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

NIH RePORTER · NIH · R01 · $647,874 · view on reporter.nih.gov ↗

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
10425450
Project number
5R01AI163398-02
Recipient
UNIVERSITY OF WASHINGTON
Principal Investigator
David L. Smith
Activity code
R01
Funding institute
NIH
Fiscal year
2022
Award amount
$647,874
Award type
5
Project period
2021-06-09 → 2026-05-31