Spatial analysis of the COVID-19 distribution pattern in São Paulo State, Brazil

At the end of 2019, the outbreak of COVID-19 was reported in Wuhan, China. The outbreak spread quickly to several countries, becoming a public health emergency of international interest. Without a vaccine or antiviral drugs, control measures are necessary to understand the evolution of cases. Here, we report through spatial analysis the spatial pattern of the COVID-19 outbreak. The study site was the State of São Paulo, Brazil, where the first case of the disease was confirmed. We applied the Kernel Density to generate surfaces that indicate where there is higher density of cases and, consequently, greater risk of confirming new cases. The spatial pattern of COVID-19 pandemic could be observed in São Paulo State, in which its metropolitan region standed out with the greatest cases, being classified as a hotspot. In addition, the main highways and airports that connect the capital to the cities with the highest population density were classified as medium density areas by the Kernel Density method.It indicates a gradual expansion from the capital to the interior. Therefore, spatial analyses are fundamental to understand the spread of the virus and its association with other spatial data can be essential to guide control measures. Resumo No final de 2019, o surto de COVID-19 foi relatado em Wuhan, China. O surto se espalhou rapidamente para vários países, tornando-se uma emergência de saúde pública de interesse internacional. Sem uma vacina ou medicamentos antivirais, medidas de controle são necessárias para entender a evolução dos casos. Neste estudo, relatamos por análise espacial o padrão espacial do surto do COVID-19. Nosso local de estudo foi no estado de São Paulo, Brasil, onde o primeiro caso da doença foi confirmado. Aplicamos o método "Kernel Density"para gerar superfícies que indicam onde há maior densidade de casos e, consequentemente, maior risco de confirmação de novos casos. O padrão espacial da pandemia de COVID-19 foi observado no estado de São Paulo, em que a região metropolitana do estado foi a que apresentou a maior quantidade de casos, sendo classificada como um "hot spot". Além disso, as principais rodovias e aeroportos que conectam a capital às cidades com maior densidade populacional foram classificadas como áreas de média densidade pelo método "Kernel Density". Isso indica uma expansão gradual da capital para o interior. Portanto, as análises espaciais são fundamentais para entender a disseminação do vírus e sua associação com outros dados espaciais pode ser essencial para orientar as medidas de controle.

Spatial analysis of the COVID-19 distribution pattern in São Paulo State, Brazil

At the end of 2019, the outbreak of COVID-19 was reported in Wuhan, China. The outbreak spread quickly to several countries, becoming a public health emergency of international interest. Without a vaccine or antiviral drugs, control measures are necessary to understand the evolution of cases. Here, we report through spatial analysis the spatial pattern of the COVID-19 outbreak. The study site was the State of São Paulo, Brazil, where the first case of the disease was confirmed. We applied the Kernel Density to generate surfaces that indicate where there is higher density of cases and, consequently, greater risk of confirming new cases. The spatial pattern of COVID-19 pandemic could be observed in São Paulo State, in which its metropolitan region standed out with the greatest cases, being classified as a hotspot. In addition, the main highways and airports that connect the capital to the cities with the highest population density were classified as medium density areas by the Kernel Density method.It indicates a gradual expansion from the capital to the interior. Therefore, spatial analyses are fundamental to understand the spread of the virus and its association with other spatial data can be essential to guide control measures. Resumo No final de 2019, o surto de COVID-19 foi relatado em Wuhan, China. O surto se espalhou rapidamente para vários países, tornando-se uma emergência de saúde pública de interesse internacional. Sem uma vacina ou medicamentos antivirais, medidas de controle são necessárias para entender a evolução dos casos. Neste estudo, relatamos por análise espacial o padrão espacial do surto do COVID-19. Nosso local de estudo foi no estado de São Paulo, Brasil, onde o primeiro caso da doença foi confirmado. Aplicamos o método "Kernel Density"para gerar superfícies que indicam onde há maior densidade de casos e, consequentemente, maior risco de confirmação de novos casos. O padrão espacial da pandemia de COVID-19 foi observado no estado de São Paulo, em que a região metropolitana do estado foi a que apresentou a maior quantidade de casos, sendo classificada como um "hot spot". Além disso, as principais rodovias e aeroportos que conectam a capital às cidades com maior densidade populacional foram classificadas como áreas de média densidade pelo método "Kernel Density". Isso indica uma expansão gradual da capital para o interior. Portanto, as análises espaciais são fundamentais para entender a disseminação do vírus e sua associação com outros dados espaciais pode ser essencial para orientar as medidas de controle.

Spatial analysis of the COVID-19 distribution pattern in São Paulo State, Brazil.

At the end of 2019, the outbreak of COVID-19 was reported in Wuhan, China. The outbreak spread quickly to several countries, becoming a public health emergency of international interest. Without a vaccine or antiviral drugs, control measures are necessary to understand the evolution of cases. Here, we report through spatial analysis the spatial pattern of the COVID-19 outbreak. The study site was the State of São Paulo, Brazil, where the first case of the disease was confirmed. We applied the Kernel Density to generate surfaces that indicate where there is higher density of cases and, consequently, greater risk of confirming new cases. The spatial pattern of COVID-19 pandemic could be observed in São Paulo State, in which its metropolitan region standed out with the greatest cases, being classified as a hotspot. In addition, the main highways and airports that connect the capital to the cities with the highest population density were classified as medium density areas by the Kernel Density method.It indicates a gradual expansion from the capital to the interior. Therefore, spatial analyses are fundamental to understand the spread of the virus and its association with other spatial data can be essential to guide control measures.