# Hardening Software for Rule-based models-Competitive Revision

> **NIH NIH R01** · NORTHERN ARIZONA UNIVERSITY · 2021 · $64,243

## Abstract

PROJECT SUMMARY/ABSTRACT
In this competitive revision application, we are proposing to expand the scope of Research Project
2R01GM111510-05 by adding a new sub-aim to Specific Aim 3. As originally formulated, the goal of Aim 3 was
to apply new features of PyBioNetFit (PyBNF) in modeling studies of immunoreceptor signaling. This activity
now becomes Aim 3a. The new sub-aim, Aim 3b, will be focused on data-driven modeling of the effects of vac-
cination and immunity-evading SARS-CoV-2. The modeling of Aim 3b will complement Aims 1 and 2 by driving
improvements of PyBNF that will be broadly useful for epidemiological modelers. Aim 3b addresses a need for
situational awareness, i.e., an ability to monitor for signs of new surges in incidence of severe COVID-19. Aim
3b also addresses a need to monitor for waning of natural and vaccine-induced immunity and emergence of
new strains of SARS-CoV-2 that are capable of evading vaccine-induced immunity. This work will extend our
recently published COVID-19 forecasting efforts in which we used mathematical models for region-specific
COVID-19 epidemics to make accurate short-term predictions of COVID-19 case detection. In this work, we
focused on making predictions for metropolitan areas, which are defined on the basis of socioeconomic coher-
ence. We have found that metropolitan areas are more uniformly impacted by COVID-19 than states. Most
forecasting to date has focused on making state-level predictions vs. predictions for cities and their sur-
rounding metropolitan areas. We plan to extend our existing models to account for vaccination in the 15
most populous metropolitan statistical areas (MSAs) in the United States. After new versions of these region-
specific models are formulated, we will begin to update model parameterizations daily using Bayesian infer-
ence. Daily updates are important for maintaining prediction accuracy and for modifying the models to account
for changes in social-distancing behaviors. Our daily inferences will include quantification of forecast uncertain-
ties, so as to allow for detection of surges and confident rapid responses. The model structure that we are us-
ing as the basis for our forecasts is a deterministic compartmental model that extends the classic SEIR model,
which consists of four ordinary differential equations (ODEs) for the dynamics of susceptible (S), exposed (E),
infected (I), and removed (R) populations. Our extended model accounts for a) the variable time from infection
to onset of symptoms, which is non-exponentially distributed; b) shedding of virus by asymptomatic individuals;
c) mild and severe forms of symptomatic disease; d) quarantine driven by testing and contact tracing; and e)
widespread implementation of time-varying social-distancing measures. Here, we are proposing to extend the
model further to account for vaccination, including vaccines that require booster shots and the time required for
development of vaccine-induced immunity. We will also ...

## Key facts

- **NIH application ID:** 10382135
- **Project number:** 3R01GM111510-06S1
- **Recipient organization:** NORTHERN ARIZONA UNIVERSITY
- **Principal Investigator:** William S Hlavacek
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $64,243
- **Award type:** 3
- **Project period:** 2014-08-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10382135

## Citation

> US National Institutes of Health, RePORTER application 10382135, Hardening Software for Rule-based models-Competitive Revision (3R01GM111510-06S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10382135. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*