Knowledge regarding immunization among mothers of under-five children in Uttarakhand

Background: Immunization is one of the most impactful and cost-effective health investments globally that helps in reducing the burden of infectious diseases keeping children safe. Mothers are the major role players with regard to their children’s immunization. Methods: A descriptive cross-sectional study was conducted to assess knowledge regarding immunization among mothers of under-five children in the Doiwala block of Dehradun, Uttarakhand. A total of one hundred mothers of under-five children were conveniently selected through door-to-door survey. A structured knowledge questionnaire on under-five immunization was administered through the interview technique to assess the knowledge of the mothers. Results: Among 100 mothers of under-five children 13% had poor knowledge, 63% had average knowledge and 24% had good knowledge regarding under-five immunization. There was significant association between age, education status and socioeconomic status of mothers with knowledge score regarding under-five immunization. Conclusions: There is a strong need to increase awareness and knowledge about immunization among children; its benefits and importance. There is also a need to educate people especially mothers regarding harmful consequences of incomplete immunization of children.

Comparison of exponential smoothing and ARIMA time series models for forecasting COVID-19 cases: a secondary data analysis

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.

Comparison of exponential smoothing and ARIMA time series models for forecasting COVID-19 cases: a secondary data analysis

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.

A Study to Analyse Covid-19 Outbreak Using Multiple Linear Regression: A Supervised Machine Learning Approach

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.