Models of infectious disease can inform public health policy decisions. The long-term goal of this research is to create network-based models of disease that become useful tools for public health policy-makers. To this end, we propose research tasks in the following areas: surveillance and detection;dynamics of infectious diseases at several spatio-temporal scales;response strategies;behavior;conceptual development of models;model verification and validation;and dissemination. These tasks will test the hypotheses that: (a) the detailed structure of interaction networks and the effect of disease on that structure have important consequences for public health;and (b) models based on complex, dynamic networks of interacting elements can overcome several significant hurdles to their acceptance and routine use by public health policy-makers. Novel algorithms for efficient simulation and characterization of large (100 million node) networks, along with insight gained from interactions with policy makers, will be used to accomplish the following specific aims: (1) improve our understanding of network-based models of disease;(2) compare models to each other and to available data;(3) extend existing models to new domains;(4) extend the range of questions to which models are applied;(5) improve communications about model outcomes. Each of these aims addresses one of the common hurdles encountered in applying modeling results to public health policy. Public Health Relevance: This research will create network-based models of infectious disease that become useful tools for public health policy-makers. These models will guide the design of targeted interventions that optimize allocation of public health resources to combat the spread of infectious disease.
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