A joinpoint regression model to determine COVID-19 virulence due to vaccination programme in India: a longitudinal analysis from 2020 to 2022
نویسندگان
چکیده
Abstract Background In late 2019, coronavirus disease, an acute respiratory illness caused by the novel (SARS-CoV-2), was designated COVID-19 and declared a pandemic. The interim guidance for prevention is through voluntary quarantine, mandatory personal protective measures maintaining social distance in public places. However, considering severity rapid spread of disease to various countries, vaccine development last option cope with dire consequences. As 14 Feb 2023, approximately 756 million people were infected 6.84 deaths. 30 Jan around 1317 crores doses administered worldwide. India, as 15 there 44.15 persons due 5,30,756 deaths (1.2%). Considering high case fatality rate population size, Government India (GOI) implemented COVID vaccination programme on 16 2021. 220.63 administered. Methods We applied joinpoint regression analysis determine virulence cases concerning their daily percentage change (DPC) average DPC (ADPC) during India’s prevaccination phases. considered database reporting covering 1018 days (19 Mar 2020 31 Dec 2022) that included both Results Three analyses adequately fit data identified four segments Although value 6.4% (95% confidence interval [CI]: 4.7 8.3) initial period 50 days, ADPC significantly declined 1.6% CI 1.3 1.8) at end phase. During phase, model two significant segment periods coincided waves SARS-CoV-2 Omicron Delta variants. corresponding values 4.6% 4.2 4.9) 21.6% 15.1 28.4), respectively. Despite these waves, reduced (− 1.6%; 95% − 1.7 1.5). Conclusions demonstrated lockdown phases ADPC. Furthermore, we quantified SARS-CoV-2, variant. study findings are from epidemiological perspective can help health professionals implement appropriate control measures.
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ژورنال
عنوان ژورنال: Bulletin of the National Research Centre
سال: 2023
ISSN: ['2522-8307', '1110-0591']
DOI: https://doi.org/10.1186/s42269-023-01071-4