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Study Ranks New York City as Top Cannabis-Consuming City in the World

Study Ranks New York City as Top Cannabis-Consuming City in the World
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MyJournals org - Science - IJERPH, Vol 20, Pages 5941: Estimating Overall and Cause-Specific Excess Mortality during the COVID-19 Pandemic: Methodological Approaches Compared (International Journal of Environmental Research and Public Health)

MyJournals.org - Science - IJERPH, Vol. 20, Pages 5941: Estimating Overall and Cause-Specific Excess Mortality during the COVID-19 Pandemic: Methodological Approaches Compared (International Journal of Environmental Research and Public Health)

Vulnerabilities and exposures to rise to 1,900 a month in 2023: Coalition

The cybersecuirty insurer predicts that the 1,900 CVEs would include 270 high-severity and 155 critical-severity vulnerabilities. The predictions are based on data collected over the last ten years.

Vulnerabilities and exposures to rise to 1,900 a month in 2023: Coalition

The cyber insurer predicts that the 1,900 CVEs would include 270 high-severity and 155 critical-severity vulnerabilities. The predictions are based on data collected over the last ten years.

Forecasting healthcare cost in Australia using health insurance claims by Koliya Webadu Wedanage, Rohan Wickramasuriya et al

Private health insurance (PHI) companies have a growing claims dataset that could be used for cost forecasting. This study focused on addiction, mental health, obesity and musculoskeletal disorders disease groups, the costliest disease groups in Australia. PHI claims dataset has been employed to forecast cost using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. PHI company experts had been working along with the study to provide assistance in understanding data and relevant information from the PHI point of view. Ten years worth of data has been split into 90% training (2008-2016) and 10% testing (2017) datasets. SARIMA model parameters have been chosen while minimizing the error component. As per the outcomes, addiction, mental health, obesity, and musculoskeletal disorders cost forecasting have been done with an accuracy of 88.43%, 91.08%, 81.93% and 88.82%, respectively. As per the further improvements, exogenous variables not present in the used dataset could

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