Humanity has often been taken by surprise by new strains of microorganisms that adversely affect human life expectancy The coronavirus outbreak is causing an unprecedented global health emergency and a global economic slowdown Epidemiologists are aware of various specimens of viruses like the ones that cause Malaria, for instance, and scientists have been able to meet such threats by introducing drugs that make them perish at a rapid pace so that the natural human resistance to meet such attacks become sufficient for the patients to return to normal health But viruses also try to survive by transmuting themselves into different strains that are capable of overcoming the existing antiviral drugs Probably, COVID-19 may be one such transmutation Hence the main objectives are to study how the COVID-19 virus affects the population and at what stage and time the epidemic will abate As primary data is challenging to get, we use ICMR and other Govt publications for the related information Simple demographic and statistical methods are used for the analysis At the early stages of the infection, we have no protocol on how to approach the virus But now the health department has the protocol and thus even though we have no medicine, we are able to increase the recovery rate and flatten the mortality Thus by following government and health workers' directions - Break the Chain movement - one can make sure that one is safe and also the people around him So governments need to take measures for testing as many people under observation as possible and assess their COVID-19 status at the earliest
Kerala reported the first three cases of coronavirus in India in late January 2020 Kerala, one of India's most densely populated states, which makes its success in fighting the Covid-19 all the more commendable Moreover, an estimated 17% of its 35 million population employed or lives elsewhere, more than 1 million tourists visit each year, and hundreds of students study abroad, including in China All of this mobility makes the state more vulnerable to contagious outbreaks What is the strategy behind the success story? This paper compares the situation of COVID-19 pandemic in major states and Kerala by the different phase of lockdown and also highlights Kerala's fight against the pandemic We used publicly available data from https://www covid19 india org/and Covid-19 Daily Bulletin (Jan 31-May 31), Directorate of Health Services, Kerala (https://dashboard kerala gov in/) We calculate the phase-wise period prevalence rate (PPR) and the case fatality rate (CFR) of the last phase Compared to other major states, Kerala showed a better response in preventing a pandemic The equation for Kerala's success has been simple, prioritized testing, widespread contact tracing, and promoting social distance They also imposed uncompromising controls, were supported by an excellent healthcare system, government accountability, transparency, public trust, civil rights and importantly, the decentralized governance and strong grass-root level institutions The proactive measures taken by Kerala such as early detection of cases and extensive social support measures can be a model for India and the world
This paper analyses the trend in daily reported confirmed cases of COVID19 in India using joinpoint regression analysis The analysis reveals that there has been little impact of the nation-wide lockdown and subsequent extension on the progress of the COVID-19 pandemic in the country and there is no empirical evidence to suggest that relaxations under the third and the fourth phase of the lockdown have resulted in a spike in the reported confirmed cases The analysis calls for a population-wide testing approach to check the increase in the reported confirmed cases of COVID-19
The outbreak of novel coronavirus disease of 2019 (COVID-19) has a wider geographical spread than other previous viruses such as Ebola and H1N1 This study aims to provide the estimates of the basic reproduction rate (R0) and case fatality rate (CFR), which applies to a generalized population The systematic review helped to retrieve the published estimates of reproduction rate and case fatality rate in peer-reviewed articles from the PubMed MEDLINE database with defined inclusion and exclusion criteria in the period 15 December 2019 to 3 May 2020 A meta-analysis, with the inverse variance method, fixed- and random-effects model and the Forest plot, was performed to estimate the mean effect size or mean value of the basic reproduction rate and case fatality rate We estimated the robust estimate of R0 at 3 02 (2 42-3 68) persons and the robust estimate of CFR at 2 56 (2 06-3 05) percent after accounting for heterogeneity among studies using the random-effects model We found that one person is likely to infect two to three persons in the absence of any control measures, and around three percent of the population are at the risk of death within one-and-a-half months from the onset of disease COVID-19 in a generalized population
The COVID-19 cases and related deaths have been increasing throughout the world, without any let up Health planners, medical professionals, researchers, and scientists have been working relentlessly in containing the disease from the first day, with hardly any success Hence, study has been under taken with an objective to establish the statistical model of trends in COVID-19 active cases for states of India, so that disease containment performance is assessed objectively, taking model as reference level The state-wise data from Kaggle website were used to study the case load, cure rate, and levels and trends in active cases by Modified Second Degree Polynomial Model in Indian state using SPSS-22 and Microsoft Office-10 Spot snap of COVID-19situation as on 21st June, 2020 has also been depicted to fill up the gap infirmity The COVID-19 Active Case Rate varied from 0 to 139 per 100,000 populations COVID-19 Active Case Rate was highest in Delhi followed by Maharashtra, with best state Meghalaya (COVID-19 with Active case rate = 0 3) and Andaman and Nicobar (2 9) In addition, the model fitted well with Coefficient of Determinants (R2) as highest (R2=0 999), with more than 0 80 R2 in 23 states, the states with R2 less than 0 80 had very less number of COVID-19cases, hence, model did not fit well The Modified Quadratic Model had the best fit, to assess the performance of states in controlling the COVID-19 The models are expected to be used by Health Planners and Health Professionals, as the models were fitted well in 23 states Further, the reasons for wide variation in active cases of COVID-19 may be unrest in community due to labour movement Hence, for containment of COVID-19, the need is to restrict labour movement, and pay attention on enhancement of the health services along with provisioning of public necessities
