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Ecology of Malaria Mortality: A Spatiotemporal Mapping Approach
Amanda Cooke1, Megan B Brickley1
1Department of Anthropology, McMaster University, Hamilton, Ontario, Canada.
American Journal of Biological Anthropology
|June 5, 2026
Summary
Malaria risk in 19th-century Ontario shifted from wetlands to human settlements due to colonial expansion and landscape changes. Settlement proximity, not wetlands, predicted malaria mortality density over time.
Area of Science:
- Paleopathology
- Historical Epidemiology
- Environmental History
Background:
- 19th-century Ontario experienced significant settlement expansion and landscape transformation.
- Understanding the spatiotemporal dynamics of disease ecology is crucial in historical contexts.
- Settler-colonial practices profoundly impacted disease transmission patterns.
Purpose of the Study:
- To examine the spatiotemporal ecology of probable malaria mortality in 19th-century southern Ontario.
- To evaluate how settlement, landscape change, and infrastructure influenced malaria transmission risk.
- To analyze the role of environmental and anthropogenic factors in structuring disease ecology.
Main Methods:
- Geographic Information Systems (GIS) analysis of geocoded malaria-attributed deaths (1869-1900).
- Spatial analysis of mortality in relation to historical settlements, railways, and wetlands.
- Statistical modeling (negative binomial regression, generalized additive models) to identify predictors of malaria mortality density.
Main Results:
- Malaria mortality was spatially clustered but became less concentrated over time.
- Mortality locations shifted away from wetlands as drainage increased.
- Settlement proximity was the primary predictor of malaria mortality density, independent of wetland proximity.
Conclusions:
- Malaria risk reorganized from environmental ecologies to anthropogenic landscapes shaped by colonial activities.
- Disease transmission was embedded in settlement practices rather than static environmental features.
- Spatiotemporal GIS approaches are valuable for interpreting past disease ecologies.
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