Predicting behavioral influences on epidemic dynamics

Award Period
Award Amount
Agency Name
Oak Ridge Associated Universities
Award Number
PI First Name
PI Last Name
Yun Tao
Kevin Lafferty
MSI People
Area/s of Research
Ecology and Evolution

Most epidemic models (e.g. individual-based simulations, compartmental models) assume that hosts mix uniformly or do not move. However, host movement could easily change during the course of infection, either due to behavioral changes associated with infection, or actions related to control that cause individuals to move towards or away from the ones infected. In the proposed project, I will develop a dynamical disease model based on my Finite-volume Updates of Grid Use Estimates (FUGUE) model to test hypotheses about how behavioral feedback affects outbreak dynamics. New to this model will be a mechanistic description of movement, which I term Transient Epi-Behavior Lattice Equations (TREBLE). Next, I will use advanced statistical tools from the field of movement ecology to analyze human data and expand the model to incorporate empirical observations about movement and epidemics. I will then implement FUGUE and develop management counterpart model to TREBLE, termed Behaviorally Adaptive Spatial Strategies (BASS). I will apply these advances in understanding to model the movement response of healthcare personnel and explore more efficient context-dependent intervention strategies. These FUGUE, TREBLE, and BASS models are unified under a common conceptual and mathematical framework, naturally extending my previous work on population dynamics, movement ecology, and epidemiology. The models I propose here are the next step in moving toward a predictive capacity to contain real-world outbreaks, on real landscapes, with real behavioral responses.