Reifications in Disease Ecology 2: Towards a Decolonized Pedagogy Enabling Science by, and for, the People

Chaves, Luis Fernando; Gottdenker, Nicole; Runk, Julie Velásquez; Bergmann, Luke

doi:10.1080/10455752.2022.2152065

Cited by 2 publications

(6 citation statements)

References 71 publications

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“…Other analyses have leveraged expert assessment of model outputs when AUC was unable to clearly rank models by performance (42). This aligns with the well-known but underemployed guideline that remotely-sensed, big data models need to be integrated with local, on-the-ground knowledge to create the best understanding of the system of interest (83).…”

Section: Supplementary Fig S4)mentioning

confidence: 83%

“…Proportion of temporary crop cover was defined-in natural areas-as farming areas where it was not possible to distinguish between pasture and agriculture and-in urban areas-as areas of urban vegetation, including cultivated vegetation, natural forest, and non-forest vegetation (81). We selected these two LULC variables based on our team's on-the-ground knowledge of snail presence (83). In total, we provided our models with 12 environmental covariates (Supplementary Fig S1 ), none of which had pairwise Pearson correlation coefficients above 0.7 with any of the other covariates (36).…”

Section: Environmental Data and Multicollinearity Analysismentioning

confidence: 99%

“…Therefore, in addition to evaluating multiple model performance measures (AUC, sensitivity, specificity, TSS, etc. ), it is crucial to leverage local ecological knowledge to assess the biological realism of each model's predicted suitability map, as well as of the estimated ecological relationships derived from partial dependence plots (83). Other analyses have leveraged expert assessment of model outputs when AUC was unable to clearly rank models by performance (42).…”

Section: Supplementary Fig S4)mentioning

confidence: 99%

See 2 more Smart Citations

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

Singleton

Glidden

Chamberlin

et al. 2023

Preprint

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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—a debilitating parasitic disease of poverty affecting over 200 million people across Africa, Asia, and South America—is transmitted to humans through contact with the free-living infectious stage ofSchistosomaspp. 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. tenagophilaandB. straminea). 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.

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Section: Supplementary Fig S4)mentioning

confidence: 83%

Section: Environmental Data and Multicollinearity Analysismentioning

confidence: 99%

Section: Supplementary Fig S4)mentioning

confidence: 99%

See 1 more Smart Citation

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

Singleton

Glidden

Chamberlin

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, in addition to evaluating multiple model performance measures (AUC, sensitivity, specificity, pAUC, TSS, etc. ), it is crucial to leverage local ecological knowledge to assess the biological realism of each model’s predicted suitability map, as well as of the estimated ecological relationships derived from partial dependence plots [ 93 ]. Other analyses have leveraged expert assessment of model outputs when AUC was unable to clearly rank models by performance [ 51 ].…”

Section: Discussionmentioning

confidence: 99%

“…Proportion of temporary crop cover was defined—in natural areas—as farming areas where it was not possible to distinguish between pasture and agriculture and—in urban areas—as areas of urban vegetation, including cultivated vegetation, natural forest, and non-forest vegetation [ 91 ]. We selected these two LULC variables based on our team’s on-the-ground knowledge of snail presence [ 93 ]. In total, we provided our models with 12 environmental covariates ( Fig A in S1 Text ), none of which had pairwise Pearson correlation coefficients above 0.7 with any of the other covariates [ 44 ].…”

Section: Methodsmentioning

confidence: 99%

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

Singleton,

Glidden,

Chamberlin

et al. 2024

PLOS Glob Public Health

View full text Add to dashboard Cite

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.

show abstract

Reifications in Disease Ecology 2: Towards a Decolonized Pedagogy Enabling Science by, and for, the People

Cited by 2 publications

References 71 publications

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

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

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

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