# Malaria Genomic Epidemiology for Identifying Sources of Malaria Infection and Transmission

> **NIH NIH R21** · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · 2020 · $199,375

## Abstract

Abstract: Despite dramatic decreases in malaria deaths over the past decade, recent reports show these
declines are stalling with an increase in clinical cases last year. New strategies are needed to restart progress
toward malaria elimination, with a demand for strategies that detect imported malaria and measure the impact
of newly imported infections on onward transmission. Pathogen sequencing has emerged as a paradigm-
shifting approach to understand infectious disease transmission. Resulting genomic epidemiology strategies
are routine for non-sexually recombining pathogens like Ebola, Zika, Influenza, and methicillin-resistant
Staphylococcus aureus (MRSA), but are not readily applicable to sexually-recombining pathogens like malaria.
Population genetic tools that account for inheritance and recombination open the door for using genomic
sequencing strategies that leverage parasite relatedness to inform malaria transmission networks. The main
question we want to answer is whether genetic data can provide precise information about infection origin and
connectivity (i.e., degree of genetic relatedness) between infections. The goal of this proposal is to develop a
genetic toolkit that can (1) determine if a malaria infection is local or imported, and (2) reveal linkages (e.g.,
shared ancestry) between malaria infections. A ‘proof of concept’ study will test this genetic approach in
northern Senegal, where the few remaining cases are thought to result from imported malaria. Our central
hypothesis is that local and imported malaria infections are genetically distinct, and that infections found close
to each other share genetic ancestry consistent with transmission. The specific aims of the current application
are: 1. To determine whether a malaria infection is local or imported using genetic relatedness metrics. When
malaria prevalence is very low and few cases remain, the choice of an intervention strategy depends critically
on identifying whether infections are imported or the result of local transmission. Genetic methods based on
parasite relatedness will be developed to differentiate local from imported infections. 2. To define infection
linkages using genetic relatedness to infer possible transmission networks. Recent genetic relatedness
between infections can be used to create probabilistic network plots. These networks will be benchmarked
against current epidemiological modeling approaches to validate and assess the value of genetic approaches
for transmission chain identification.

## Key facts

- **NIH application ID:** 9827513
- **Project number:** 5R21AI141843-02
- **Recipient organization:** HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH
- **Principal Investigator:** Sarah Kay Volkman
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $199,375
- **Award type:** 5
- **Project period:** 2018-11-20 → 2022-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9827513

## Citation

> US National Institutes of Health, RePORTER application 9827513, Malaria Genomic Epidemiology for Identifying Sources of Malaria Infection and Transmission (5R21AI141843-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9827513. Licensed CC0.

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