Um sistema de suporte à decisão espacial para determinar o nível de prioridade municipal para o combate à covid-19 no estado da Paraíba

• Autor(a) principal: Pontes, Catarina Ferreira
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFPB
• Texto Completo: https://repositorio.ufpb.br/jspui/handle/123456789/24057
• topic: Infecções por coronavírus; Análise espacial; Análise espaço-temporal; Sistemas de apoio à decisão espacial multicritério; Coronavirus infections; Spatial analysis; Spatio-temporal analysis; Multicriteria spatial decision support systems; CNPQ::CIENCIAS DA SAUDE::SAUDE COLETIVA

The COVID-19 pandemic has been considered one of the most serious public health crises in recent history, interfering with the homeostasis of the world's health organizations and causing economic and social collapse. Thus, this work aimed to characterize the connection between geographic aspects and the involvement by COVID-19 in the state of Paraíba, as well as to build a spatial decision support system to determine the level of priority by county to combat COVID-19, described as follows: “non-priority”, “trend to non-priority”, “trend to priority” and “priority”. It is, therefore, a quantitative, exploratory and ecological study, based on secondary data of COVID-19 cases in the state of Paraíba by county, made available by the Influenza Syndrome Notification System of the Ministry of Health. The study population consisted of all confirmed COVID-19 cases from the 12th epidemiological week of 2020 to the 32nd epidemiological week of 2021. Data analysis followed the steps present in the Multiple Criteria Decision Making architecture: Statistical Analysis, Normality Test, Spatial Incidence Ratio, Spatial Analysis, Spatio- Temporal Analysis, Time of the Spatio-Temporal Cluster, Persistence of the Spatio- Temporal Cluster, Spearman Correlation Coefficient and Fuzzy Rule-Based System. During the research, 373,789 cases of COVID-19 were collected, of which the most affected were: individuals aged 20 to 60 years (75.23%), female biological sex (55.99%) and comorbidities such as heart disease (4.46%). Therefore, with the application of the Spatial Decision Support System based on Weighted Linear Combination, a final decision map was obtained as a product that allowed the detection and spatial visualization by counties according to priority level. It was evidenced that 153 counties were classified as “non-priority”, 51 as “non-priority trend” and 19 as “priority trend”. Based on this result, managers can provide assertive interventions, making decision based on multiple risk factors for COVID-19 in the state of Paraíba.