RAPID: Developing Social Differentiation-respecting Disease Transmission Models

In this project, transmission models that account for differences in social networks and exposure opportunities are developed to gain insight into the unequal spread of COVID-19 across populations. Some areas have experienced slow to no spread of COVID-19 while other settings have been overwhelmed. Within high-volume locations, some neighborhoods have been at much greater risk than others. To account for this uneven spread, these models incorporate population differences related to social density and sociodemographic characteristics?features that shape disease exposure and ability to social distance. These models augment general understanding of how social situation affects both disease risk and the cost of disease mitigation efforts, which will allow decisionmakers to evaluate the relative costs of different health-preserving interventions and, potentially, optimize interventions that minimize economic harm while maximizing physical safety.

This project aims to have accurate, flexible and scalable models for disease transmission that can account for observed social differentiation in disease spread. Simulation models are employed to meet this goal, drawing on best estimates from the COVID-19 pandemic for disease-specific infection parameters and rates of transitioning into hospitalization, death, or recovery. Modeling occurs on two levels: Agent-Based Models (ABMs) and small-area cell-based simulation models. ABMs are constructed from social network data and allow for maximum flexibility, being tunable to different types of populations, ranging from rural communities in developing nations to dense urban centers. Small-area (census block group) cell-based simulation models, which translate network structure to interaction probabilities based on demographic and economic similarity profiles, include population differentiation but scale to the national level. These two modeling strategies complement each other and can be used to evaluate different mitigation strategies for both health effectiveness (lives saved) and economic hardship.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.