# Observational causal inference with infectious disease outcomes

> **NIH NIH R35** · BROWN UNIVERSITY · 2024 · $382,852

## Abstract

Project Summary
Infectious disease is a leading cause of global morbidity and mortality. Transmission dynamic models have
played a critical role in guiding interventions related to many infectious pathogens, including HIV, influenza,
SARS-CoV-1, ebolaviruses, SARS-CoV-2, and mpox. Models project how potential interventions (e.g., non-
pharmaceutical measures, therapeutics, and vaccines) may affect disease future transmission. However, they
often rely on small scale studies to project effects, and there have been growing concerns that models may
produce inaccurate, overly optimistic estimates of population-level intervention effectiveness. Observational
causal inference models, which measure intervention effectiveness in real-world settings, could help address
this limitation, but applying these to infectious disease is not straightforward. Observational approaches, such
as difference-in-differences and synthetic control methods, estimate the impact of an intervention based on
empirical counterfactuals: comparing outcomes of interest between treated units or places and similar
untreated units. While well-understood with linear outcomes, they can produce biased and misleading results
in the context of nonlinear transmission dynamics. Even where observational models perform well, it further
remains challenging to transport estimates to new settings to project the impact of future interventions. To
address these issues, this project will develop comprehensive theoretical architecture for synthesizing
transmission dynamic models with observational causal inference models – employing empirical
counterfactuals while accounting for complex biological and population dynamics. In the retrospective
workstream, I will propose robust specifications for observational causal inference models that can estimate
unbiased treatment effects in policy evaluations using infectious disease outcomes. I will also develop model
selection and decision-analytic methods to address potentially significant parameter uncertainty. In the
prospective workstream, I will develop approaches to generalize estimates from observational causal inference
models to new settings using transmission dynamic models and update projected effects in real-time based on
local surveillance indicators. I will illustrate the implications of our methods by re-analyzing prior studies on
COVID-19 as well as applying them to answer new questions about respiratory illness control, in collaboration
with partners in state and local public health institutions. Across both workstreams, I will develop and
disseminate open-source public tools and software to facilitate adoption of these methods. Overall, this work
will produce a suite of methodological innovations to improve understanding of the impact of past policies and
the accuracy of future projections, while also supporting their implementation in public health institutions to
guide planning and priority setting.

## Key facts

- **NIH application ID:** 10941031
- **Project number:** 1R35GM155224-01
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** Alyssa Bilinski
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $382,852
- **Award type:** 1
- **Project period:** 2024-09-15 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10941031

## Citation

> US National Institutes of Health, RePORTER application 10941031, Observational causal inference with infectious disease outcomes (1R35GM155224-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10941031. Licensed CC0.

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