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Spatial-temporal coupling of malaria vector habitat suitability and biting probability
Grace R Aduvukha1, Elfatih M Abdel-Rahman1, Onisimo Mutanga2
1International Centre of Insect Physiology and Ecology (ICIPE), P. O. Box 30772, Nairobi 00100, Kenya; School of Agriculture and Science, University of KwaZulu-Natal, Pietermaritzburg 3209, South Africa.
Spatial and Spatio-Temporal Epidemiology
|February 19, 2026
Summary
Effective malaria vector control requires understanding mosquito behavior. This study models habitat suitability and biting risk, finding that increased bed net use reduces biting probability, aiding malaria elimination efforts.
Area of Science:
- Environmental science
- Epidemiology
- Vector ecology
Background:
- Malaria vector control is essential for elimination.
- Species distribution modelling (SDM) advances understanding of mosquito behavior and distribution.
- Integrating vector behavior, like biting probability, into SDMs remains a challenge.
Purpose of the Study:
- To model both habitat suitability and biting probability of malaria vectors.
- To assess spatial and temporal biting risk probability using integrated variables.
- To evaluate two rule-based model scenarios for biting risk assessment.
Main Methods:
- Utilized MaxEnt (maximum entropy) SDM with remote sensing, climatic, and topographic data for vector distribution.
- Employed fuzzy logic rule-based techniques incorporating vector presence, human availability, insecticide resistance, and bed net usage for biting risk.
- Evaluated two models (flexible ORs and strict ANDs) for biting risk from 2000-2018, validated with independent biting observations (2017-2021).
Main Results:
- Models achieved a mean accuracy of 91% upon validation.
- Reduced biting probability was observed with increased bed net usage, even under optimal biting conditions.
- Findings correlate with decreased Plasmodium falciparum prevalence due to enhanced intervention measures.
Conclusions:
- Integrating vector presence, ecological factors, human presence, and control methods is crucial for malaria transmission risk assessment.
- The developed models serve as an early warning system for climate and control method usage changes.
- Results are pivotal for optimizing targeted malaria vector management and supporting malaria elimination strategies.
