Machine Learning Insights into Regional Dynamics and Prevalence of COVID-19 Variants in US Health and Human Services Regions
Lejia Hu,
Xuan Zhang,
Fabian D’Souza
Abstract:Background
The COVID-19 pandemic arising from the emergence of SARS-CoV-2 in late 2019 has led to global devastation with millions of lives lost by January 2024. Despite the WHO's declaration of the end of the global health emergency in May 2023, the virus persists, propelled by mutations. Variants continue to challenge vaccination efforts, underscoring the necessity for ongoing vigilance. This study aimed at contributing to a more data-driven approach to pandemic management by employing random forest regress… Show more
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