# EpiMoRPH: A simulation environment for generating spatially-refined intervention strategies for the control of infectious disease

> **NIH AI R01** · NORTHERN ARIZONA UNIVERSITY · 2026 · $682,464

## Abstract

Project Summary
The recent SARS-CoV-2 pandemic has highlighted that mathematical modeling of infectious disease is critical
for data-informed decision making. At the same time, however, it has been made clear that the modeling
community does not have appropriately advanced informatics infrastructures that facilitate a rapid consensus
understanding during epidemics and that put the power of modeling in the hands of local public health
stakeholders. This project proposes three integrated elements to transform the workflow of constructing, testing,
and crowd-sourcing spatial epidemiological models to gain deep understanding of epidemics, to provide usable
decision-making tools for local stakeholders, and to propose concrete, locally focused solutions. Our proposal is
to develop a proof-of-concept, collaborative informatics framework for model construction, analysis and
comparison, followed by rigorous optimization of spatial intervention strategies. In Aim 1, we design EpiMoRPH
(Epidemiological Modeling Resources for Public Health), a system that will streamline and automate the
construction and testing of spatial models against benchmark data. EpiMoRPH will support rapid model
comparisons in a community-driven environment to build consensus and to produce a broad understanding of
which modeling approaches are most appropriate in different spatial contexts. Importantly, EpiMoRPH will assist
local public health stakeholders with deciding on the best, community-contributed models that are relevant for
their particular situations and will then implement those best models to make locally customized forecasts. In
Aim 2, we make advances in the automation of spatial and robust optimization algorithms, with the goal of
allowing non-expert users to generate tailor-made intervention strategies relevant to their local municipalities.
Here, we will develop a tool kit of robust optimization algorithms that account for various uncertainties and that
will gradually build upon the fu

## Key facts

- **NIH application ID:** 11374804
- **Project number:** 5R01AI168144-05
- **Recipient organization:** NORTHERN ARIZONA UNIVERSITY
- **Principal Investigator:** Joseph  Mihaljevic
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** AI
- **Fiscal year:** 2026
- **Award amount:** $682,464
- **Award type:** 5
- **Project period:** 2022-04-01T00:00:00 → 2027-03-31T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11374804

## Citation

> US National Institutes of Health, RePORTER application 11374804, EpiMoRPH: A simulation environment for generating spatially-refined intervention strategies for the control of infectious disease (5R01AI168144-05). Retrieved via AI Analytics 2026-05-19 from https://api.ai-analytics.org/grant/nih/11374804. Licensed CC0.

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