# Measuring spillover effects of reactive, focal malaria elimination interventions

> **NIH NIH K01** · UNIVERSITY OF CALIFORNIA BERKELEY · 2020 · $48,885

## Abstract

PROJECT SUMMARY / ABSTRACT
This proposed K01 award will support the career development of Dr. Jade Benjamin-Chung, an Epidemiologist
in the Division of Epidemiology & Biostatistics at the University of California (UC), Berkeley. Dr. Benjamin-
Chung’s career goal is to become a leader in the application of rigorous biostatistical methods to infectious
disease control and elimination. To support her career development, this application proposes a study she will
lead to fill an important gap in research on malaria elimination interventions. As malaria transmission declines it
becomes more heterogeneous and is characterized by focal hot spots of transmission. Blanket coverage of
interventions becomes impractical and is not cost-effective. Reactive, focal interventions target hot spots by
delivering antimalarials to people residing near to a symptomatic malaria case that presents to a surveillance
site. Focally delivered interventions aim to reduce transmission to those outside focal treatment zones,
including to asymptomatic malaria cases, who are thought to be responsible for the majority of transmission in
elimination settings. Thus, information about whether interventions reduce illness among intervention recipients
(“direct effects”) vs. non-intervention recipients in intervention clusters (i.e., “spillover effects”) is critical to
understanding whether these interventions can eliminate malaria, yet current studies have not estimated such
spillover effects. This study will estimate site-specific and pooled direct effects and spillover effects in three
cluster-randomized trials of reactive, focal malaria elimination interventions in low malaria transmission settings
in Namibia, Swaziland, and Zambia. The specific aims are to (1) estimate direct effects and spillover effects of
reactive, focal malaria elimination interventions on Plasmodium falciparum malaria incidence and prevalence
and (2) assess whether direct effects and spillover effects of reactive, focal malaria elimination interventions
vary by distance to intervention, intervention coverage, and time from incident case detection. Evidence of
spillover effects would suggest that reactive, focal interventions hold promise for malaria elimination when
scaled up. The absence of spillover effects would suggest that interventions did not interrupt transmission; if
so, information about the spatial configuration of infections would inform who and how many people to treat
using redesigned interventions. This study will apply novel machine learning-based methods for estimation of
causal effects appropriate for infectious disease data. This application proposes a 4-year training plan
including mentorship from two leading biostatisticians at UC Berkeley and two malaria epidemiologists at UC
San Francisco. Dr. Benjamin-Chung’s training goals are to (1) develop skills in machine learning and causal
inference methods for dependent data, (2) learn about malaria biology and epidemiology, and (3) enhance h...

## Key facts

- **NIH application ID:** 10133883
- **Project number:** 3K01AI141616-02S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Jade Benjamin-Chung
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $48,885
- **Award type:** 3
- **Project period:** 2019-06-10 → 2021-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10133883

## Citation

> US National Institutes of Health, RePORTER application 10133883, Measuring spillover effects of reactive, focal malaria elimination interventions (3K01AI141616-02S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10133883. Licensed CC0.

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