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ECOLOGICAL NICHE MODELING APPLICATIONS TO INFECTIOUS DISEASES
Shariful Islam1,2, Mariana Castaneda-Guzman1, Diego Soler-Tovar3
1Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA, USA.
Biodiversity Informatics
|April 27, 2026
Summary
Ecological niche modeling (ENM) applications in disease systems lack reporting consistency. Improving transparency in ENM components is crucial for robust spatial epidemiology and accurate disease transmission risk mapping.
Area of Science:
- Ecology
- Epidemiology
- Biogeography
Background:
- Ecological niche modeling (ENM) is vital for predicting species distributions and has been applied to spatial epidemiology.
- Limited research evaluates ENM components and assumptions in disease systems, raising concerns about robustness, reproducibility, and transparency.
Purpose of the Study:
- To systematically review and evaluate articles on ENM applications to infectious diseases between 2020-2022.
- To identify trends and gaps in reporting ENM protocols for mapping disease transmission risk.
Main Methods:
- Conducted a systematic review of 78 articles published between 2020 and 2022 on ENM in infectious diseases.
- Extracted information on ENM components using a standard reporting protocol, including study subject, location, duration, and data selection.
Main Results:
- Significant variation observed in spatial extent (village to global) and temporal duration (1-101 years).
- Key components like temporal autocorrelation tests (2.66%), algorithmic uncertainty (28.21%), and model averaging (20.51%) were infrequently reported.
- Inconsistent reporting of background data selection, coordinate reference systems, and model performance was noted.
Conclusions:
- Findings highlight a lack of consistency and transparency in disease ecology and biogeography studies using ENM.
- Inconsistent reporting may lead to misleading ENM applications in spatial epidemiology.
- Clear reporting of ENM components by researchers and reviewers is essential for biologically sound outputs in disease systems.
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