Comparative Analysis of Machine Learning Models for Covid-19 Forecasting
Keywords:
vaccination, performance, Public safety, Analytical Hierarchy Process (AHP), Pfizer-BioNTechAbstract
The development and approval of several very effective COVID-19 vaccines for mass vaccination. We examined how immunisation has an impact on the COVID-19 pandemic and disease outcomes in Ontario, Canada. An agent-based transmission model was parameterized using the features of the COVID-19, the population of Ontario, and age-specific clinical outcomes. We carried out a two-dose vaccination schedule in line with accepted methods utilised in clinical studies for the Pfizer-BioNTech and Moderna vaccines. placing the requirements of medical professionals, sufferers with comorbid diseases, and individuals older than 66 years of age first. The daily vaccination rate was parameterized in accordance with information on vaccine administration. We used estimates of vaccine efficiency to forecast the impact of immunisation on the overall attack rate, hospitalisations, and death. COVID-19 has harmed the workforce and disrupted the lives of billions of people throughout the world. Due to the disease's spread, most nations have also enacted strict lockdown regulations and shut down the majority of their industries. This study aimed to highlight the significant challenges of energy project performance along with the construction activities that had to be halted during the COVID-19 pandemic in order to follow societal norms. Public safety, separation, and lockdown borders. To accomplish the objective, a questionnaire survey was the Goal of this investigation. Responses from 45 energy project professionals and experts were evaluated using the Analytical Hierarchy Process (AHP) is a method for collective decision-making.
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