# Spatial Targeting and Adaptive Vector Control for Residual Transmission and Malaria Elimination in Urban African Settings

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2024 · $647,874

## Abstract

Project Summary:
The malaria control program on Bioko Island, Equatorial Guinea was among the vanguard of
highly intensive and highly successful malaria control programs in sub-Saharan Africa. Intensive
malaria control began in 2004 under the Bioko Island Malaria Control Program (BIMEP)
manages commodity distribution, surveillance, monitoring, and evaluation to eliminate malaria
from Bioko Island. After initial success, the program has documented slower progress, and
malaria persists through residual local transmission by vectors and frequent travel to mainland
Equatorial Guinea resulting in malaria importation. There is a significant need to develop a
methodology that would allow BIMEP to improve malaria control through spatial targeting and
rapid development of an evidence base to reduce residual transmission and guide elimination
efforts across transmission contexts. A practical solution, called adaptive vector control, that
combines elements of integrated vector control and adaptive management. The overall goal of
this proposal is to develop adaptive vector control as a rigorous and quantitative methodology to
help programs understand residual transmission, build an evidence base, and identify strategies
to suppress residual transmission and eliminate malaria. The specific goals of adaptive vector
control are to quantify residual transmission in the urban setting of Malabo, Bioko Island the
capital of Equatorial Guinea, where 90% of the residents of Bioko Island live, and use that
evidence to guide vector control through an iterative, structured policy process. We will use
existing evidence from surveillance, monitoring and evaluation to develop, validate, and analyze
dynamic models of mosquito aquatic habitats, mosquito population dynamics, and malaria
transmission in the city. We will use the models to design adaptive sampling and adaptive
studies to reduce uncertainty about programmatic decisions, and through simulation-based
analytics, we will help the program to improve spatial targeting of indoor residual spraying and
larval source management. Finally, we will use the methods to build an evidence base to
support enhanced vector control with novel vector-based interventions to help BIMEP eliminate
malaria. The challenges of reducing malaria incidence in Malabo and on Bioko Island are similar
to the challenges faced elsewhere in sub-Saharan Africa, and adaptive vector control is one
way of addressing the problems of urban vector control in the African context.

## Key facts

- **NIH application ID:** 10834896
- **Project number:** 5R01AI163398-04
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** David L. Smith
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $647,874
- **Award type:** 5
- **Project period:** 2021-06-09 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10834896

## Citation

> US National Institutes of Health, RePORTER application 10834896, Spatial Targeting and Adaptive Vector Control for Residual Transmission and Malaria Elimination in Urban African Settings (5R01AI163398-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10834896. Licensed CC0.

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