دورية أكاديمية
أعرض في EDS

Species distribution modeling for disease ecology: A multi-scale case study for schistosomiasis host snails in Brazil.

التفاصيل البيبلوغرافية
العنوان: Species distribution modeling for disease ecology: A multi-scale case study for schistosomiasis host snails in Brazil.
المؤلفون: Singleton AL; Emmett Interdisciplinary Program in Environment and Resources, Stanford University, Stanford, California, United States of America., Glidden CK; Department of Biology, Stanford University, Stanford, California, United States of America.; Institute for Human-centered Artificial Intelligence, Stanford University, Stanford, California, United States of America., Chamberlin AJ; Department of Oceans, Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America., Tuan R; Pasteur Institute, São Paulo, Brazil., Palasio RGS; Pasteur Institute, São Paulo, Brazil., Pinter A; Pasteur Institute, São Paulo, Brazil., Caldeira RL; Fiocruz Minas/Belo Horizonte-Minas Gerais, Belo Horizonte, Brazil., Mendonça CLF; Fiocruz Minas/Belo Horizonte-Minas Gerais, Belo Horizonte, Brazil., Carvalho OS; Fiocruz Minas/Belo Horizonte-Minas Gerais, Belo Horizonte, Brazil., Monteiro MV; Geoinformation & Earth Observation Division, National Institute for Space Research (INPE), São Paulo, Brazil., Athni TS; Department of Biology, Stanford University, Stanford, California, United States of America.; Harvard Medical School, Boston, Massachusetts, United States of America., Sokolow SH; Department of Oceans, Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America.; Marine Science Institute, University of California Santa Barbara, Santa Barbara, California, United States of America., Mordecai EA; Department of Biology, Stanford University, Stanford, California, United States of America.; Woods Institute for the Environment, Stanford University, Stanford, California, United States of America., De Leo GA; Department of Oceans, Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America.; Woods Institute for the Environment, Stanford University, Stanford, California, United States of America.
المصدر: PLOS global public health [PLOS Glob Public Health] 2024 Aug 02; Vol. 4 (8), pp. e0002224. Date of Electronic Publication: 2024 Aug 02 (Print Publication: 2024).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Public Library of Science Country of Publication: United States NLM ID: 9918283779606676 Publication Model: eCollection Cited Medium: Internet ISSN: 2767-3375 (Electronic) Linking ISSN: 27673375 NLM ISO Abbreviation: PLOS Glob Public Health Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: San Francisco, California : Public Library of Science, [2021]-
مستخلص: Species distribution models (SDMs) are increasingly popular tools for profiling disease risk in ecology, particularly for infectious diseases of public health importance that include an obligate non-human host in their transmission cycle. SDMs can create high-resolution maps of host distribution across geographical scales, reflecting baseline risk of disease. However, as SDM computational methods have rapidly expanded, there are many outstanding methodological questions. Here we address key questions about SDM application, using schistosomiasis risk in Brazil as a case study. Schistosomiasis is transmitted to humans through contact with the free-living infectious stage of Schistosoma spp. parasites released from freshwater snails, the parasite's obligate intermediate hosts. In this study, we compared snail SDM performance across machine learning (ML) approaches (MaxEnt, Random Forest, and Boosted Regression Trees), geographic extents (national, regional, and state), types of presence data (expert-collected and publicly-available), and snail species (Biomphalaria glabrata, B. straminea, and B. tenagophila). We used high-resolution (1km) climate, hydrology, land-use/land-cover (LULC), and soil property data to describe the snails' ecological niche and evaluated models on multiple criteria. Although all ML approaches produced comparable spatially cross-validated performance metrics, their suitability maps showed major qualitative differences that required validation based on local expert knowledge. Additionally, our findings revealed varying importance of LULC and bioclimatic variables for different snail species at different spatial scales. Finally, we found that models using publicly-available data predicted snail distribution with comparable AUC values to models using expert-collected data. This work serves as an instructional guide to SDM methods that can be applied to a range of vector-borne and zoonotic diseases. In addition, it advances our understanding of the relevant environment and bioclimatic determinants of schistosomiasis risk in Brazil.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Singleton et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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معلومات مُعتمدة: R01 AI102918 United States AI NIAID NIH HHS; R01 AI168097 United States AI NIAID NIH HHS; R35 GM133439 United States GM NIGMS NIH HHS; T32 GM144273 United States GM NIGMS NIH HHS
تواريخ الأحداث: Date Created: 20240802 Latest Revision: 20240923
رمز التحديث: 20240923
مُعرف محوري في PubMed: PMC11296653
DOI: 10.1371/journal.pgph.0002224
PMID: 39093879
قاعدة البيانات: MEDLINE
الوصف
تدمد:2767-3375
DOI:10.1371/journal.pgph.0002224