Data-Driven Modeling to Improve Understanding of Human Behavior, Mobility, and Disease Spread

May 2021-Present

Dr. Anderson and team are investigating how the inclusion of how more realistic representations of human behavior can improve spatial agent-based models (ABM) of disease spread. The project focuses on the development of a conceptual data-driven modeling framework that acknowledges the spatial and temporal heterogeneity of human behavior and tightly-couples behavior to mobility, human interaction, and disease dynamics. The modeling framework will be transferrable and scalable so that it can be used to simulate a variety of diseases in different study areas.

The Role of the Urban Built Environment in Breast Cancer Mortality Health Disparities

May-August 2022

Dr. Anderson and team are exploring the relationship between the urban built environment and breast cancer mortality health disparities in the United States. Health disparities are driven by complex and often interrelated factors. The built environment in which an individual is lives, including greenspace, health care providers, housing, walkability, healthy food choices, profoundly affects health and wellness. Despite recent commentaries that urge a better understanding of how

and why the environment matters to an individual’s health, the assessment of built environmental factors in relation to breast cancer survival is relatively unexplored.

RAPID: An Ensemble Approach to Combine Predictions from COVID-19 Simulations

May 2020-Present

Dr. Anderson and team are exploring the use machine learning approaches to combine predictions from a wide range of COVID-19 models, also called components, into what is called an ensemble. Ensemble models produce a single prediction that typically outperforms the predictions derived from its components. Different models produce radically different predictions with varying levels of uncertainty, creating confusion among policy makers and the public about the severity of the situation and how best to respond.