# Serial Killers to Mosquitos: The Spatial Targeting of Larval habitats in rural Uganda using geographic Profiling

> **NIH NIH K23** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2020 · $189,332

## Abstract

PROJECT SUMMARY/ABSTRACT
The widespread deployment of vector control measures, such as long-lasting insecticidal nets (LLIN), has
resulted in significant declines in the global burden of malaria. Yet, these strategies alone are insufficient to
interrupt transmission and sustain gains. Thus, innovations in vector control are urgently needed. Larval source
management (LSM) is the modification of potential breeding habitats to prevent immature mosquitos from
developing into adults, often through the application of larvicides. LSM is, however, resource intensive and
thereby only recommended in select environments. New methods of efficiently locating and targeting
Anopheles breeding sites must be developed to make LSM feasible in a wider variety of settings. Geographic
profiling (GP) was originally developed as an analytical tool in criminology, using the locations of linked crimes
to narrow the search area for likely suspects. GP has been successfully adopted to a number of biological
problems, but in the case of malaria, it has only been applied to a single retrospective data set. Nevertheless,
the model was able to efficiently identify mosquito breeding sites based on the location of clinical cases. My
long-term career goal is to become an independent investigator with expertise in applied epidemiology and
spatial analysis in order to advance our understanding of the geographic factors that influence malaria
transmission and target interventions in the most effective manner. However, to achieve my career goals and
scientific objectives, I need additional mentorship and training in: (i) geographic information science (GISc), (ii)
entomological surveillance, and (iii) molecular epidemiology, all specifically in the context of malaria control. I
will draw upon this training and leverage the resources of my mentors to achieve my scientific objective, which
is to evaluate the effectiveness of a Bayesian GP model to identify Anopheles breeding sites in a low
transmission, highland area of Western Uganda. My central hypothesis is that GP will facilitate the
identification of Anopheles breeding sites without the need for large scale field operations. I will test this
hypothesis by pursuing three specific research aims: (1) establish the accuracy and efficiency of GP to identify
Anopheles breeding sites in comparison to field-based larval surveillance; (2) demonstrate the benefit of
adding the spatial distribution of adult mosquito densities to the GP model; and (3) evaluate the effect of using
a novel, high-throughput sequencing method to exclude imported malaria cases to improve the accuracy of the
GP model. I am well positioned to achieve these aims given the vast institutional resources available to me
through the University of North Carolina at Chapel Hill and the London School of Hygiene and Tropical
Medicine, along with an internationally-renowned team of mentors and advisors, including Dr. Jonathan
Juliano, Dr. Sarah Staedke, and Dr. Steven Le Combe...

## Key facts

- **NIH application ID:** 9827514
- **Project number:** 5K23AI141764-02
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Ross Mathew Boyce
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $189,332
- **Award type:** 5
- **Project period:** 2018-12-01 → 2023-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9827514

## Citation

> US National Institutes of Health, RePORTER application 9827514, Serial Killers to Mosquitos: The Spatial Targeting of Larval habitats in rural Uganda using geographic Profiling (5K23AI141764-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9827514. Licensed CC0.

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