# Genetic and social network analysis to target interventions for malaria elimination

> **NIH NIH K01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2022 · $144,939

## Abstract

PROJECT SUMMARY/ABSTRACT
This proposed K01 award will support the career development of Dr. Jennifer Smith, an Assistant Adjunct
Professor in the Department of Epidemiology and Biostatistics at the University of California, San Francisco
(UCSF). Dr. Smith's career goal is to become an independent researcher with combined expertise in parasite
genotyping and human network analyses to optimize interventions for infectious disease elimination. To
support her career development, this application proposes a study that leverages data collected as part of
ongoing research in malaria high-risk populations and uses novel genetic and social network analyses to
address an urgent challenge preventing achievement of malaria elimination targets. As malaria transmission
declines, an increasingly large proportion of the parasite reservoir is clustered in specific sub-populations with
high exposure to infection and who often face significant barriers to accessing and utilizing malaria
interventions. While normative bodies like the World Health Organization recommend a targeted response in
known malaria high-risk populations, there is limited evidence on the extent to which these populations drive
transmission, the impact of targeted interventions or how to optimize coverage. Through cross-sectional and
temporal analysis of genetic and social network data collected as part of an existing, separately funded
population-based evaluation of targeted malaria interventions in high-risk populations, this K01 proposes to
investigate genetic connectivity between infections in migrant and resident populations and the role social
networks play in uptake of malaria interventions. The specific aims are to (1) quantify parasite genetic
connectivity and transmission potential within and between migrant and resident populations at different time
points and spatial scales, (2) evaluate the influence of social network attributes on uptake of malaria prevention
measures, and (3) model transmission networks and estimate the impact of alternative intervention strategies
in migrant and resident agricultural workers. This study will provide crucial knowledge on how malaria high-risk
populations contribute to transmission dynamics, inform how social networks can be leveraged to improve
intervention uptake, and quantify the impact of targeted interventions on overall transmission. The proposed
research will build on Dr. Smith's foundation in epidemiologic methods and include a 5-year training plan
including mentorship from leaders in genetic and malaria epidemiology, social network analysis and
mathematical modelling at UCSF, University of Southern California and UC Berkeley. Dr. Smith's training goals
are to (1) gain knowledge in malaria genetic epidemiology and applied analytic approaches for genetic data, (2)
develop expertise in advanced social network theory and analytic methods, and (3) obtain training in
mathematical modelling. The findings will be used as a foundation for an R01 t...

## Key facts

- **NIH application ID:** 10434847
- **Project number:** 5K01AI153555-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Jennifer Linnea Smith
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $144,939
- **Award type:** 5
- **Project period:** 2020-07-22 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10434847

## Citation

> US National Institutes of Health, RePORTER application 10434847, Genetic and social network analysis to target interventions for malaria elimination (5K01AI153555-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10434847. Licensed CC0.

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