Introduction: Globally, COVID-19 have impacted people's quality of life Machine learning have recently become 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, positive 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 compared to Linear Regression model with R2=0.88. The daily number of positive, cured, deceased cases are significant 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 similar 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 address this pandemic in future and establish appropriate policies and recommendations for regular prevention.
Background: In order to manage outbreaks and plan resources, health systems must be capable of accurately projecting COVID-19 case patterns. Health systems can effectively predict future illness patterns by using mathematical and statistical modelling of infectious diseases. Different methods have been used with comparatively good accuracy for various prediction goals in medical sciences. Some illustrations are provided by statistical techniques intended to forecast epidemic cases. In order to increase healthcare systems readiness, this study aimed to identify the most accurate models for COVID-19 with a high global prevalence of positive cases. Methods: Exponential smoothing model and ARIMA were employed on time series datasets to forecast confirmed cases in upcoming months and hence the effectiveness of these predictive models were compared on the basis of performance measures. Results: It was seen that the ARIMA (0,0,2) model is best fitted with smaller values of performance measures (RMSE=4.46 and MAE=2.86) while employed on the recent dataset for short duration. Holt-Winters Exponential smoothing model was found to be more accurate to deal with a longer period of time series based data. Conclusions: The study revealed that working with recent dataset is more accurate to forecast the number of confirmed cases as compared to the data collected for longer period. The early-stage warnings through these predictive models would be beneficial for governments and health professionals to be prepared with the strategies at different levels for public health prevention.
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.
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