Background and aims In India, COVID-19 case fatality rates (CFRs) have consistently been very high in states like Punjab and Maharashtra and very low in Kerala and Assam. To investigate the discrepancy in state-wise CFRs, datasets on various factors related to demography, socio-economy, public health, and healthcare capacity have been collected to study their association with CFR. Methods State-wise COVID-19 data was collected till April 22, 2021. The latest data on the various factors have been collected from reliable sources. Pearson correlation, two-tailed P test, Spearman rank correlation, and Artificial Neural Network (ANN) structures have been used to assess the association between various factors and CFR. Results Life expectancies, prevalence of overweight, COVID-19 test positive rates, and H1N1 fatality rates show a significant positive association with CFR. Human Development Index, per capita GDP, public affairs index, health expenditure per capita, availability of govt. doctors & hospital beds, prevalence of certain diseases, and comorbidities like diabetes and hypertension show insignificant association with CFR. Sex ratio, health expenditure as a percent of GSDP, and availability of govt. hospitals show a significant negative correlation with CFR. Conclusion The study indicates that older people, males of younger age groups, and overweight people are at more fatality risk from COVID-19. Certain diseases and common comorbidities like diabetes and hypertension do not seem to have any significant effect on CFR. States with better COVID-19 testing rates, health expenditure, and healthcare capacity seem to perform better with regard to COVID-19 fatality rates.

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The approximate controllability of Sobolev-type Hilfer fractional control differential systems is the main emphasis of this paper. We use fractional calculus, Gronwall's inequality, semigroup theory, and the Cauchy sequence to examine the main results for the proposed system. The application of well-known fixed point theorem methodologies is avoided in this paper. Finally, a fractional heat equation is discussed as an example.

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Although the Covid-19 Case Fatality Rate (CFR) in the Indian States and UTs has been changing with time, some states constantly appear to show significantly higher CFR than the national average. Our objective is to calculate the CFR of all the states/UTs of India and analyse the possible factors behind the disparities in it. Research papers and news articles on Covid-19 were explored to understand the factors responsible for the CFR disparities in the States/UTs. State-wise CFR was calculated and Correlated with Covid-19 Testing Rates and data from Demographic & Healthcare factors, using Spearman’s Rank Correlation Coefficient Methodology. The overall Covid-19 CFR in India was among the lowest (1.76%) in the world but varied vastly from one state to another. Where the states like Punjab and Maharashtra constantly have the highest CFR in the country, states like Assam, Kerala, and Bihar have the lowest. In the correlation analysis, a weak agreement (+0.33) between state-wise CFR and ‘Test Positive Rate’ was found. CFR and ‘Life Expectancy at 60’ showed a moderate agreement (+0.49). Healthcare components like ‘Number of Doctors Per Million People’ and ‘Number of Hospital Beds’ showed very weak agreement with CFR. Where the higher Life Expectancy and Test Positive Rates clearly tend to increase CFR, Healthcare Facilities had surprisingly little effect on it. Analyses of various news articles suggested that Comorbidities, Availability of Essential Drugs, Trained Manpower, Contact Tracings, and Hospital Referral Time were also some of the major factors affecting CFR.