Integrating Human Behavior, Socio-economic Heterogeneity, Climatic Change, and Epidemiological Models to Forecast Disease Effects and Guide Health Policies

The COVID-19 pandemic underscored the necessity for accurately calibrated models to inform disease control and mitigation strategies, considering factors like human behavior, economics, and the environment. Many existing disease models overlook these complexities, limiting their utility in addressing health and economic trade-offs effectively. This project aims to bridge this gap by developing models that integrate disease dynamics with socio-economic, environmental, and human behavioral factors to guide heterogeneous policies for disease control. Using respiratory and vector-borne diseases as examples and leveraging diverse data sets from the US and Africa, the models will be used to assess the synergistic feedback between disease dynamics, human behavior, the economy, and environmental factors, evaluate the impact of control measures on epidemiological and economic outcomes in different contexts, and to compare the effects of voluntary versus mandatory interventions.

Undergraduate students can contribute in assembling data, data analysis, model development, and model analysis and interpretation. By engaging in this interdisciplinary research, research partners will gain hands-on experience in mathematical modeling and data analysis. They will learn about the interconnectedness of health, adaptive human behavior, and socio-economic factors, enhancing their critical thinking and problem-solving skills.