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Spatiotemporal Distribution of Leishmaniasis in an Endemic Area of Northeast Brazil: Implications for Intervention Actions.

Antonia Suely Guimarães-E-SilvaRomário de Sousa OliveiraKaren Brayner Andrade PimentelRosa Cristina Ribeiro da SilvaBruno Leite RodriguesJosé Manuel Macário RebêloValéria Cristina Soares Pinheiro
Published in: Journal of medical entomology (2022)
This study aimed to analyze the spatiotemporal distribution of leishmaniases, and contribute to the knowledge of their epidemiological dynamics from 2007 to 2017 in the municipality of Caxias, Maranhão, Northeast Brazil. Data on American tegumentary leishmaniasis (ATL) and human visceral leishmaniasis (HVL) were obtained in the Epidemiological Surveillance Sector of Caxias, while data on canine visceral leishmaniasis (CVL) were obtained in the Zoonoses Surveillance Unit. For data analysis and spatial representation of leishmaniasis cases, the geoprocessing of the data was performed, and the geometric features of the state of Maranhão, Caxias, and the disease registration sites were obtained from the shapefile database of the Brazilian Institute of Geography and Statistics. Geostatistics was used to create maps based on the Kernel density method, starting from the points, producing a raster file for each case with several data frames, allowing the instantaneous comparison of the phenomena. During the study period, ATL, HVL, and CVL were reported in Caxias, accounting for 114,304 and 8,498 cases, respectively. The geoprocessing analysis showed that leishmaniasis is widely distributed in the urban area of Caxias. However, there are risk areas for the transmission of these diseases to humans and dogs, associated with deforestation and urban expansion, and may vary over time. Preventive measures must focus on risk areas, including conservation efforts and urban planning, in order to reduce the transmission of leishmaniases.
Keyphrases
  • data analysis
  • electronic health record
  • big data
  • public health
  • healthcare
  • endothelial cells
  • randomized controlled trial
  • artificial intelligence