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.

Are Health Caregivers safe from workplace violence? A cross sectional study on workplace safety from Tertiary Care Hospital of Uttarakhand

Background: The prevalence of workplace violence in the healthcare sector is a problem that is frequently ignored and underreported. The performance of healthcare workers who have been the target of violence may suffer, which may have a negative effect on patient satisfaction and health. Aims & Objectives: The purpose of the current study was to determine the prevalence of workplace violence (WPV), risk factors for violence against healthcare workers, and their experiencesregarding the same. Methodology: It was a cross-sectional study conducted on 157 hospital staff at Tertiary Care Medical College of Uttarakhand. Data was gathered using a semi-structured, self-administered questionnaire that was modified from the ILO, ICN, WHO, and PSI. Data were analyzed using SPSS software (version 20). Results: Factors like age, gender, job profile, lesser work experience, night shifts, and fewer staff on duty were found to have a positive association with workplace violence. It was observed that the majority of incidents took place in the ward, and the patient’s relatives were the attacker in most of the cases. It was also seen that the majority of Hospital staff did not get bothered by the incident except by staying super alert while dealing with other patients or their relatives. Conclusion: The study concludes that while caring for patients, Hospital staff are at risk of being victims of aggressive and violent situations. To reduce this problem, strategies like training staff in order to handle such incidents in the future should be brought into practice. Laws should be made stricter & assaulting staff on duty should be made a cognizable offense with serious consequences & heavy penalties. Also, the young budding MBBS students should be trained by incorporating these strategies, laws & policies in the CBME curriculum

Time Series Analysis of COVID-19 Data- A study from Northern India

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.

Why road safety is the need of the hour? A study among youth of District Dehradun, Uttarakhand

Background: Road traffic injuries are the eighth leading cause of death globally, and the leading cause of death for young people aged 15–29 years. Each year, almost 400,000 young people under 25 years old are killed in a road traffic crash - about 1049 youngsters every day. Aims and objectives: To find out the prevalence of road safety related health risk behaviours and its determinants amongst young males of District Dehradun. Methodology: It was a cross sectional study conducted over 12 months of duration. The study sample comprised of 1800 male youth aged 15-24years studying in various schools and colleges of District Dehradun. A pre-tested and pre-structured questionnaire (YRBSS) was used. The data was entered and analysed using SPSS-version 20.0. Results: Approximately three-fourth of youth reported never using seat-belt while driving and only 4.4% reported always using helmet whereas 24.0% accepted never use of helmet. Approximately one-fourth of the total 1168 at risk subjects in past 30 days, accepted driving a vehicle while drunk and 39.9% reported use of mobile phones while driving. Personality traits (extrovert, neuroticism and lack of direction) turned out to be the major factor in road safety related health risk behaviour. Conclusion: Although, road safety related health risk was found to be more among urban youth as compared to their rural counterparts, yet it was found alarmingly high for both rural and urban study population.

Sexual Risk Behavior: A Perilous Journey By Unmarried Youth Of District Dehradun, Uttarakhand

Background: Sexual health is an integral part of overall health and well-being and determination of sexuality of the youth is an important milestone in understanding their behavior, associated risks and outcomes. The declining age at puberty and increasing age at marriage has created a longer growing period in which youth may engage in sexual health risk behaviors. This research focuses attention on estimation and involvement of the most dynamic & valuable segment of a nation’s population towards sexual risk behaviors. Aims & objectives: The aim of this study is to find out the prevalence and determinants of sexual health risk behaviors amongst youth in District Dehradun. Material and methods: It was a cross-sectional study conducted over a period of 06 months in rural and urban area of District Dehradun. The study surveyed 1800 male youth aged 15-24 years using a self-administered questionnaire (YRBSS & Big five inventory). After collection, the data was entered using the SPSS software and analyzed using SPSS and Microsoft Excel 2010. Results: Out of 1800 participants, 19% were found to be at risk of sexual health risk behavior and reported having had sex. Out of these 342 at-risk, 43.5% accepted involving in sexual activity even before adulthood, 31% reported having multiple sexual partners and 21.1% accepted not using condoms. Place of residence and personality trait were found to be important determinants of sexual health risk behaviors. Conclusion: Our study documented the high prevalence of sexual risk behavior among male youth of rural and urban area of District Dehradun.

Menstrual Hygiene and Reproductive Morbidity in Adolescent Girls in Dehradun, India.

Context: In India, adolescent girls face serious health problem due to socio-economic, environmental and cultural conditions as well as gender discrimination. Avast majority of girls in India are suffering from either general or reproductive morbidities. Unhygienic practices during menstruation expose them for Reproductive Tract Infections (RTI). If not treated early, it could lead to various disabilities and consequently affect their valuable lives. This study was done with the aim of estimating the magnitude of gynaecological morbidities among unmarried adolescent girls as well as to find out the relation between menstrual hygiene and RTI. Settings and Design: Cross-Sectional Observational study conducted in two randomly selected Inter colleges (one rural and one urban) of district Dehradun, Uttarakhand state. Methods and Material: A cross-sectional study was undertaken in school going unmarried adolescent girls to know their menstrual hygiene practices as well as reproductive morbidity. Data was collected by interview method using a pretested, prestructured questionnaire after taking consent. Statistical analysis used: percentages and Chi-square test Results: Approximately 65 % of the girls reported having dysmenorrhoea and 19 % of the girls had given the history of excessive vaginal discharge with or without low backache/lower abdominal pain. Strong association was found between Reproductive Tract Infections and poor menstrual hygiene. Conclusions: Girls should be made aware of the process of menstruation and importance of maintaining its hygiene before attaining menarche. They should also be made aware about its linkages with their forthcoming reproductive health.