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Comparing prioritization strategies for delivering indoor residual spray (IRS) implementation, using a network
Sadie J Ryan1,2, Anne C Martin3, Bhavneet Walia4
1Quantitative Disease Ecology and Conservation (QDEC) Lab, Department of Geography, University of Florida, Gainesville, FL, 32611, USA. sjryan@ufl.edu.
Malaria Journal
|September 5, 2020
Summary
Indoor residual spraying (IRS) targeting strategies in Zambia showed that higher malaria risk areas were not necessarily more remote. This research provides a framework for optimizing IRS implementation based on accessibility and risk, not just distance.
Area of Science:
- Vector-borne disease control
- Spatial epidemiology
- Public health resource allocation
Background:
- Indoor residual spraying (IRS) is a key malaria vector control method, complementing insecticide-treated nets.
- Limited funding necessitates balancing malaria risk with the cost of IRS implementation.
- This study developed a framework to model distances for IRS targeting in Zambia.
Purpose of the Study:
- To create a framework for modeling household distances for targeted IRS implementation.
- To apply this framework to prioritize IRS strategies in four Zambian provinces.
- To compare different malaria risk maps for IRS targeting.
Main Methods:
- Utilized optimal network models to calculate travel distances between operations bases and communities.
- Compared network distances with Euclidean distances to highlight the importance of road networks.
- Assessed IRS targeting for 50% of households using malaria risk maps (Plasmodium falciparum parasite rate and vector presence).
Main Results:
- Network route distances for IRS delivery ranged from 0.05 to 115.69 km.
- Euclidean distance inaccurately estimated network routes.
- Prioritization strategies showed minimal overlap, with less than 10% agreement on house inclusion/exclusion.
- Distances to prioritized communities were not statistically different from non-prioritized ones.
Conclusions:
- The distance to targeted communities varied significantly by risk prioritization strategy.
- Higher malaria risk did not correlate with greater remoteness.
- Findings suggest that high-burden malaria areas may not be more remote than lower-burden areas in Zambia.
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