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Int J Infect Dis ; 105: 424-435, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-1147705

ABSTRACT

OBJECTIVE: The World Health Organization formally announced the global COVID-19 pandemic on March 11, 2020 due to widespread infections. In this study, COVID-19 cases in India were critically analyzed during the pre-lockdown (PLD), lockdown (LD), and unlock (UL) phases. METHOD: Analyses were conducted using geospatial technology at district, state, and country levels, and comparisons were also made with other countries throughout the world that had the highest infection rates. India had the third highest infection rate in the world after the USA and Brazil during UL2.0-UL3.0 phases, the second highest after the USA during UL4.0-UL5.0 phases, and the highest among South Asian Association for Regional Cooperation (SAARC) countries in PLD-UL5.0 period. RESULTS: The trend in the number of COVID-19 cases was associated with the population density where higher numbers tended to be record in the eastern, southern, and west-central parts of India. The death rate in India throughout the pandemic period under study was lower than the global average. Kerala reported the maximum number of infections during PLD whereas Maharashtra had the highest numbers during all LD and UL phases. Eighty percent of the cases in India were concentrated mainly in highly populous districts. CONCLUSION: The top 25 districts accounted for 70.99%, 69.38%, 54.87%, 44.23%, 40.48%, and 38.96% of the infections from the start of UL1.0 until the end of UL phases, respectively, and the top 26-50 districts accounted for 6.38%, 6.76%, 11.23%, 12.98%, 13.40%, and 13.61% of cases in these phase, thereby indicating that COVID-19 cases spread during the UL period. By October 31, 2020, Delhi had the highest number of infections, followed by Bengaluru Urban, Pune, Mumbai, Thane, and Chennai. No decline in the infection rate occurred, even in UL5.0, thereby indicating a highly alarming situation in India.


Subject(s)
COVID-19/epidemiology , Geographic Information Systems , Pandemics , Spatial Analysis , COVID-19/mortality , COVID-19/prevention & control , Communicable Disease Control/methods , Geographic Mapping , Humans , India/epidemiology , Ribosomal Protein L3 , SARS-CoV-2
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