Interactive Outbreak Simulator: A Robust and Customizable Platform for Socio-Epidemiological Insights

University Of Texas At Austin, Austin TX

Investigators

Abstract

Project Summary / Abstract The long-term objectives of this work are to develop a platform for modeling disease transmission during outbreaks, and to deploy that platform into research laboratories and public health settings across the United States. Through these objectives we will accomplish our goal of providing critical insights into how disease spreads through different population compartments and the effects of various intervention strategies. Stratifying the population into compartments based on risk status or social determinants of health including including social vulnerability index, front line workers, pregnant individuals, children, and the elderly, will help us understand if targeting interventions toward areas of health disparity will have greater effect in slowing the spread of disease. These insights ultimately will give researchers and public health experts the tools to make real time decisions and better forecast uncertain outcomes when data is limited. The work proposed here builds off a previous collaboration and a prototype platform. The proposed work is divided into three Specific Aims. In Aim #1, we will transform and re-architect the front and back ends of the platform to standardize the data model to improve efficiency and compatibility with established visualization libraries, enable modeling of all US states at a range of scales – including statewide, metro areas, and cities, by county, zip code, or census tract, and add support for simulation concurrency and checkpointing, driving scalability and robustness. In Aim #2 we will write new functions to ingest additional data sources, including social vulnerability index and others, develop a toolkit to help other researchers augment the existing models with custom disease models, travel models, and intervention strategy models, and expand on the existing unit and functional tests to increase code coverage and improve documentation for future contributors. Finally, in Aim #3, we will leverage our existing network of epidemiological researchers and public health experts in order to perform hands-on testing, gather feedback, and refine the platform interface and functionality, and optimize the deployment strategies for single-user and multi-user environments in real-world settings to ensure reproducibility and foster a community-driven approach.