预点前后的每日数据。采用分段线性回归分析研究干预后的坡度，以及干预后是否有直接变化。我们还在分析中加入了反事实和延迟时间效应，以研究我们的ITS设计的意义。结果在这里，我们观察到干预后的趋势在每日病例和每日死亡人数上都具有统计学意义和负相关。我们还发现，在干预开始后，趋势没有立即变化，因此我们研究了一些延迟时间效应，这些效应显示了趋势的变化是如何随着时间的推移而发生的。从我们研究中的反事实中，我们可以了解如果不实施干预措施，COVID的情况会发生什么。我们通过在分析中探索ITS设计的所有可能成分，从统计上试图找出德里和马哈拉施特拉邦不同的COVID情景，以便提出一个可行的设计，以表明实施适当的干预政策对于应对这种可能具有各种高传染性变异的大流行的重要性。
期刊介绍：
Epidemiologic Methods (EM) seeks contributions comparable to those of the leading epidemiologic journals, but also invites papers that may be more technical or of greater length than what has traditionally been allowed by journals in epidemiology. Applications and examples with real data to illustrate methodology are strongly encouraged but not required. Topics. genetic epidemiology, infectious disease, pharmaco-epidemiology, ecologic studies, environmental exposures, screening, surveillance, social networks, comparative effectiveness, statistical modeling, causal inference, measurement error, study design, meta-analysis
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
