Models are central in important areas of public health, including pandemic flu, chronic disease, and disaster preparedness. However, behavioral factors are virtually ignored in current modeling, a crucial defect. Behavior can fundamentally shape the spread of infectious diseases (such as influenza). People may flout, or not know, government containment directives. Distrustful communities may refuse vaccine. Rather than self-isolate, exposed individuals may flee in fear, accelerating the spatial spread of disease. Epidemic modeling to date has virtually ignored these behavioral adaptations and their consequences. Behavior also shapes chronic disease outcomes-yet no behavioral mechanism is currently offered to explain, for example, the striking historical dynamics of obesity. Theoretical and empirical work could identify such mechanisms, including peer effects. Behavior would also shape health outcomes in a disaster. In an urban toxic plume release, the natural impulse to flee could amplify congestion, undermining evacuation, and increasing exposure. Are there simple decentralized rules or tailored messages that could, instead, generate efficient evacuation? Behavior under stress may be based on poor information, unwarranted fear, or entrenched social norms. These possibilities should condition risk communication and the design of containment/preventive strategies across the spectrum of public health threats. At present, they do not. A central goal of this research, then, is to model boundedly rational endogenous behavioral adaptations and their feedback effects on spatio-temporal disease dynamics. The Project covers both the epidemiology of behaviors (such as panic, non-compliance, distrust) and the role of behavioral factors in the progress of infectious and chronic diseases. The core analytical technique will be agent-based modeling by multi- disciplinary research teams. The PI is a recognized pioneer in this innovative field, and has directed successful multi-disciplinary agent modeling projects on Smallpox, Archaeology, Economics, and Civil Violence, as recounted in his book, Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton, 2006).

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
NIH Director’s Pioneer Award (NDPA) (DP1)
Project #
8DP1GM105382-05
Application #
8307825
Study Section
Special Emphasis Panel (ZGM1-NDPA-B (P2))
Program Officer
Eckstrand, Irene A
Project Start
2008-09-30
Project End
2014-07-31
Budget Start
2013-08-01
Budget End
2014-07-31
Support Year
5
Fiscal Year
2013
Total Cost
$787,446
Indirect Cost
$307,296
Name
Johns Hopkins University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21218