Association Rule Extraction in Data Related to COVID-19 Vaccination in the State of Acre
Resumo
Context: This research was motivated by the need to deepen understanding of the effects of vaccination against Severe Acute Respiratory Syndrome (SARS) related to COVID-19. Problem: Some doubts still remain about the efficacy of COVID-19 vaccines, considering the occurrence of cases during the period of the main variants, age group, and interval between the first and second doses in the diagnosis of COVID-19. Solution: This paper uses data mining techniques to identify the relationships between COVID-19 vaccination and the reduction of positive cases of the disease in the state of Acre. IS Theory: The work used General Systems Theory in knowledge discovery in databases (KDD), applying it to the research on COVID-19 vaccination in the state of Acre, treating the research as an information system that collects, processes, and analyzes data. Method:This research adopts a prescriptive approach, using an experimental study that uses the Apriori association rule extraction algorithm. The analysis of the results is done in a quantitative way. Summary of Results: Vaccination is associated with a reduction in COVID-19 cases (25%), especially in relation to the main variants, from 2022, with more noticeable effects in the elderly population (23%). In addition, an interval of 4 weeks between the first and second doses of vaccination is related to a significant reduction (35%) in the positive diagnosis of COVID-19. Contributions: The article contributes to enable enriched and linked open data, from the open data portal (dados.gov.br), related to events associated with COVID-19 vaccination.
