Geographical information system (GIS) modeling territory receptivity to strengthen entomological surveillance: Anopheles (Nyssorhynchus) case study in Rio de Janeiro State, Brazil.
Hermano Gomes Albuquerque, Paulo Cesar Peiter, Luciano M Toledo, Jeronimo A F Alencar, Paulo C Sabroza, Cristina G Dias, Jefferson P C Santos, Martha C Suárez-Mutis
Author Information
Hermano Gomes Albuquerque: Laboratório de Doenças Parasitárias, Fiocruz, Av. Brasil, 4365 Manguinhos, Rio de Janeiro, RJ, 21040-360, Brazil. hermanofio@gmail.com.
Paulo Cesar Peiter: Laboratório de Doenças Parasitárias, Fiocruz, Av. Brasil, 4365 Manguinhos, Rio de Janeiro, RJ, 21040-360, Brazil.
Luciano M Toledo: Laboratório de Monitoramento Epidemiológico de Grandes Empreendimentos, Fiocruz, Av. Brasil, 4365 Manguinhos, Rio de Janeiro, RJ, 21040-360, Brazil.
Jeronimo A F Alencar: Laboratório de Diptera, Fiocruz, Av. Brasil, 4365 Manguinhos, Rio de Janeiro, RJ, 21040-360, Brazil.
Paulo C Sabroza: Laboratório de Monitoramento Epidemiológico de Grandes Empreendimentos, Fiocruz, Av. Brasil, 4365 Manguinhos, Rio de Janeiro, RJ, 21040-360, Brazil.
Cristina G Dias: Secretaria de Estado de Saúde do Rio de Janeiro, R. México, 128 Centro, Rio de Janeiro, RJ, 20031-142, Brazil.
Jefferson P C Santos: Laboratório de Monitoramento Epidemiológico de Grandes Empreendimentos, Fiocruz, Av. Brasil, 4365 Manguinhos, Rio de Janeiro, RJ, 21040-360, Brazil.
Martha C Suárez-Mutis: Laboratório de Doenças Parasitárias, Fiocruz, Av. Brasil, 4365 Manguinhos, Rio de Janeiro, RJ, 21040-360, Brazil.
BACKGROUND: Extra-Amazonian malaria mortality is 60 times higher than the Amazon malaria mortality. Imported cases correspond to approximately 90% of extra-Amazonian cases. Imported malaria could be a major problem if it occurs in areas with receptivity, because it can favor the occurrence of outbreaks or reintroductions of malaria in those areas. This study aimed to model territorial receptivity for malaria to serve as an entomological surveillance tool in the State of Rio de Janeiro, Brazil. Geomorphology, rainfall, temperature, and vegetation layers were used in the AHP process for the receptivity stratification of Rio de Janeiro State territory. RESULTS: The model predicted five receptivity classes: very low, low, medium, high and very high. The 'very high' class is the most important in the receptivity model, corresponding to areas with optimal environmental and climatological conditions to provide suitable larval habitats for Anopheles (Nyssorhynchus) vectors. This receptivity class covered 497.14 km or 1.18% of the state's area. The 'high' class covered the largest area, 17,557.98 km, or 41.62% of the area of Rio de Janeiro State. CONCLUSIONS: We used freely available databases for modeling the distribution of receptive areas for malaria transmission in the State of Rio de Janeiro. This was a new and low-cost approach to support entomological surveillance efforts. Health workers in 'very high' and 'high' receptivity areas should be prepared to diagnose all febrile individuals and determine the cause of the fever, including malaria. Each malaria case must be treated and epidemiological studies must be conducted to prevent the reintroduction of the disease.
