# Mapping malaria incidence using health facility surveillance data in Uganda

> **NIH NIH F31** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $21,506

## Abstract

PROJECT SUMMARY
 In sub-Saharan Africa, malaria is a major cause of morbidity and mortality with an estimated 200 million
cases and 405,000 deaths in 2017. Effective surveillance of malaria cases is essential for tracking disease
burden, allowing for targeted interventions, and evaluating the effect of control efforts. The current method for
conducting malaria surveillance is through the health management information system (HMIS), which relies on
accurate diagnosis and reporting of cases. Unfortunately, neither are performed consistently, often
compromising the quality of these data. Furthermore, even in the presence of high quality HMIS data,
quantifying an accurate denominator for malaria incidence rates is ambiguous because catchment areas
around health facilities are not well defined. Without measures of malaria incidence over time, high
transmission regions cannot accurately measure malaria morbidity and therefore are unable to effectively
target high risk groups and track progress toward goals.
 The goal of this proposal is to leverage quality health facility surveillance data to generate high
resolution, accurate risk maps of malaria incidence over time in Uganda, the country with the fifth largest
malaria burden globally. Since 2006, high quality individual-level patient data has been collected in 34 sentinel
health facilities across Uganda as part of the Uganda Malaria Surveillance Project, with nearly 100% laboratory
testing of all individuals suspected of having malaria and information on patient geographic residence. The
study will define catchment areas around health facilities to generate a population denominator for incidence
rates around these sentinel sites (Aim 1) and identify environmental, demographic, and intervention predictors
of malaria incidence around the 34 sites over time (Aim 2). This information will be used to build a statistical
model to predict incidence and generate monthly maps of malaria risk in all 60,791 villages across Uganda
(Aim 3). The proposed work will provide valuable insights on the burden of malaria in Uganda, allowing for
long-term evaluation of recent control interventions including mass distributions of insecticide-treated mosquito
nets and indoor residual spraying of insecticides. Knowledge gained from this research will directly advance
the NIAID’s mission of reducing malaria morbidity and mortality with the ultimate goal of eradication. The
proposed training, guided by an excellent mentorship team, will enhance the applicant’s epidemiologic and
statistical methods, research competency, and content expertise needed for her career as a future
independent academic researcher. In particular, the applicant will gain necessary skills in geospatial analyses,
infectious disease epidemiologic methods, and communication of findings to improve both scientific knowledge
and public health practice.

## Key facts

- **NIH application ID:** 10404909
- **Project number:** 5F31AI150029-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Adrienne Epstein
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $21,506
- **Award type:** 5
- **Project period:** 2020-06-01 → 2021-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10404909

## Citation

> US National Institutes of Health, RePORTER application 10404909, Mapping malaria incidence using health facility surveillance data in Uganda (5F31AI150029-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10404909. Licensed CC0.

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