# Causal Inference in Infectious Disease Prevention Studies

> **NIH NIH R01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2020 · $341,300

## Abstract

Summary
 The overall objective of this research is to develop statistical methods for quantifying the effects of interventions
to prevent infectious diseases. The primary motivating examples for this research are studies of vaccines, although
the developed methods will be general and have immediate application in other settings. One particularly significant
and challenging problem in vaccine studies entails assessing indirect (spillover) effects of vaccination. For vaccines
that are costly or do not afford complete protection from disease when an individual is vaccinated, evaluating indirect
effects (or herd immunity) is important in policy considerations about vaccine introduction and utilization. Failure to
account for herd immunity can lead to incorrect conclusions regarding the public health benefit of a vaccine. Drawing
inference about herd immunity is non-standard because indirect effects measure the effect of vaccinating one
individual on another individual's health outcome. In the nomenclature of causal inference, this is known as
“interference.” That is, interference is said to be present if the treatment (e.g., vaccination) of one individual affects
the outcome of another individual. In this grant innovative statistical methods will be developed for drawing inference
about the effects of a treatment or exposure when there is possibly interference between individuals. For each of
the project's aims, the theoretical properties of the proposed statistical methods will be established. Simulation
studies will be conducted to evaluate the performance of the proposed methods over a wide range of realistic
settings. The developed methods will be used to analyze data from several large infectious disease prevention
studies, providing new insights into the different effects of vaccines for cholera, influenza, and other pathogens, and
malaria bed nets. The resulting inferences will have straightforward interpretations in terms of the expected number
of infections or cases of disease averted due to the intervention. User-friendly software implementing the proposed
methods will be developed and made freely available. The statistical methods and software developed will be
applicable to many other settings where interference may be present, including econometrics, education, network
analysis, political science, and spatial analyses.

## Key facts

- **NIH application ID:** 10050672
- **Project number:** 2R01AI085073-10
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Michael G Hudgens
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $341,300
- **Award type:** 2
- **Project period:** 2009-12-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10050672

## Citation

> US National Institutes of Health, RePORTER application 10050672, Causal Inference in Infectious Disease Prevention Studies (2R01AI085073-10). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10050672. Licensed CC0.

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