# Satellite and geospatial dynamic modeling of malaria risk

> **NIH NIH U19** · DUKE UNIVERSITY · 2020 · $131,355

## Abstract

With the heaviest malaria burden in the region, Myanmar is central to the newly launched campaign
to eliminate malaria from Southeast Asia. Malaria prevalence, incidence, and transmission risk are
both highly heterogeneous and do not fully overlap, posing serious challenges for targeting
elimination interventions. Research that enhances the understanding of space-time malaria risk
dynamics and transmission pathways will improve elimination prospects. Project 3 addresses
Research Area B, Transmission, focusing on the two major transmission pathways: human and
mosquito. A suite of geospatial techniques will be introduced to model vector and parasite
presence/abundance in space and time and establish relationships between human mobility and
malaria transmission at multiple scales. The first aim will establish spatially explicit relationships
between environmental conditions, vector abundance, and malaria burden along a multi-seasonal
temporal gradient to enable the development of a predictive malaria risk system at study sites in
Myanmar and near its borders with China and Bangladesh. This will be accomplished through
applying a combination of field sampling of mosquito abundance at high temporal density, DNA-
based speciation of mosquitos, Plasmodium falciparum and Plasmodium vivax presence/abundance
(from Project 1), satellite-derived environmental parameters, and random forest analytical framework.
Tools will be developed to forecast falciparum and vivax malaria burden as a function of
environmental conditions. The modeling results will be compared with the outcomes of Project 1
serological analyses that quantify the exposure of study participants to site-specific parasite sub-
populations as defined in Project 2 genomic epidemiology studies. Aim 2 will identify spatial drivers
for malaria transmission and the relationships between patterns of human mobility and risk. Spatial
network modeling approaches will be used to study human mobility at the village level based on the
daily activities of individuals as reported through travel histories and traffic analyses. Longer-distance
movements at a regional level, reported through travel histories and other travel data (e.g. rail, water
travel, airways) will be used to assess regional transmission patterns and how populations may
become vulnerable to infection. Human mobility patterns will also be linked to genetic information
from Project 2 about parasite population structure and movement to assess the relationships between
population movements and parasite migration. Together with the Mapping Core, this project will
improve the ability of National Malaria Control Programs to target interventions much more
precisely—and therefore more effectively and efficiently—than is possible with current tools and
approaches.

## Key facts

- **NIH application ID:** 9912077
- **Project number:** 5U19AI129386-04
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Tatiana V Loboda
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $131,355
- **Award type:** 5
- **Project period:** — → 2021-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9912077

## Citation

> US National Institutes of Health, RePORTER application 9912077, Satellite and geospatial dynamic modeling of malaria risk (5U19AI129386-04). Retrieved via AI Analytics 2026-06-14 from https://api.ai-analytics.org/grant/nih/9912077. Licensed CC0.

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