TITLE:
A Study on the Global Scenario of COVID-19 Related Case Fatality Rate, Recovery Rate and Prevalence Rate and Its Implications for India—A Record Based Retrospective Cohort Study
AUTHORS:
Vinod K. Ramani, R. Shinduja, K. P. Suresh, Radheshyam Naik
KEYWORDS:
Case Fatality Rate, COVID-19, Prevalence Rate, Recovery Rate, Statistical Analysis
JOURNAL NAME:
Advances in Infectious Diseases,
Vol.10 No.3,
November
24,
2020
ABSTRACT: Importance: Corona virus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pandemic claiming millions of lives since the first outbreak was reported in Wuhan, China during December 2019. It is thus important to make cross-country comparison of the relevant rates and understand the socio-demographic risk factors. Methods: This is a record based retrospective cohort study. Table 1 was extracted from https://www.worldometers.info/coronavirus/ and from the Corona virus resource center (Table 2, Figures 1-3), Johns Hopkins University. Data for Table 1 includes all countries which reported >1000 cases and Table 2 includes 20 countries reporting the largest number of deaths. The estimation of CFR, RR and PR of the infection, and disease pattern across geographical clusters in the world is presented. Results: From Table 1, we could infer that as on 4th May 2020, COVID-19 has rapidly spread world-wide with total infections of 3,566,423 and mortality of 248,291. The maximum morbidity is in USA with 1,188,122 cases and 68,598 deaths (CFR 5.77%, RR 15% and PR 16.51%), while Spain is at the second position with 247,122 cases and 25,264 deaths (CFR 13.71%, RR 38.75%, PR 9.78%). Table 2 depicts the scenario as on 8th October 2020, where-in the highest number of confirmed cases occurred in US followed by India and Brazil (cases per million population: 23,080, 5007 & 23,872 respectively). For deaths per million population: US recorded 647, while India and Brazil recorded 77 and 708 respectively. Conclusion: Studying the distribution of relevant rates across different geographical clusters plays a major role for measuring the disease burden, which in-turn enables implementation of appropriate public healthcare measures.