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Mining COVID-19 data to predict the effect of policies on severity of outbreaks

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dc.contributor.authorEl-Hajj, Mohamad
dc.contributor.authorAnton, Calin
dc.contributor.authorAnton, Cristina
dc.contributor.authorDobosz, Dominic
dc.contributor.authorSmith, Iain
dc.contributor.authorDeiab, Fattima
dc.contributor.authorSaleh, Nagam
dc.date.accessioned2025-01-30T20:58:39Z
dc.date.available2025-01-30T20:58:39Z
dc.date.issued2023
dc.description.abstractDuring 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.
dc.description.urihttps://macewan.primo.exlibrisgroup.com/permalink/01MACEWAN_INST/1mogj0i/cdi_proquest_journals_2916476994
dc.identifier.citationEl-Hajj, M., Anton, C., Anton, C., Dobosz, D., Smith, I., Deiab, F., & Saleh, N. (2023) Mining COVID-19 data to predict the effect of policies on severity of outbreaks. In 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Istanbul, Turkiye, 2023, pp. 4890-4892. https://doi.org/10.1109/BIBM58861.2023.10385665
dc.identifier.doihttps://doi.org/10.1109/BIBM58861.2023.10385665
dc.identifier.urihttps://hdl.handle.net/20.500.14078/3765
dc.language.isoen
dc.rightsAll Rights Reserved
dc.subjectbioinformatics
dc.subjectcorrelation coefficients
dc.subjectCOVID-19
dc.subjectdata mining
dc.subjectpublic policy
dc.subjectvaccines
dc.titleMining COVID-19 data to predict the effect of policies on severity of outbreaksen
dc.typePresentation

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Department of Computer Science