# Digital data streams and machine learning for real-time modeling of vaccine-preventable infectious diseases

> **NIH NIH R35** · BOSTON CHILDREN'S HOSPITAL · 2022 · $442,337

## Abstract

PROJECT SUMMARY/ABSTRACT
Over the last 30 years, a new field––known as computational epidemiology (comp epi)––has emerged at the
intersection of digital data streams (e.g., news and social media, search query, and mobility data), machine
learning (e.g., nonlinear optimization, natural language processing, and agent-based modeling), and public
health crises. Due to the ongoing COVID-19 pandemic, as well as other vaccine-preventable diseases (e.g.,
measles) that have re-emerged in the United States due to vaccine hesitancy, comp epi has shifted part of its
focus as a field to improving public health decision-making during outbreaks and epidemics of vaccine-
preventable disease. In this proposal, we present four foundational challenges within the context of vaccine-
preventable disease research and comp epi more broadly. While the first three of these challenges are more
conventionally scientific in nature, the fourth involves scientific community-building: (1) estimating the time-
varying transmissibility (i.e., the effective reproduction number, REff) of a given vaccine-preventable infectious
disease; (2) real-time monitoring and measurement of health behaviors that impact disease transmissibility
(e.g., vaccine hesitancy, mobility, etc.); (3) forecasting of vaccine-preventable outbreaks and epidemics as a
function of individual health behaviors; and (4) recruitment of new scholars to the yet-insular field of comp epi.
To address these challenges, we propose the development of (1) a meta-analytical tool for ensemble
estimation of REff across multiple research groups; (2) a surveillance system to monitor vaccine hesitancy and
an inference system to produce more representative measures for human mobility; (3) a generalizable agent-
based model for epidemic forecasting that features behavioral parameters, as informed by the aforementioned
surveillance and inference systems; and (4) a cross-institutional virtual laboratory for comp epi scholars to
collaborate on vaccine-preventable disease research all around the world. By addressing the first three
challenges, we hope to help clinicians and public health policymakers make data-informed decisions during
vaccine-preventable crises while simultaneously providing opportunities for other public health researchers to
augment their own efforts in transmissibility estimation and epidemic forecasting by harnessing expected
products from our proposed research. Meanwhile, by addressing the fourth challenge, we hope to help new
scholars–-particularly those from under-represented backgrounds––form meaningful collaborations both with
pioneers in comp epi and with each other, while simultaneously promoting growth and diversification of the
field as we move forward.

## Key facts

- **NIH application ID:** 10500570
- **Project number:** 1R35GM146974-01
- **Recipient organization:** BOSTON CHILDREN'S HOSPITAL
- **Principal Investigator:** Maimuna Shahnaz Majumder
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $442,337
- **Award type:** 1
- **Project period:** 2022-09-01 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10500570

## Citation

> US National Institutes of Health, RePORTER application 10500570, Digital data streams and machine learning for real-time modeling of vaccine-preventable infectious diseases (1R35GM146974-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10500570. Licensed CC0.

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