Utilizing prospective space-time scan statistics to discover the dynamics of coronavirus disease 2019 clusters in the State of São Paulo, Brazil.

BACKGROUND: The number of deaths and people infected with coronavirus disease 2019 (COVID-19) in Brazil has steadily increased in the first few months of the pandemic. Despite the underreporting of coronavirus cases by government agencies across the country, São Paulo has the highest rate among all Brazilian states. METHODS: To identify the highest-risk municipalities during the initial outbreak, we utilized daily confirmed case data from official reports between February 25 and May 5, 2020, which were aggregated to the municipality level. A prospective space-time scan statistic was conducted to detect active clusters in three different time periods. RESULTS: Our findings suggest that approximately 4.6 times more municipalities belong to a significant space-time cluster with a relative risk (RR) > 1 on May 5, 2020. CONCLUSIONS: Our study demonstrated the applicability of the space-time scan statistic for the detection of emerging clusters of COVID-19. In particular, we identified the clusters and RR of municipalities in the initial months of the pandemic, explaining the spatiotemporal patterns of COVID-19 transmission in the state of São Paulo. These results can be used to improve disease monitoring and facilitate targeted interventions.

Utilizing prospective space-time scan statistics to discover the dynamics of coronavirus disease 2019 clusters in the State of São Paulo, Brazil

ABSTRACT Background: The number of deaths and people infected with coronavirus disease 2019 (COVID-19) in Brazil has steadily increased in the first few months of the pandemic. Despite the underreporting of coronavirus cases by government agencies across the country, São Paulo has the highest rate among all Brazilian states. Methods: To identify the highest-risk municipalities during the initial outbreak, we utilized daily confirmed case data from official reports between February 25 and May 5, 2020, which were aggregated to the municipality level. A prospective space-time scan statistic was conducted to detect active clusters in three different time periods. Results: Our findings suggest that approximately 4.6 times more municipalities belong to a significant space-time cluster with a relative risk (RR) > 1 on May 5, 2020. Conclusions: Our study demonstrated the applicability of the space-time scan statistic for the detection of emerging clusters of COVID-19. In particular, we identified the clusters and RR of municipalities in the initial months of the pandemic, explaining the spatiotemporal patterns of COVID-19 transmission in the state of São Paulo. These results can be used to improve disease monitoring and facilitate targeted interventions.

Spatial modeling applied to environmental monitoring: identifying sources of potentially toxic metals in aquatic system.

Aquatic contamination by potentially toxic metals is a problem that has been aggravated, especially due to the quantity and the diversity of sources. Locating these sources is not always an easy task, especially because of the wide variety of possibilities. In this context, the application of geostatistical methods may represent an excellent tool to find out sources of metal contaminants in aquatic systems. Thus, the objective of this work was to elaborate an approach to identify sources of potentially toxic metals (Zn, Ba, Pb, Cr, Mn and Fe), by relating their spatial-temporal variations with the local land use patterns, along a longitudinal profile of the Pirapora River, located in the State of Sao Paulo, Brazil. For this purpose, water samples were collected at different points, taking into consideration each specific land use pattern and quantifying the metals contents by microwave plasma atomic emission spectrometry. In this work, thirteen land use patterns have been identified: mining, forestry, abandoned pasture, water, urban area, human occupation, floodplain, bare soil, temporary crop, roads, forest, streets and pasture. The results revealed temporal variations for the metals Ba, Cr, Fe, and Pb and spatial for Zn and Mn, making possible to correlate the presence of these two latter metals with mining and forestry, the most proeminent activities in the region. Overall, this work proposes a model which brings together geoprocessing and analytical methods, in order to correlate spatial-temporal variations of potentially toxic metals with specific land use patterns of a determined region, aiming the environmental monitoring.