The coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, is an acute respiratory disease that has been classified as a pandemic by the World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public healthcare systems. Hence, it is crucial to identify the key factors for mortality prediction to optimize patient treatment strategy. Different routine blood test results are widely available compared to other forms of data like X-rays, CT-scans, and ultrasounds for mortality prediction. This study proposes machine learning (ML) methods based on blood tests data to predict COVID-19 mortality risk. A powerful combination of five features neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP), and age helps to predict mortality with 96% accuracy. Various ML models (neural networks, logistic regression, XGBoost, random forests, SVM, and decision trees) have been trained and performance compared to determine the model that achieves consistently high accuracy across the days that span the disease. The best performing method using XGBoost feature importance and neural network classification, predicts with an accuracy of 90% as early as 16 days before the outcome. Robust testing with three cases based on days to outcome confirms the strong predictive performance and practicality of the proposed model. A detailed analysis and identification of trends was performed using these key biomarkers to provide useful insights for intuitive application. This study provide solutions that would help accelerate the decision-making process in healthcare systems for focused medical treatments in an accurate, early, and reliable manner.Hand hygiene practices are important not only during the corona virus disease 2019 (COVID-19) pandemic, but also critical to prevent the possible spread of other infectious diseases. This study aims to examine the current hand hygiene behaviors during the COVID-19 pandemic, post pandemic behavior intentions, and the relationship between behavior, psychosocial and contextual factors. A cross-sectional online survey was conducted from 28 May to 12 June 2020, with 896 valid responses obtained from Indonesian citizens over 18 years old. The survey questions included demographic characteristics, individual practices, risk perceptions, attitude, norm factors and ability factors related to hand hygiene during the COVID-19 pandemic. Descriptive analysis, chi square and multiple logistic regression tests were used to analyse the data. The results showed that 82.32% of female respondents and 73.37% male respondents reported handwashing practice 8 times or more per day during COVID-19 pandemic. Participants who perceivein the community.Background The coronavirus disease 2019 (COVID-19) pandemic has become a major public health crisis worldwide, and the Eastern Mediterranean is one of the most affected areas. Materials and Methods We use a data-driven approach to assess the characteristics, situation, prevalence, and current intervention actions of the COVID-19 pandemic. We establish a spatial model of the spread of the COVID-19 pandemic to project the trend and time distribution of the total confirmed cases and growth rate of daily confirmed cases based on the current intervention actions. Results The results show that the number of daily confirmed cases, number of active cases, or growth rate of daily confirmed cases of COVID-19 are exhibiting a significant downward trend in Qatar, Egypt, Pakistan, and Saudi Arabia under the current interventions, although the total number of confirmed cases and deaths is still increasing. However, it is predicted that the number of total confirmed cases and active cases in Iran and Iraq may continue to increase.  https://www.selleckchem.com/products/ABT-263.html  Conclusion The COVID-19 pandemic in Qatar, Egypt, Pakistan, and Saudi Arabia will be largely contained if interventions are maintained or tightened. The future is not optimistic, and the intervention response must be further strengthened in Iran and Iraq. The aim of this study is to contribute to the prevention and control of the COVID-19 pandemic.Background Risks attributed to chronic diseases, cancer, musculoskeletal discomfort, and infectious diseases among Indonesians were found to be associated with lifestyle behaviors, particularly in rural areas. The aim of this study was to examine the outcomes of a home-visiting lifestyle modification program on improving health risk behaviors among Indonesians living in rural areas. Methods A total of 160 Indonesians living in rural hamlets in the Yogyakarta Region of Indonesia participated in the program in the period of June 21 to July 21, 2019. In the pre-intervention home interview, learning needs of diet, exercise, hand hygiene, and substance use were identified by using structured assessment tools. In the next home visit, the visitors provided health education and facilitated lifestyle planning based on the related affective and cognitive domains of learning. Subsequent follow-up interviews were conducted 3 weeks after intervention. Results The results showed that the self-reported intake of vegetables, fruits, meat and salt, cooking with less oil, hand hygiene before eating, number of cigarettes smoked, and symptoms of muscle stiffness significantly improved after the intervention. The lifestyle modification program consisted of the affective and cognitive domains of learning, and could lead to the target behavioral changes in self-reported and observable measures over 1 month. Conclusions The findings contributed to the framework of community-based health education for health risk reduction and behavioral modification in developing rural communities where health care resources were limited. Further studies with control groups and vigorous objective measures were recommended to elucidate its long-term impacts. The factors leading to its sustainability concerning collaborative care partnerships between community residents and faculty resources are worthy of continued exploration.Background The coronavirus disease 2019 (COVID-19) outbreak is spreading rapidly around the world. Purpose We aimed to explore early warning information for patients with severe/critical COVID-19 based on quantitative analysis of chest CT images at the lung segment level. Materials and Methods A dataset of 81 patients with coronavirus disease 2019 (COVID-19) treated at Wuhan Wuchang hospital in Wuhan city from 21 January 2020 to 14 February 2020 was retrospectively analyzed, including ordinary and severe/critical cases. The time course of all subjects was divided into four stages. The differences in each lobe and lung segment between the two groups at each stage were quantitatively analyzed using the percentage of lung involvement (PLI) in order to investigate the most important segment of lung involvement in the severe/critical group and its corresponding time point. Results Lung involvement in the ordinary and severe/critical groups reached a peak on the 18th and 14th day, respectively. In the first stage, PLIs in the right middle lobe and the left superior lobe between the two groups were significantly different.