# Model-based inference and forecasting of co-circulating pathogen dynamics

> **NIH NIH R35** · UNIVERSITY OF NOTRE DAME · 2021 · $391,250

## Abstract

PROJECT SUMMARY
Public health faces threats from a multitude of pathogens on an ongoing basis, yet pathogens
associated with different diseases are typically compartmentalized with respect to surveillance,
management, and research. This compartmentalized approach ignores the many ways that
pathogens interact, in some cases leading to the exacerbation of their collective burden on
public health. These interactions can be biological (e.g., cross-reactive immunity), behavioral
(e.g., prompting adherence to good hygiene), or clinical (e.g., misdiagnosis). Modern, data-
driven approaches to mathematical modeling have the potential to resolve the dynamics of co-
circulating pathogens by accounting for these interactions. In doing so, modeling also has the
potential to improve pathogen-specific disease forecasts by borrowing information across
surveillance data for different diseases. To date, this potential remains largely untapped. In this
project, I will develop a generalizable framework for modeling the dynamics of co-circulating
pathogens. The first component of this framework will use Bayesian hierarchical modeling to
fuse mechanistic descriptions of pathogen transmission dynamics with statistical descriptions of
surveillance processes, allowing for maximal leveraging of heterogeneous data streams to
inform biological inferences. The second component of this framework will involve validating
model inferences through forecasts of future disease dynamics. Both components of this
framework will involve the use of multiple models that represent competing hypotheses about
pathogen interaction, as well as other forms of model uncertainty. This framework will be
applied in two settings: mosquito-borne viruses in Brazil and respiratory pathogens in Indiana. In
both of these settings, co-circulation of recently emerged and endemic pathogens poses new
challenges for surveillance and control activities, making the development of new modeling tools
to address these challenges especially timely.

## Key facts

- **NIH application ID:** 10276759
- **Project number:** 1R35GM143029-01
- **Recipient organization:** UNIVERSITY OF NOTRE DAME
- **Principal Investigator:** Alex Perkins
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $391,250
- **Award type:** 1
- **Project period:** 2021-09-24 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10276759

## Citation

> US National Institutes of Health, RePORTER application 10276759, Model-based inference and forecasting of co-circulating pathogen dynamics (1R35GM143029-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10276759. Licensed CC0.

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