Background and aims: Covid-19 virus started from Wuhan, China and has brought the world down to its knees. It has catapulted as a venomous global phenomenon. This study focuses on the Covid-19 situation in India and its recovery time. Method: The study period is from March 1, 2020 to April 25, 2020. A random sample of 221 individuals found positive with Covid-19 from March 1, 2020 to 31st March is included in the study which is followed up April 25, 2020. There is a male preponderance in the sample with 66% of the Covid-19 patients being male and about 34% being female. Kaplan-Meier Product limit estimator, Kaplan-Meier survival curve and Log-rank test are used to analyze the recovery time of Covid-19 patients. Result: From the results of the study, it is found that the average recovery time of Covid-19 patients in India is 25 days (95% C.I. 16 days to 34 days). Only 4% of the patients get cured after 10 days of treatment. The recovery time of male and female patients is not statistically different. Recovery time of patients belonging to different age groups is also not statistically significant. Conclusion: This information on recovery time of Covid-19 patients will help planners to chalk out effective strategies.

The pandemic COVID-19, starts at the end of the year 2019, and rapidly blowout almost all over the sphere. There were more than 16.4 million people in the world pretentious by the disease up to the month of July 2020 and the miserable part was that we lost more than 0.6 million people in it. Still, an encouraging note for us was that most of the patients, more than 9.57 million people have recuperated from it. In the month of July 2020 India became the country with the third biggest amount of confirmed cases in the universe. In case of the recapture of COVID-19 patients, Spatial factor may play a significant role. To be mindful of this, the research was done to study the recovery time of the COVID-19 patients of India in respect of their spatial locations by means of spatial frailty model under Bayesian mechanism. The study time of the research was from 1st March, 2020 to 25th April, 2020. Arbitrarily selected a sample of 294 COVID-19 positive cases reported during the study period, in seven exceedingly pretentious states of India up to the month of March, 2020, were included in the study which were followed up to 25th April, 2020. Surprisingly the analysis showed that spatial effect actually plays an important role in the recovery time of the COVID-19 patients and it establishes the prominence of the application of frailty model in this circumstance. Besides this, the study also reveals the significant effect of the factors age and gender on their respective recovery times

Inflated models are generally used whenever there is an excess number of frequencies at particular count. In this study, a three-inflated Poisson (ThIP) distribution is proposed by mixing the Poisson distribution and a distribution to a point mass at three. Some of its distribution properties and reliability characteristics are studied. A simulation study is carried out to see the performance of the MLEs. In India Covid-19 implications on mental health have been abysmal. Covid-19 related suicide data of India during lockdown to the first gradual relaxation of the terms of the total lockdown (unlocking 1.0) are used to examine the appropriateness of the proposed distribution. Likelihood ratio test is used for discriminating between Poisson and the proposed distribution.

Objective: To introduce a one-inflated Binomial distribution (OIBD) and discuss its applications. Methods: Study its distributional properties, reliability characteristics, and estimation of its parameters using the method of moment estimation (MM) and maximum likelihood estimation (MLE). A simulation study has been conducted to see the behaviour of the MLEs. Two real-life examples are used to examine the pertinent of the proposed distribution. Findings : The proposed one-inflated binomial distribution (OIBD) provides better fitting in terms of AIC, BIC, and KS test comparison to the other known distributions. Novelty: Develop a new statistical distribution to study the count data having inflated frequency at count one, along with the different statistical properties. The practical utility of the distribution is also discussed with real-life examples.

The study was undertaken to understand the current JE scenario of Sivasagar, develop awareness and the importance of community participation. A questionnaire based cross-sectional survey was carried out by visiting Japanese encephalitis Patients from 2015 to 2019 to study epidemiology, sociodemographic status and the knowledge of people about the disease . Chi-square test was performed to evaluate the association among variables with significance level of 95%.Total 256 participants were interacted. 62% have lack of knowledge on vectors, 73% are unaware about proper symptoms and 69% have partial information on transmission cycle. The epidemic season for JE is from April to August of every year. The majority of infections occur in rural areas 77%. All ages are at risk. 64% of the respondents showed positive attitude towards community participation. The association between knowledge – Attitude is statistically significant (P-value .00001).Vaccination, control of vectors, and personal protection are the preventive measures. Identification of geographic locations of occurrence of disease, then a conversation with the patient and family members regarding clinical symptoms, their sociodemographic status, and knowledge regarding JE is important for the control of the disease.