This paper takes a novel approach for forecasting the risk of disease emergence by combining risk management, signal processing and econometrics to develop a new forecasting approach. We propose quantifying risk using the Value at Risk criterion and then propose a two staged model based on Multivariate Singular Spectrum Analysis and Quantile Regression (MSSA-QR model). The proposed risk measure (PLVaR) and forecasting model (MASS-QR) is used to forecast the worst cases of waterborne disease outbreaks in 22 European and North American countries based on socioeconomic and environmental indicators. The results show that the proposed method perfectly forecasts the worst case scenario for less common waterborne diseases whilst the forecasting of more common diseases requires more socioeconomic and environmental indicators.

This article investigates the role of Big Data in situations of psychological stress such as during the recent pandemic caused by the COVID-19 health crisis. Quarantine measures, which are necessary to mitigate pandemic risk, are causing severe stress symptoms to the human body including mental health. We highlight the most common impact factors and the uncertainty connected with COVID-19, quarantine measures, and the role of Big Data, namely, how Big Data can help alleviate or mitigate these effects by comparing the status quo of current technology capabilities with the potential effects of an increase of digitalization on mental health. We find that, while Big Data helps in the pre-assessment of potentially endangered persons, it also proves to be an efficient tool in alleviating the negative psychological effects of quarantine. We find evidence of the positive effects of Big Data on human health conditions by assessing the effect of internet use on mental health in 173 countries. We found positive effects in 110 countries with 90 significant results. However, increased use of digital media and exclusive exposure to digital connectivity causes negative long-term effects such as a decline in social empathy, which creates a form of psychological isolation, causing symptoms of acute stress disorder.

Background: More than half of the population of women suffer from cyclic mastalgia which can interfere with women's sexual function and affect their sexual satisfaction. The current study was conducted to determine the effect of sexual counseling on sexual satisfaction in women with cyclic mastalgia. Materials and Methods: This randomized controlled trial study was performed on 81 women with cyclic mastalgia. The subjects were randomly divided into two groups of intervention (n = 40) and control (n = 41). Intervention was performed as Permission, Limited information, Specific suggestions, Intensive therapy (PLISSIT) sex counseling in four sessions for a maximum of 90 min in the intervention group. The demographic checklist and Index of Sexual Satisfaction (ISS) were used for data collection. Follow-up was performed 1 and 3 months after the intervention. The obtained data were analyzed using repeated-measures test. Results: The intervention and control groups were in good balance in terms of demographic characteristics and sexual satisfaction scores prior to the intervention and no statistically significant differences were observed. There was a statistically significant increase in the mean (SD) score of female sexual satisfaction in the intervention group: 93 (12.52), 101.15 (7.70),101.37 (5.31), (F (1,39) = 27.4, p < 0.001). We also observed a decrease in the mean (SD) score of sexual satisfaction in the control group: 93.39 (13.12), 90.68 (8.41), 90.85 (6.57), (F (1,40) = 11.9, p < 0.001) 1 and 3 months following the intervention. Conclusions: This study revealed that individual counseling by PLISSIT sex counseling could lead to improvement in sexual satisfaction index in women with cyclic breast pain.

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