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Browsing by Author "Saleh, Nagam"

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    Mining COVID-19 data to predict the effect of policies on severity of outbreaks
    (2023) El-Hajj, Mohamad; Anton, Calin; Anton, Cristina; Dobosz, Dominic; Smith, Iain; Deiab, Fattima; Saleh, Nagam
    During the years 2020, 2021, and partially 2022, the COVID-19 virus ran rampant across the globe, causing devastating effects on the masses. Using data mining techniques, we explored factors linked to severe cases of COVID-19 and tried to identify the effect of different government policies on the evolution of the severity of infections. Four countries were selected with a date range of the year 2021 to investigate each region’s efforts regarding vaccine distribution and specific policies enacted for COVID-19 suppression. Pearson’s Correlation Coefficients were used to help establish initially relationships between the policies, vaccines, and severe cases. We used the identified factors to predict the number of new COVID-19 cases and hospital ICU admissions. We included all the country data from Our World in Data (OWID) for this phase. Our investigation indicates that, given enough data, long-range trend predictions can be obtained using Random Forest Regressors. A trained Random Forest model can readily explain factors that effectively slow the spread of COVID-19. With proposed policies given as input, the model can return the expected number of cases, thus informing policies without spending multiple weeks tracking results.
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    Mining COVID-19 data to predict the effect of policies on severity of outbreaks
    (2023) El-Hajj, Mohamad; Anton, Calin; Anton, Cristina; Dobosz, Dominic; Smith, Iain; Deiab, Fattima; Saleh, Nagam
    During the years 2020, 2021, and partially 2022, the COVID-19 virus ran rampant across the globe, causing devastating effects on the masses. Using data mining techniques, we explored factors linked to severe cases of COVID-19 and tried to identify the effect of different government policies on the evolution of the severity of infections. Four countries were selected with a date range of the year 2021 to investigate each region’s efforts regarding vaccine distribution and specific policies enacted for COVID-19 suppression. Pearson’s Correlation Coefficients were used to help establish initially relationships between the policies, vaccines, and severe cases. We used the identified factors to predict the number of new COVID-19 cases and hospital ICU admissions. We included all the country data from Our World in Data (OWID) for this phase. Our investigation indicates that, given enough data, long-range trend predictions can be obtained using Random Forest Regressors. A trained Random Forest model can readily explain factors that effectively slow the spread of COVID-19. With proposed policies given as input, the model can return the expected number of cases, thus informing policies without spending multiple weeks tracking results.
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    A web-based training module in geriatric depression for future health and allied health professionals
    (2024) Azulai, Anna; Tong, Hongmei; Saleh, Nagam; Brown, Ellen; Vihos, Jill; Pawliuk, Brandi; Zhang, Chunyan; Leung, Mevis; Feist, Lynn
    Study rational and purpose: Web-based education has been proven effective in enhancing knowledge and confidence of health professionals in addressing mental health conditions. However, no web-based training, specific to geriatric depression, exists to date in Canada for educating future health and allied health professionals. The goal of this study was to develop, implement and evaluate a web-based learning module, Depression Assessment Training in Elderly (DATE), to enhance knowledge and confidence in screening for geriatric depression among social work, psychiatric nursing, and nursing students in an undergraduate Canadian university. Design/methodology/approach: This cross-sectional study utilized a set of quantitative surveys of undergraduate students in three different health and mental health disciplines in Canada. Findings: Findings suggest that the DATE module significantly improves confidence of all students in recognizing geriatric depression. Also, it increases clinical knowledge of geriatric depression in social work and psychiatric nursing students. Practical implications: The DATE module is now available for Canadian and international community of clinicians. Further research is needed to test the DATE in a larger sample of Canadian students of social work, psychiatric nursing, and nursing as well as among practicing clinicians. What is original/value of paper: The DATE is the first web-based learning module in Canada that contains clinical simulation case studies on the screening of geriatric depression.