Background and aims: As, the COVID-19 has been deemed a pandemic by World Health Organization (WHO), and since it spreads everywhere throughout the world, investigation in relation to this disease is very much essential. Investigation of pattern in the occurrence of COVID-19, to check the influence of different meteorological factors on the incidence of COVID-19 and prediction of incidence of COVID-19 are the objectives of this paper. Methods: For trend analysis, Sen's Slope and Man-Kendall test have been used, Generalized Additive Model (GAM) of regression has been used to check the influence of different meteorological factors on the incidence and to predict the frequency of COVID-19, and Verhulst (Logistic) Population Model has been used. Results: Statistically significant linear trend found for the daily-confirmed cases of COVID-19. The regression analysis indicates that there is some influence of the interaction of average temperature (AT) and average relative humidity (ARH) on the incidence of COVID-19. However, this result is not consistent throughout the study area. The projections have been made up to 21st May, 2020. Conclusions: Trend and regression analysis give an idea of the incidence of COVID-19 in India while projection made by Verhulst (Logistic) Population Model for the confirmed cases of the study area are encouraging as the sample prediction is as same as the actual number of confirmed COVID-19 cases.
This article attempts to estimate timeliness of vaccination coverage and to model the pattern of timeliness of vaccination using techniques of survival analysis in order to trace the determinants of age-appropriate immunization status of children. Multistage cluster sampling has been used to collect information on immunization and other related variables using a pre-tested questionnaire from the universe of children of age between 12 and 36 months of two districts of Assam, India. At first, the Kaplan-Meier product limit estimator has been applied to estimate the age-appropriate immunization coverage. Though the immunization coverage as a whole is quite satisfactory in the study area, the Kaplan-Meier estimator shows poor age-appropriate immunization coverage which necessitates studying the impact of different demographic and socio-economic factors on this problem. In this context, the Cox proportional hazard model has been used which is found to be a good fit. The findings of this model show that education of mother, caste, religion and socio-economic status of the family have significant impact on the age-appropriate immunization coverage of children. Thus, it can be concluded that though the child immunization coverage in Assam has been inclining, there is still a lot of concern over the timeliness of vaccination coverage.
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
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