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

Abstract

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.

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“…Importantly, population density and SSR RT-qPCR positivity were directly correlated with spatial death occurrence along the city over the whole analyzed period (17 th and 38 th epidemiologic week), thus, authorities should focus on containing the spread of the disease mainly at these speci c locations. Previous analysis at two states of Brazil, Ceará and São Paulo, also provided evidence that metropolitan highly populated areas showed the greatest number of COVID-19 cases [34,35]. Spread of the virus can be highly associated with the mobility of people by means of transportation, such as planes and buses [35].…”

“…This analysis allowed the comparison of deaths caused by COVID-19 among neighborhoods. To estimate the SRR, the geocoded cases (deaths) were grouped by neighborhood, and indirect standardization [35] was used to compute the expected number of cases for each neighborhood. The SRR then follows as the ratio of the observed number of events (cases and deaths) over the expected number:…”

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