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

PLoS Negl Trop Dis. 2024 Nov 4;18(11):e0012582. doi: 10.1371/journal.pntd.0012582. eCollection 2024 Nov.

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.

MeSH terms

  • Animals
  • Brazil / epidemiology
  • Endemic Diseases
  • Humans
  • Prevalence
  • Rivers*
  • Schistosoma mansoni / isolation & purification
  • Schistosomiasis / epidemiology
  • Schistosomiasis / prevention & control
  • Schistosomiasis / transmission
  • Schistosomiasis mansoni / epidemiology
  • Schistosomiasis mansoni / prevention & control
  • Schistosomiasis mansoni / transmission
  • Snails / parasitology

Grants and funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. This study was also funded by the Regular Research Project, Integrated risk mapping and targeted snail control to support schistosomiasis elimination in Brazil and Cote d’Ivoire under future climate change, Awarded n. 2019/23593-3, FAPESP-SP/Brazil Line of Funding associated with the BELMONT FORUM Cooperation Agreements. Field work was partially funded by a seed grant of the Stanford Center for Innovation in Global Health (SPO#231988, GL). VAFS was funded by CAPES-Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Ministry of Education of Brazil (CAPES, Finance Code 001) and the AEB - Brazilian Space Agency, with a Master’s Scholarship for the graduate programme in Remote Sensing and Geoinformation at INPE. MK has been partially funded by grants from UKRI-GCRF 723 (EP/T003820/1), FAPESP (2021/04128-8), and FUSP (2017/00686-0). AP received founding from FAPESP/Belmont Forum, (2019/23593-3). GADL, ALS, CG and AC have been partially supported by US NSF (grant ICER-2024383 and DEB – 2011179) and by a seed grant of the Stanford Center for Innovation in Global Health. AS, CG, AC and GL were partially funded by the Belmont Collaborative Forum for Climate, Environment and Health (2019/23593-3) and the US National Science Foundation (grant ICER-2024383 and DEB – 2011179). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.