The feasibility of using machine learning to predict COVID-19 cases.
Journal:
International journal of medical informatics
PMID:
39864109
Abstract
BACKGROUND: Coronavirus Disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, emerged as a global health crisis in 2019, resulting in widespread morbidity and mortality. A persistent challenge during the pandemic has been the accuracy of reported epidemic data, particularly in underdeveloped regions with limited access to COVID-19 test kits and healthcare infrastructure. In the post-COVID era, this issue remains crucial. This study introduces a novel approach by leveraging machine learning to predict cases and uncover critical discrepancies, focusing on African regions where reported daily cases per million often deviate significantly from machine learning-predicted cases. These findings strongly suggest widespread underreporting of cases. By identifying these gaps, our research provides valuable insights for future pandemic preparedness, improving epidemic forecasting accuracy, data reliability, and response strategies to mitigate the impact of emerging global health crises.