Ashutosh Kumar, Adil Asghar, Prakhar Dwivedi, Gopichand Kumar, Ravi K Narayan, Rakesh K Jha, Rakesh Parashar, Chetan Sahni, Sada N Pandey
{"title":"基于对印度第二波COVID-19疫情的回顾性分析，用于预测未来COVID-19疫情的生物信息学工具：模型开发研究。","authors":"Ashutosh Kumar, Adil Asghar, Prakhar Dwivedi, Gopichand Kumar, Ravi K Narayan, Rakesh K Jha, Rakesh Parashar, Chetan Sahni, Sada N Pandey","doi":"10.2196/36860","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Since the start of the COVID-19 pandemic, health policymakers globally have been attempting to predict an impending wave of COVID-19. India experienced a devastating second wave of COVID-19 in the late first week of May 2021. We retrospectively analyzed the viral genomic sequences and epidemiological data reflecting the emergence and spread of the second wave of COVID-19 in India to construct a prediction model.</p><p><strong>Objective: </strong>We aimed to develop a bioinformatics tool that can predict an impending COVID-19 wave.</p><p><strong>Methods: </strong>We analyzed the time series distribution of genomic sequence data for SARS-CoV-2 and correlated it with epidemiological data for new cases and deaths for the corresponding period of the second wave. In addition, we analyzed the phylodynamics of circulating SARS-CoV-2 variants in the Indian population during the study period.</p><p><strong>Results: </strong>Our prediction analysis showed that the first signs of the arrival of the second wave could be seen by the end of January 2021, about 2 months before its peak in May 2021. By the end of March 2021, it was distinct. B.1.617 lineage variants powered the wave, most notably B.1.617.2 (Delta variant).</p><p><strong>Conclusions: </strong>Based on the observations of this study, we propose that genomic surveillance of SARS-CoV-2 variants, complemented with epidemiological data, can be a promising tool to predict impending COVID-19 waves.</p>","PeriodicalId":73552,"journal":{"name":"JMIR bioinformatics and biotechnology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9516867/pdf/","citationCount":"0","resultStr":"{\"title\":\"A Bioinformatics Tool for Predicting Future COVID-19 Waves Based on a Retrospective Analysis of the Second Wave in India: Model Development Study.\",\"authors\":\"Ashutosh Kumar, Adil Asghar, Prakhar Dwivedi, Gopichand Kumar, Ravi K Narayan, Rakesh K Jha, Rakesh Parashar, Chetan Sahni, Sada N Pandey\",\"doi\":\"10.2196/36860\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Since the start of the COVID-19 pandemic, health policymakers globally have been attempting to predict an impending wave of COVID-19. India experienced a devastating second wave of COVID-19 in the late first week of May 2021. We retrospectively analyzed the viral genomic sequences and epidemiological data reflecting the emergence and spread of the second wave of COVID-19 in India to construct a prediction model.</p><p><strong>Objective: </strong>We aimed to develop a bioinformatics tool that can predict an impending COVID-19 wave.</p><p><strong>Methods: </strong>We analyzed the time series distribution of genomic sequence data for SARS-CoV-2 and correlated it with epidemiological data for new cases and deaths for the corresponding period of the second wave. In addition, we analyzed the phylodynamics of circulating SARS-CoV-2 variants in the Indian population during the study period.</p><p><strong>Results: </strong>Our prediction analysis showed that the first signs of the arrival of the second wave could be seen by the end of January 2021, about 2 months before its peak in May 2021. By the end of March 2021, it was distinct. B.1.617 lineage variants powered the wave, most notably B.1.617.2 (Delta variant).</p><p><strong>Conclusions: </strong>Based on the observations of this study, we propose that genomic surveillance of SARS-CoV-2 variants, complemented with epidemiological data, can be a promising tool to predict impending COVID-19 waves.</p>\",\"PeriodicalId\":73552,\"journal\":{\"name\":\"JMIR bioinformatics and biotechnology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9516867/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JMIR bioinformatics and biotechnology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2196/36860\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR bioinformatics and biotechnology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2196/36860","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
A Bioinformatics Tool for Predicting Future COVID-19 Waves Based on a Retrospective Analysis of the Second Wave in India: Model Development Study.
Background: Since the start of the COVID-19 pandemic, health policymakers globally have been attempting to predict an impending wave of COVID-19. India experienced a devastating second wave of COVID-19 in the late first week of May 2021. We retrospectively analyzed the viral genomic sequences and epidemiological data reflecting the emergence and spread of the second wave of COVID-19 in India to construct a prediction model.
Objective: We aimed to develop a bioinformatics tool that can predict an impending COVID-19 wave.
Methods: We analyzed the time series distribution of genomic sequence data for SARS-CoV-2 and correlated it with epidemiological data for new cases and deaths for the corresponding period of the second wave. In addition, we analyzed the phylodynamics of circulating SARS-CoV-2 variants in the Indian population during the study period.
Results: Our prediction analysis showed that the first signs of the arrival of the second wave could be seen by the end of January 2021, about 2 months before its peak in May 2021. By the end of March 2021, it was distinct. B.1.617 lineage variants powered the wave, most notably B.1.617.2 (Delta variant).
Conclusions: Based on the observations of this study, we propose that genomic surveillance of SARS-CoV-2 variants, complemented with epidemiological data, can be a promising tool to predict impending COVID-19 waves.
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