# Advancing Methods in Infectious Diseases Models: Incorporating Structural Causes

> **NIH NIH R35** · BOSTON COLLEGE · 2024 · $389,962

## Abstract

PROJECT ABSTRACT
This research program aims to develop novel modeling methods, tools, and guidelines to incorporate
racialized lived experiences into mathematical models of infectious disease transmission by explicitly
modeling structural drivers of racial disparities in infectious disease exposure, susceptibility and severity,
and consequences. In particular, this research will intentionally engage with geographic disparities in the
United States through geographic information systems (GIS) coded data to highlight the importance of
social context and determinants across the life course to the transmission of infectious diseases.
We will employ systems science to analyze in silico simulations and post-hoc data analysis of simulation
output to understand the structural drivers of infectious disease disparities. In silico simulation allows for
the development of synthetic populations that represent individuals and households (and their
characteristics) within a particular geographic area. We plan to modify the model structure to explore the
impact and specificity gained by adding a variety of model characteristics, including stochasticity, natural
history, and environmental influence. We then aim to perform comprehensive sensitivity analyses
accounting for social and political context and the incorporation of multiple interacting factors that may help
identify patterns in spread of particular disease types. Ultimately, the goal of the in silico simulations is to
mathematically link policy effects to health outcomes through racialized lived experiences (represented
and parameterized as agent characteristics). While the modeling frame will be flexible, we will use data on
SARS-CoV-2 and influenza as two examples to demonstrate the feasibility of the methods we develop.
The results from this work will allow us to develop policy recommendations for structural interventions to
reduce racial disparities in infectious disease outcomes. Incorporating structural interventions into the
model structure will require flexibility to account for the interference and feedback with individual behaviors.
The structural interventions we plan to examine using in silico simulations include eliminating residential
segregation, increasing accessibility to stable housing, reducing income inequality, and distribution of
healthy food choices represented by real-world programs across the United States. This research will lay
the groundwork to inform ongoing control of existing and emerging infectious disease pathogens and
prevent the unequal health- and cost-related burdens on communities of color.

## Key facts

- **NIH application ID:** 10893405
- **Project number:** 5R35GM142863-04
- **Recipient organization:** BOSTON COLLEGE
- **Principal Investigator:** Nadia Natasha Abuelezam
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $389,962
- **Award type:** 5
- **Project period:** 2021-08-15 → 2024-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10893405

## Citation

> US National Institutes of Health, RePORTER application 10893405, Advancing Methods in Infectious Diseases Models: Incorporating Structural Causes (5R35GM142863-04). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10893405. Licensed CC0.

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