# Genomic and geospatial analyses of malaria parasite migration to inform elimination

> **NIH NIH R01** · UNIVERSITY OF MARYLAND BALTIMORE · 2020 · $772,516

## Abstract

PROJECT SUMMARY
In response to the emergence of multi-drug-resistant Plasmodium falciparum in the Greater Mekong Subregion,
the World Health Organization is working with local partners to completely eliminate malaria from this geographic
region by 2030. Elimination efforts in the region have led to drastic reductions in the number of malaria cases
and deaths. However, elimination will become increasingly difficult to achieve as the species composition shifts
from P. falciparum to P. vivax (the more difficult species to eliminate), and the malaria burden becomes more
concentrated in border areas, where frequent movement of human populations and mosquito vectors across
borders and the difficulties of conducting surveillance and allocating resources between different countries make
elimination challenging. Local information about factors driving malaria risk will be important for prioritizing
resources and optimizing strategies for malaria elimination, particularly in border areas. Estimates of parasite
migration are important in stratifying malaria risk. Population genomics approaches are beginning to be used to
understand connectivity between parasite populations; however, many of these studies have focused primarily
on regional geographic scales and/or have only used geospatial data to make post hoc geographic
interpretations. Here, we propose an approach that explicitly models the spatial structure in genomic data to
understand parasite migration patterns in an area of emerging drug resistance along the northern border of
Cambodia with Thailand. The work will be accomplished in two aims. First, we will estimate the local population
structure and migration of P. falciparum and P. vivax in an area of dense sampling on either side of the northern
border of Cambodia with Thailand. To achieve this aim, we will generate whole-genome sequence data for P.
falciparum and P. vivax and utilize estimated effective migration surfaces (EEMS) based on rare variation and
identity-by-descent to infer connectivity of P. falciparum and P. vivax populations between different study sites.
Second, we will estimate local human travel patterns and their association with the parasite migration contours
from Aim 1. To achieve this aim, we will develop a model of local travel networks that is spatially and temporally
explicit at the village level and that accounts for key geospatial features in the region that impact human
movement and effective migration. The association between estimated local human travel patterns and parasite
migration patterns will be assessed and will facilitate identification of segments of the travel network that coincide
with regions of high parasite migration that can be used to define geographical units for targeting elimination
interventions. If successful, the proposed research will illuminate the contribution of movement by local
population groups to spatial patterns of parasite migration and will provide a framework to identify specific
geo...

## Key facts

- **NIH application ID:** 9971019
- **Project number:** 1R01AI145852-01A1
- **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE
- **Principal Investigator:** SHANNON Takala Harrison
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $772,516
- **Award type:** 1
- **Project period:** 2020-03-01 → 2025-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9971019

## Citation

> US National Institutes of Health, RePORTER application 9971019, Genomic and geospatial analyses of malaria parasite migration to inform elimination (1R01AI145852-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9971019. Licensed CC0.

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