# Predictive Modeling & Optimal Control Framework for Model-Based Epidemic Response in Delaware

> **NIH NIH P20** · UNIVERSITY OF DELAWARE · 2021 · $234,918

## Abstract

PROJECT SUMMARY AND ABSTRACT.
In this project, our team seeks to develop and evaluate a unique predictive modeling approach that can be
applied to the spread of SARS-CoV-2 and subsequently adapted to address other emergent infectious diseases.
Robust and accurate predictive models are needed to allow healthcare and public health experts to devise and
evaluate interventions for controlling viral spread and mitigating the effects of disease. When new disease-caus-
ing viruses arise (such as the recent novel coronavirus SARS-CoV-2), deploying useful predictive models is
challenging. First, the transmission characteristics within often diverse populations are not immediately under-
stood; and second, many existing model frameworks are based on a necessarily simplified set of serially-com-
partmentalized transmissions between susceptible, exposed, infected, and recovered (SEIR) groups that may
not accurately represent the realities of the new virus. In the current proposal, we develop and validate a novel
modeling approach based on principles of chemical reaction kinetics (CRK). The CRK approach allows us to
model the infection/transmission of any virus with the same formalism employed to describe the chemical reac-
tion of one molecule (an infected individual) with another (an uninfected). Our approach also employs “Residence
Time Distribution” theory, which is typically applied to understand large-scale chemical reactors where reagents
move, mix, and interact in a complex manner, to capture elegantly and effectively the uncertainties involved in
complex disease processes, especially those resulting in recovery or mortality. In the long-term, our CRK-based
system will provide a readily-adaptable and facile framework that can be linked to relevant data streams available
through INBRE partner institutions in the state of Delaware, which has a population basis that is broadly repre-
sentative of the nation, to allow rapid use. In addition, the model itself will be accessible to researchers, clinicians,
and public health experts through a convenient online interface. In the long-term, the model and interface will be
vetted and deployed following a detailed Resource Sharing Plan designed to assure usability and impact. In the
initial 12-month study proposed here, we begin development of this system by utilizing existing datasets for
SARS-CoV-2 to deploy a flexible model framework that predicts fundamental aspects of SARS-CoV-2 spread.
In short, a set of ordinary differential equations is developed based on CRK principles where the “reaction rate
constants” directly connect to physiological and epidemiological parameters and where recovery and death are
characterized by directly determinable “Mean-Time-To-Recovery” and “Mean-Time-To-Death” parameters. We
will proceed by addressing two aims: 1) Develop and validate a “Chemical Reaction Kinetics”-based model of
COVID-19 infection for the State of Delaware; 2) Develop Optimal CRK Model-Based Mitigation Strate...

## Key facts

- **NIH application ID:** 10266331
- **Project number:** 3P20GM103446-20S2
- **Recipient organization:** UNIVERSITY OF DELAWARE
- **Principal Investigator:** Steven J. Stanhope
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $234,918
- **Award type:** 3
- **Project period:** 2001-09-30 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10266331

## Citation

> US National Institutes of Health, RePORTER application 10266331, Predictive Modeling & Optimal Control Framework for Model-Based Epidemic Response in Delaware (3P20GM103446-20S2). Retrieved via AI Analytics 2026-06-14 from https://api.ai-analytics.org/grant/nih/10266331. Licensed CC0.

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