Análise espacial e Covid-19: revisão sistemática metodológica e clusters de internações e óbitos na Paraíba - Pinheiro, Rejane Barbosa Ciriaco (2024)
Introduction: The new coronavirus pandemic caught the world by surprise. It’s high infectivity and lethality caused an estimated excess of 4.5 million deaths in its first year. It was necessary to organize new ways of coping with the epidemiological situation and to rethink some tools for analyzing and combating COVID-19, including spatial analysis, as an important strategy for identifying the disease distribution pattern in different territories. Objective: To analyze the spatial distribution of hospitalizations for Severe Acute Respiratory Syndrome (SARS) and deaths from COVID-19 in the state of Paraíba, from 2020 to 2022. Methodology: The study was developed in two parts. In the first part, a systematic review was conducted to identify and search studies that evaluated different methodologies used in the spatial analysis of COVID-19, considering both the number of cases and deaths. The second part involved analyzing data on hospitalizations for SARS and deaths from COVID-19 in Paraíba, assessing their distribution in the state from 2020 to 2022. Results: Seventeen studies were identified that evaluated different methods of spatial analysis. The most commonly used methods were Moran’s Global Index (I) and the Local Indicator of Spatial Association (LISA) (n=9), Kernel density estimation (n=3), and other methods (n=5). Hospitalizations for SARS and deaths from COVID-19 revealed a scenario in which hospitalizations in the first year showed high-high clusters on the coast and in the state capital (Mata Paraibana region). Then, there was an inland spread of the disease with greater intensity in the Sertão Paraibano, with spatial clusters identified in all analyzed periods. COVID-19 deaths exhibited this distribution pattern only in the first year of the pandemic, remaining on the coast and in the capital. In the following years, a random distribution of clusters was observed in both semesters, without a clear pattern of concentration among the analyzed groups. Conclusion: Spatial analysis identified distribution patterns of the new coronavirus, and geoprocessing tools, combined with different methodologies for analyzing the spatial distribution of cases, supported and guided actions in fight against COVID-19.