Análise da difusão tempo-espacial da Covid-19 em dois estados brasileiros

• Ano de defesa: 2024
• Autor(a) principal: Campos, Maria da Luz Góis
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio Grande do Norte; Brasil; UFRN; PROGRAMA DE PÓS-GRADUAÇÃO EM DEMOGRAFIA
• Palavras-chave em Português: Demografia; Coronavírus 2 (Covid-19); Fatores socioeconômicos e demográficos; Modelo Misto de Regressão Geograficamente Ponderada Autorregressivo (MGWRSAR); Sistemas dinâmicos; Modelo epidemiológico comportamental; CNPQ::CIENCIAS SOCIAIS APLICADAS::DEMOGRAFIA
• Link de acesso: https://repositorio.ufrn.br/handle/123456789/60952

The SARS-CoV-2 pandemic has had considerable effects on the global economy and demographics, as well as affecting regions with more vulnerable populations, both socially and in terms of health and sanitary conditions, as discussed in the vast literature. It is in this context that this study has as its main objective to study the flow of the spread of the COVID-19 infection to the states of Rio Grande do Norte (RN) and Amazonas (AM). Thus, using data from the Public Health Department (SESAP) of the state of Rio Grande do Norte and the IBGE – 2010 Census, we analyzed the incidence of COVID-19 for the period of the 1st pandemic wave from March 22, 2020 to November 29, 2020 and cross-sectional data from the epidemiological week SE49 (11/23/2020 to 11/29/2020). Therefore, this is a descriptive, cross-sectional exploratory study, whose empirical basis is to analyze global and local spatial models to model the incidence of COVID-19 in the 167 municipalities of Rio Grande do Norte and its main socioeconomic and demographic determinants. From a methodological point of view, we describe the presence of spatial autocorrelation and cluster formation, using Exploratory Spatial Data Analysis (AEDE). The presence of spatial heterogeneity was observed for the incidence of COVID-19 in the municipalities of RN, which allows adjusting for the indicators of identification of social vulnerability a Mixed Geographically Weighted Spatial Autoregressive Regression Model (MGWRSAR) as the main analysis instrument. From the perspective of the results, it is demonstrated that the coefficients of the study variables presented patterns of associations with the socioeconomic and demographic factors, therefore, the Gini index (Gini) and urban concentration (Gurb) are the most relevant factors to explain the spread of the disease to the state of RN. The results of the correlation clusters of the incidence of COVID-19 support the conclusion that the interregional flows generated by the activities of the larger centers to the smaller ones, through spatial spillovers of the disease, started from the microregions of Mossoró and Western and Eastern Seridó and the urban concentration of Natal-RN. To study the extensive spread of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) in the state of Amazonas, we used data from MonitoraCovid19/Fiocruz and the Amazonas Health Surveillance Foundation (FVS-AM) from March 9, 2020 to April 25, 2021. From a methodological point of view, we used a descriptive experimental and ecological study, using continuous dynamic simulation modeling through dynamic systems, with an emphasis on the behavioral epidemiological model, susceptible, infected, recovered and dead (SIRD-AM), in addition to statistical analysis. This analysis procedure aims to estimate alternative scenarios, based on the social distancing measures imposed by the Amazonas state government, as well as hospital capacity, at the population level, to predict deaths and cases due to COVID-19. From the perspective of the results, we simulated 5 times more deaths for Amazonas; when we consider hospital capacity, we estimate an excess mortality rate 411% higher than the Brazilian rate for 2021, and the flows I(t), R(t) and D(t) decreased at an average rate of 2.5, in favor of pandemic control policies. In particular, this study contributes to the discussion of the incidence of COVID19 based on regional spatial variations at the municipal level for Rio Grande do Norte, as well as produced an epidemiological modeling necessary for public health to understand the population and their needs in terms of health surveillance, when it comes to the COVID19 pandemic, for Amazonas, the epicenter of COVID-19 in Brazil.