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Predicting mortality in COVID‐19 is one of the most significant and difficult tasks at hand. This study compares time series and machine learning methods, including support vector machines (SVMs) and neural networks (NNs), to forecast the mortality rate in seven countries: the United States, India, Brazil, Russia, France, China, and Bangladesh. Data were gathered between December 31, 2019, when COVID‐19 began, and March 31, 2021. The study used 457 observations with 4 variables: daily confirmed cases, daily deaths, daily mortality rate, and date. To predict the death rate in the seven countries that were chosen, the data were analyzed using time series analysis and machine learning techniques. Models were compared to obtain more accurate mortality predictions. The autoregressive integrated moving average (ARIMA) model with the lowest AIC value for each nation is found through time series analysis. By increasing the hidden layer and applying machine learning techniques, the NN model for each country is chosen, and the optimal model is determined by determining the model with the lowest error value. Additionally, SVM analyzes every country and calculates its R2 and root‐mean‐square error (RMSE). The lowest RMSE value is used to compare all of the time series and machine learning models. According to the comparison table, SVM provides a more accurate model to predict the mortality rate of the seven countries, with the lowest RMSE value. During the study period, mortality rates increased in Brazil and Russia and decreased in the United States, India, France, China, and Bangladesh, according to the comparison value of RMSE in this study. Furthermore, this paper shows that SVM outperforms all other models in terms of performance. According to the author's analysis of the data, SVM is a machine learning technique that can be used to accurately predict mortality in a pandemic scenario. [ABSTRACT FROM AUTHOR]

Background: This study aimed to evaluate the knowledge, awareness, and perceived barriers regarding cervical cancer screening among women in Bangladesh. Methods: A hospital-based survey was conducted from January to April 2021 among 200 female participants. Statistical analysis of this study was performed by using Stata 13 (StataCorp LP, 4905 Lakeway Drive, College Station, TX 77845, USA), where Chisquare test was used for the determination of the correlation among different variables. Results: Among the participants, 86.5% were rural residents, 96% were married, 87.5% were housewives, 47% were uneducated, and 93.5% lived on a husband's income. Moreover, 84.5% hadn't any family history of cancer, 91.5% had no knowledge about cervical self-examination, 74.5% and 61.5% never heard of screening programs and cervical cancer, respectively (p < 0.05). In addition, 23.5% knew that early sexual activity was a risk factor for cervical cancer. Again, the outcome of ideas about other people's thinking (6%), the stigma of cancer diagnosis (15%), difficulty talking with doctors (5%), poor knowledge (12.5%), and fear of physicians and examiners (5%) were statistically significant (p < 0.05). Conclusions: This study revealed that most female respondents lack knowledge and awareness regarding cervical cancer. To improve this situation, appropriate and socially acceptable awareness programs are necessary. [ABSTRACT FROM AUTHOR]

Background and Aims: Problematic internet use (PIU) by children and adolescents is a concern for many parents. Several factors, including students' education level, the method of instruction, the dependence on the internet, and their intended use of the internet, could all be contributing factors to PIU and depression. Disturbed mental health may be attributed to the cancellation of physical education classes because of the COVID‐19 outbreak. This study aimed to assess the association of COVID‐19 pandemic with PIU and depressive symptoms in adolescent students. Methods: We performed this cross‐sectional study among 491 school‐going adolescents of 10 to 16 years. Self‐administered questionnaires were applied to collect sociodemographic information and the internet usage pattern of the participants. We measured the prevalence of PIU and depressive symptoms using the PIU questionnaire (PIUQ‐SF‐6) and patient health questionnaire‐9 (PHQ‐9). Results: The estimations of the risk group for PIU and depressive symptoms among school‐going adolescents were 80.04% and 77.80%, respectively. The latent profiling of PIU scores for obsession, neglect, and control sub‐groups were 5.82, 6.12, and 6.35, respectively. Moreover, we observed mild, moderate, and severe depressive symptoms in 48.68%, 27.70%, and 1.43% of cases. Age, education level, medium of education, financial impression, internet connection, the device used, the purpose for internet use, and the living status of respondents were significantly associated with the PIU. Reported mental health issues was associated with education level, medium of education, financial impression, and internet connection. Conclusion: The present study revealed an association of PIU and depressive symptoms with sociodemographic factors and internet usage patterns. Therefore, these results might have practical implications in clinical psychology, psychiatry, and psychotherapy. The healthcare professional can develop a context‐specific comprehensive clinical intervention plan for children and adolescents. [ABSTRACT FROM AUTHOR]