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A joinpoint regression model to determine COVID-19 virulence due to vaccination programme in India: a longitudinal analysis from 2020 to 2022.

  • Academic Journal
  • Perumal, Vanamail1 (AUTHOR)
  • Bulletin of the National Research Centre. 7/4/2023, Vol. 47 Issue 1, p1-9. 9p.
  • Background: In late 2019, coronavirus disease, an acute respiratory illness caused by the novel coronavirus (SARS-CoV-2), was designated COVID-19 and declared a pandemic. The interim guidance for prevention is through voluntary quarantine, mandatory quarantine, personal protective measures and maintaining social distance in public places. However, considering the severity and rapid spread of the disease to various countries, vaccine development was the last option to cope with the dire consequences. As of 14 Feb 2023, approximately 756 million people were infected with COVID-19 and 6.84 million deaths. As of 30 Jan 2023, around 1317 crores of vaccine doses were administered worldwide. In India, as of 15 Feb 2023, there were approximately 44.15 million infected persons due to COVID-19 and 5,30,756 deaths (1.2%). Considering the high case fatality rate and population size, the Government of India (GOI) implemented the COVID vaccination programme on 16 Jan 2021. As of 15 Feb 2023, approximately 220.63 crores of vaccine doses were administered. Methods: We applied joinpoint regression analysis to determine the virulence of COVID-19 cases concerning their daily percentage change (DPC) and average DPC (ADPC) during India's prevaccination and vaccination phases. We considered the database of daily reporting of COVID-19 cases covering 1018 days (19 Mar 2020 to 31 Dec 2022) that included both prevaccination and vaccination phases. Results: Three joinpoint regression analyses adequately fit the data and identified four segments during the prevaccination and vaccination phases. Although the DPC value was 6.4% (95% confidence interval [CI]: 4.7 to 8.3) in the initial period of 50 days, the ADPC value significantly declined to 1.6% (95% CI 1.3 to 1.8) at the end of the prevaccination phase. During the vaccination phase, the model identified two significant segment periods that coincided with the waves of SARS-CoV-2 and Omicron Delta variants. The corresponding DPC values were 4.6% (95% CI 4.2 to 4.9) and 21.6% (95% CI 15.1 to 28.4), respectively. Despite these waves, COVID vaccination significantly reduced the ADPC value (− 1.6%; 95% CI − 1.7 to − 1.5). Conclusions: We demonstrated the lockdown and vaccination phases significantly reduced ADPC. Furthermore, we quantified the severity of SARS-CoV-2, the Delta and the Omicron variant. The study findings are significant from an epidemiological perspective and can help health professionals to implement appropriate control measures. [ABSTRACT FROM AUTHOR]
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