In the realm of COVID-19 transmission data, scientists are scrutinizing policies to identify the ideal vaccination rate for halting the virus. This study aimed to pinpoint the minimal vaccinated percentage needed to break the virus cycle within communities. The underlying motivation stems from the urgent need to contain COVID-19’s spread and reduce the strain on healthcare systems worldwide. With fluctuating infection rates and the emergence of new variants, understanding the optimal vaccination rate has become a cornerstone in public health planning and pandemic response. Using diverse machine learning methods, this study analyzed infection peaks and hospitalization rates during vaccination campaigns across countries. The goal was to find the vaccination threshold necessary to prevent virus resurgence, even with new variants. This critical milestone is crucial for health systems to combat the pandemic effectively. The study’s analysis revealed the correlation between vaccination rates and hospitalizations, highlighting immunization’s pivotal role. Employing Random Forest regression, the study successfully predicted new cases and hospitalization rates, offering valuable insights into pandemic management strategies. For future research, we recommend exploring the impact of vaccination on the evolution of virus variants and the potential influence of socio-economic factors on vaccination uptake. Moreover, a broader analysis across different geographical regions can further validate the study’s findings and enhance global pandemic preparedness.

COVID-19; vaccine; random forest regression; chain of infection; public health policies

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