# Collaborative Research: DMS/NIGMS 1: Identifiability investigation of Multi-scale Models of Infectious Diseases

> **NIH NIH R01** · VIRGINIA POLYTECHNIC INST AND ST UNIV · 2024 · $278,462

## Abstract

The emergence and re-emergence of pathogens and their impact on society has reinforced the need for 
integration and synergy across scientific fields and biological scales in order to advance understanding, 
predicting, and responding to pathogen spread. Multi-scale mathematical models that consider the timing 
and length of individual infections when modeling transmission into the population can aid 
recommendations for optimal interventions. One shortcoming when evaluating data using multi-scale 
models comes from data scarcity in the expansion stages of the infection and transmission, the differences 
in data magnitude and frequency at each scale, together with the complexity of the models considered. To 
determine the source of combined biases in parameter estimation, we will use a combined empirical-theoretical approach for investigating structural and practical parameter identifiability of multi-scale models 
of infectious diseases that may inform optimal experimental design. The proposed research will facilitate a 
better understanding of the sources of uncertainty when fitting multi-scale models to multi-scale infectious 
disease data, with a focus on Usutu and SARS-CoV-2 viruses. By combining empirical and theoretical 
approaches we aim to determine structural and practical parameter identifiability of multi-scale models, to 
inform optimal experimental design, and to improve our ability to make predictions and suggest 
interventions. Our proposal will focus on three major mathematical challenges: (1) Developing methods for 
improving practical identifiability in within-host systems; (2) Use experimental data to inform development of 
transmission models; (3) Build a quantitative framework to predict parameter identifiability in multi-scale 
systems. The overarching goal of the proposed work is to integrate multi-scale mathematical model 
development and statistical models for data fitting with collection of longitudinal virus titers and probability 
of transmission data in order to decrease uncertainty and improve results reproducibility. This will ultimately 
improve our understanding of infection disease transmission and persistence.

## Key facts

- **NIH application ID:** 10935991
- **Project number:** 5R01GM152743-02
- **Recipient organization:** VIRGINIA POLYTECHNIC INST AND ST UNIV
- **Principal Investigator:** Stanca M. Ciupe
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $278,462
- **Award type:** 5
- **Project period:** 2023-09-27 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10935991

## Citation

> US National Institutes of Health, RePORTER application 10935991, Collaborative Research: DMS/NIGMS 1: Identifiability investigation of Multi-scale Models of Infectious Diseases (5R01GM152743-02). Retrieved via AI Analytics 2026-06-04 from https://api.ai-analytics.org/grant/nih/10935991. Licensed CC0.

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