Understanding the Relationship Between the Russian War in Ukraine and COVID-19 Spread in Canada Using Machine Learning Techniques

The COVID-19 pandemic has caused significant health, social, and economic disruptions globally, exposing healthcare systems' vulnerabilities and disparities in healthcare access and outcomes. The global response to the pandemic has included a variety of measures, including public health interventions, social distancing measures, travel restrictions, and vaccine campaigns. Mathematical and computer modeling has played a crucial role in understanding and combatting the pandemic. The Russian war in Ukraine has caused immense difficulties for medical personnel and severely impacted the accessibility and availability of medical care, disrupting the country's COVID-19 vaccination and prevention efforts. The paper aims to assess the impact of the Russian war in Ukraine on the COVID-19 epidemic process in Canada. We used forecasting methods based on statistical machine learning to build a COVID-19 distribution model. Results showed high accuracy in predicting cumulative new cases and deaths in Canada for 30 days. The model was then applied to the first 30 days of the full-scale Russian invasion to Ukraine, and the study concluded that forced migration of Ukrainians to Canada did not have a significant impact on the epidemic's dynamics. The study's experimental results suggest that the developed model can be used in public health practice.