Browsing by Subject "Pandemics"
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Item Direct-to-Consumer Sexually Transmitted Infection Testing Services: A Position Statement from the American Sexually Transmitted Diseases Association(Wolters Kluwer Health, Inc., 2021-11-01) Exten, Cara; Pinto, Casey N.; Gaynor, Anne M.; Meyerson, Beth; Griner, Stacey B.; Van Der Pol, Barbara; Board of Directors of the American Sexually Transmitted Diseases, AssociationABSTRACT: Direct-to-consumer test services have gained popularity for sexually transmitted infections in recent years, with substantially increased use as a result of the SARS-CoV-2 (CoVID-19) global pandemic. This method of access has been variously known as "self-testing," "home testing," and "direct access testing." Although these online services may be offered through different mechanisms, here we focus on those that are consumer-driven and require self-collected samples, and sample shipment to a centralized laboratory without involvement of health care providers and/or local health departments. We provide the American Sexually Transmitted Diseases Association's position on utilization of these services and recommendations for both consumers and health care providers.Item Leading Predictors of COVID-19-Related Poor Mental Health in Adult Asian Indians: An Application of Extreme Gradient Boosting and Shapley Additive Explanations(MDPI, 2023-01-09) Ikram, Mohammad; Shaikh, Nazneen F.; Vishwanatha, Jamboor K.; Sambamoorthi, UshaDuring the COVID-19 pandemic, an increase in poor mental health among Asian Indians was observed in the United States. However, the leading predictors of poor mental health during the COVID-19 pandemic in Asian Indians remained unknown. A cross-sectional online survey was administered to self-identified Asian Indians aged 18 and older (N = 289). Survey collected information on demographic and socio-economic characteristics and the COVID-19 burden. Two novel machine learning techniques-eXtreme Gradient Boosting and Shapley Additive exPlanations (SHAP) were used to identify the leading predictors and explain their associations with poor mental health. A majority of the study participants were female (65.1%), below 50 years of age (73.3%), and had income >/= $75,000 (81.0%). The six leading predictors of poor mental health among Asian Indians were sleep disturbance, age, general health, income, wearing a mask, and self-reported discrimination. SHAP plots indicated that higher age, wearing a mask, and maintaining social distancing all the time were negatively associated with poor mental health while having sleep disturbance and imputed income levels were positively associated with poor mental health. The model performance metrics indicated high accuracy (0.77), precision (0.78), F1 score (0.77), recall (0.77), and AUROC (0.87). Nearly one in two adults reported poor mental health, and one in five reported sleep disturbance. Findings from our study suggest a paradoxical relationship between income and poor mental health; further studies are needed to confirm our study findings. Sleep disturbance and perceived discrimination can be targeted through tailored intervention to reduce the risk of poor mental health in Asian Indians.Item Short-term forecasting of the COVID-19 outbreak in India(Oxford University Press, 2021-06-05) Mangla, Sherry; Pathak, Ashok Kumar; Arshad, Mohd; Haque, UbydulAs the outbreak of coronavirus disease 2019 (COVID-19) is rapidly spreading in different parts of India, a reliable forecast for the cumulative confirmed cases and the number of deaths can be helpful for policymakers in making the decisions for utilizing available resources in the country. Recently, various mathematical models have been used to predict the outbreak of COVID-19 worldwide and also in India. In this article we use exponential, logistic, Gompertz growth and autoregressive integrated moving average (ARIMA) models to predict the spread of COVID-19 in India after the announcement of various unlock phases. The mean absolute percentage error and root mean square error comparative measures were used to check the goodness-of-fit of the growth models and Akaike information criterion for ARIMA model selection. Using COVID-19 pandemic data up to 20 December 2020 from India and its five most affected states (Maharashtra, Karnataka, Andhra Pradesh, Tamil Nadu and Kerala), we report 15-days-ahead forecasts for cumulative confirmed cases and the number of deaths. Based on available data, we found that the ARIMA model is the best-fitting model for COVID-19 cases in India and its most affected states.