RESUMO
Introduction: Globally, COVID-19 have impacted people's quality of life. Machine learning have recently be-come popular for making predictions because of their precision and adaptability in identifying diseases. This study aims to identify significant predictors for daily active cases and to visualise trends in daily active, posi-tive cases, and immunisations. Material and methods: This paper utilized secondary data from Covid-19 health bulletin of Uttarakhand and multiple linear regression as a part of supervised machine learning is performed to analyse dataset. Results: Multiple Linear Regression model is more accurate in terms of greater score of R2 (=0.90)as com-pared to Linear Regression model with R2=0.88. The daily number of positive, cured, deceased cases are signif-icant predictors for daily active cases (p <0.001). Using time series linear regression approach, cumulative number of active cases is forecasted to be 6695 (95% CI: 6259 - 7131) on 93rd day since 18 Sep 2022, if simi-lar trend continues in upcoming 3 weeks in Uttarakhand. Conclusion: Regression models are useful for forecasting COVID-19 instances, which will help governments and health organisations to address this pandemic in future and establish appropriate policies and recom-mendations for regular prevention.
RESUMO
The continuing new Coronavirus (COVID-19) pandemic has caused millions of infections and thousands of fatalities globally. Identification of potential infection cases and the rate of virus propagation is crucial for early healthcare service planning to prevent fatalities. The research community is faced with the analytical and difficult real-world task of accurately predicting the spread of COVID-19. We obtained COVID-19 temporal data from District Surveillance Officer IDSP, Dehradun cum District Nodal Officer- Covid-19 under CMO, Department of Medical Health and Family Welfare, Government of Uttarakhand State, India, for the period, March 17, 2020, to May 6, 2022, and applied single exponential method forecasting model to estimate the COVID-19 outbreak's future course. The root relative squared error, root mean square error, mean absolute percentage error, and mean absolute error were used to assess the model's effectiveness. According to our prediction, 5438 people are subjected to hospitalization by September 2022, assuming that COVID cases will increase in the future and take on a lethal variety, as was the case with the second wave. The outcomes of the forecasting can be utilized by the government to devise strategies to stop the virus's spread.
RESUMO
Zinc, protein, cholesterol, phospholipids, alkaline phosphatase (AlPase), acid phosphatase (AcPase), adenosine-5-triphosphatase(ATPase) and histology were studied in testis of zinc-deficient mice. Zinc and protein decreased in the 3-week experiment whereas they increased in the 6-week experiment. Zinc is involved in several functions of the cell and is regulated by hormones. Inhibition of spermatogenesis indicates for decreased zinc levels in 3-week whereas the increase in 6-week experiment indicates for accumulation of zinc in oedomatous fluid and uncontrolled diffusion of zinc across the blood testis barrier. Glycogen decreased in the 3-week as well as 6-week experiments due to blockage of androgen and spermatogenesis. Cholesterol and phospholipids increased in the 3-week experiment and decreased in 6-week experiment as both the parameters are related to steroidogenesis. Zinc deficiency leads to aspermatogenic condition and comparatively less injury to non-germinal cells. This could have blocked the transport of material across the testis barrier and therefore might have increased AlPase levels. Increased AcPase, probably represents lysosomal enzymes, as the cell debris of disorganised epithelium are to be digested and removed. ATPase increased in 3-week experiment and can be correlated to increased demands of energy of testicular cells to overcome the insults of zinc deficiency whereas the decrease in 6-week experiment could be as a result of inhibition of spermatogenesis.