ABSTRACT
COVID-19 has been declared as a “pandemic” by the World Health Organization (WHO) and has claimed more than a million lives and over 50 million confirmed cases worldwide as of 7th November 2020. This virus can be curbed in only two ways: vaccination and other by imposing non-pharmaceutical interventions (NPIs), which are behavioral changes to a person and community. Most of the nations worldwide have imposed NPIs in the form of social distancing and lockdowns, which have been effective in reducing the pace of the virus's spread, but continued implementation has deemed social and economic losses. Hence strategic implementation of NPIs in a burst of periods should be done based on educated decisions using data about population mobility trends to find hot zones that lead to a spike in cases. These decisions will positively impact the virus's spread with lower damage to social and economic aspects. © 2022 Elsevier Inc. All rights reserved.
ABSTRACT
In this study an SIUQRD model for the Corona Virus pandemic in India has been discussed, the Ordinary Differential Equations (ODEs) have been fitted and the parameters have been estimated for the two successive waves that affected the country. The data has been taken from 30/01/2020 to 12/02/2021 for fitting the first wave and 13/02/2021 to 18/06/2021 for fitting the second wave. The value has been calculated for a span of 14 days towards the end of the second wave in absence of original data to analyze the pandemic situation for the next two weeks (19th June to 2nd July). Upon fitting the data for the two waves, the required model parameters are estimated. The study has further been extended to predict the number of cases for the same time period (19th June to 2nd July) using a machine learning model - Matern 5/2 GPR with satisfactory performance metrics. Finally, the original data during the two weeks is compared with the data predicted by the Matern 5/2 GPR model to check the performance of the model. This Matern 5/2 GPR model, to the best of our knowledge is being applied to predict the COVID-19 pandemic for the first time in India. © 2021 IEEE.
ABSTRACT
INTRODUCTION: SARS-CoV-2 primarily affects a person's respiratory system and leads to the spread of pathogenicity in the person and therefore this study evaluated the Case Fatality Rate (CFR) and Basic Reproductive Rate (R-naught) of COVID-19. METHOD: This cross-sectional descriptive study was performed on all people with COVID-19 from the first date of admission of positive PCR patients with SARS-CoV-2 until September 21, 2020, in the area of Markazi Province in Khomein, Iran. Information on the records of definitively discharged and deceased patients due to COVID-19 (having positive PCR) was extracted from 20/2/2020 to 21/9/2020, and Case Fatality Rate (CFR) and Basic Reproductive Rate (R-naught) of COVID-19 were evaluated. The sample size was 691 people. RESULTS: The CFR of the COVID-19 in the current study is 6.65%, and the rate of disease transmission among the sensitive population of Khomein city at the beginning of the study (March) averaged BASIC REPRODUCTIVE RATE= 1.75, and in the last month (September) of the study This value was reduced to BASIC REPRODUCTIVE RATE= 1.306. CONCLUSION: According to our study on Case Fatality Rate (CFR) and Basic Reproductive Rate (R-naught) of COVID-19, showed that the prevalence of the disease was high in March, which due to lack of proper observance of health protocols, we saw this problem and in the months when people have more problems. Hygiene was reduced, and the rate of transmission and mortality was reduced.
ABSTRACT
R0 (R naught) is the basic reproduction number, also known as basic reproduction ratio or rate which is an epidemiological metric used to measure the transmissibility of infectious agents. R0 is a derivative of the following variables-the duration of infectivity after the patient gets infected, the likelihood of transmission of infection per contact between a susceptible person and an infectious individual, and the contact rate. R0 is usually estimated retrospectively from serial epidemiological data or using theoretical mathematical models. Epidemiologists can calculate R0 using contact-tracing data, the most common method is to use cumulative incidence data. When mathematical models are used, R0 values are estimated using ordinary differential equations. R0 of COVID-19 as initially estimated by the World Health Organization (WHO) was between 1.4 and 2.4. The forecast is of critical importance as it will help the governments to have an estimate as well as strategize quickly to avoid any unfavorable condition. HOW TO CITE THIS ARTICLE: Achaiah NC, Subbarajasetty SB, Shetty RM. R0 and Re of COVID-19: Can We Predict When the Pandemic Outbreak will be Contained? Indian J Crit Care Med 2020;24(11):1125-1127.