Mapping schistosomiasis risk landscapes and implications for disease control: A case study for low endemic areas in the Middle Paranapanema river basin, São Paulo, Brazil
Year: 2024
Type: article
Abstract: BACKGROUND: Schistosomiasis, a chronic parasitic disease, remains a public health issue in tropical and subtropical regions, especially in low and moderate-income countries lacking assured access to safe water and proper sanitation. A national prevalence survey carried out by the Brazilian Ministry of Health from 2011 to 2015 found a decrease in human infection rates to 1%, with 19 out of 26 states still classified as endemic areas. There is a risk of schistosomiasis reemerging as a public health concern in low-endemic regions. This study proposes an integrated landscape-based approach to aid surveillance and control strategies for schistosomiasis in low-endemic areas. METHODOLOGY/PRINCIPAL FINDINGS: In the Middle Paranapanema river basin, specific landscapes linked to schistosomiasis were identified using a comprehensive methodology. This approach merged remote sensing, environmental, socioeconomic, epidemiological, and malacological data. A team of experts identified ten distinct landscape categories associated with varying levels of schistosomiasis transmission potential. These categories were used to train a supervised classification machine learning algorithm, resulting in a 92.5% overall accuracy and a 6.5% classification error. Evaluation revealed that 74.6% of collected snails from water collections in five key municipalities within the basin belonged to landscape types with higher potential for S. mansoni infection. Landscape connectivity metrics were also analysed. CONCLUSIONS/SIGNIFICANCE: This study highlights the role of integrated landscape-based analyses in informing strategies for eliminating schistosomiasis. The methodology has produced new schistosomiasis risk maps covering the entire basin. The region's low endemicity can be partly explained by the limited connectivity among grouped landscape-units more prone to triggering schistosomiasis transmission. Nevertheless, changes in social, economic, and environmental landscapes, especially those linked to the rising pace of incomplete urbanization processes in the region, have the potential to increase risk of schistosomiasis transmission. This study will help target interventions to bring the region closer to schistosomiasis elimination.
Source: PLoS neglected tropical diseases
Authors: Vivian Alessandra Ferreira da Silva, Milton Kampel, Rafael Silva dos Anjos, Raquel Gardini Sanches Palasio, Maria Isabel Sobral Escada, Roseli Tuan, Alyson Singleton, Caroline Kate Glidden, Andrew Chamberlin, Giulio Alessandro De Leo, Adriano Pinter dos Santos, Antônio Miguel Vieira Monteiro
Language: English
Subfield: Parasitology
Field: Immunology and Microbiology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,670
Funders: National Science Foundation, UK Research and Innovation, Sight Research UK, Fundação de Amparo à Pesquisa do Estado de São Paulo, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Agência Espacial Brasileira, Natural Environment Research Council, Stanford University Center for Innovation in Global Health, Fundação de Apoio à Universidade de São Paulo
Awards: A novel integration of fine scale ecological data, high-resolution precision mapping, and regional network modeling to investigate environmental drivers of schistosomiasis dynamics, G29316538, G295657101, G3339324051, G4217874516, G4892126665, Integrated risk mapping and targeted snail control to support schistosomiasis elimination in Brazil and Côte d'Ivoire under future climate change, Characterization of Brazilian coastal waters biogeochemical quality evolution over the last two decades from satellite observation: impact of natural and anthropogenic forcings, G6438247056, G684092545, Belmont Forum Collaborative Research: Risk mapping and targeted snail control to support schistosomiasis elimination in Brazil and Cote d'Ivoire under future environmental change
OpenAlex ID: W4404035216
