# Characterizing dynamics of pandemic and preparing for speedy and accurate response

> **NIH ALLCDC U01** · CHILDREN'S HOSP OF PHILADELPHIA · 2024 · $1

## Abstract

Project Summary/Abstract
Properties of disease transmission can evolve throughout the pandemic and may be influenced by health policy
decisions, regional demographic characteristics, community behaviors, environmental characteristics, and
population immunity. This proposal is motivated by significant challenges we have encountered, including
dynamic connections between virus evolution, health policy, population behavior and degree of immunity over
time, and evolving data elements and data quality due to varying testing criteria and inconsistent reporting
behavior. The overarching goal of this proposal is to develop a framework of pandemic predictive intelligence
that can adapt over time to changing data quality and evolving behavioral and environmental characteristics that
influence disease transmission. The key advantage of the proposed modeling approach is its adaptation to time
varying exposures of community behavior and mobility, environmental conditions, mitigation strategies,
population immunity, and viral evolution. Through three projects, we will develop models to 1) improve the
forecasting accuracy by enhancing model robustness (robustness to data error and model assumptions), 2)
connect the dots between viral evolution and transmissibility, and 3) advance the state-of-the-art forecasting by
integrating five major components, including viral evolution, transmissibility, social behavior, population immunity
and public health policy, to build a learning system for predictive modeling for infectious disease. To ensure the
broader impact of the proposed research, we will develop, validate, and evaluate methodology and software for
pandemic forecasting, real-time monitoring, mitigation, and prevention of the spread of pathogens using national
county/city-level data from the US Department of Health and Human Services, the University of Pennsylvania,
and other publicly available data resources. The proposed work will contribute to foundational work needed to
advance pandemic science, which includes predictive modelling of pandemic and evidence-assisted health
policymaking for pandemic prevention and response.

## Key facts

- **NIH application ID:** 10913961
- **Project number:** 5U01CK000674-03
- **Recipient organization:** CHILDREN'S HOSP OF PHILADELPHIA
- **Principal Investigator:** Jing Huang
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** ALLCDC
- **Fiscal year:** 2024
- **Award amount:** $1
- **Award type:** 5
- **Project period:** 2022-09-30 → 2025-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10913961

## Citation

> US National Institutes of Health, RePORTER application 10913961, Characterizing dynamics of pandemic and preparing for speedy and accurate response (5U01CK000674-03). Retrieved via AI Analytics 2026-06-24 from https://api.ai-analytics.org/grant/nih/10913961. Licensed CC0.

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