Search the Health Library
Get the facts on diseases, conditions, tests and procedures.
I Want To...
Find a Doctor
Find a doctor at The Johns Hopkins Hospital, Johns Hopkins Bayview Medical Center or Johns Hopkins Community Physicians.
I Want To...
Find Research Faculty
Enter the last name, specialty or keyword for your search below.
Behavioral Epidemiology: Applications of Agent-Based Modeling to Infectious Disease
NIH Director’s Pioneer Award.
Under this Award, Epstein has developed a new theoretical entity: Agent_Zero. This software individual, or "agent," is endowed with distinct emotional/affective, cognitive/deliberative, and social modules. Grounded in contemporary neuroscience, these internal components interact to generate observed, often far-from-rational, individual behavior of central relevance to epidemic and public health dynamics. When multiple agents of this new type move and interact spatially, they collectively generate fundamental dynamics spanning the fields of social conflict, psychology, public health, law, network science, and economics. This is elaborated in his forthcoming book, Agent_Zero: Toward Neurocognitive Foundations for Generative Social Science Princeton University Press (2013), http://press.princeton.edu/catalogs/series/psc.html.
Pioneer-funded collaborations with The Johns Hopkins School of Engineering will place Agent_Zero populations in a range of extreme settings beyond infectious diseases, including airborne toxic plumes, hurricanes, and earthquakes. This completely new synthesis of engineering and human behavioral modeling will advance the basic science of human crisis behavior, and contribute directly to catastrophic event preparedness. We are also scaling Agent_Zero to the literally planetary level, as well as deepening the agents neurally, with the Johns Hopkins Institute for Computational Medicine.
Models of Infectious Disease Agents Study (MIDAS): Computational Models of Infectious Disease Threats II
The major goals of MIDAS are to develop advanced computational models of infectious diseases including pandemic influenza, MRSA, and other novel pathogens and, using these, to develop novel strategies for epidemic containment, and the minimization of morbidity and mortality in the event of outbreaks.
Developed under MIDAS, CAM’s Large-Scale Agent Model (LSAM) is a high-fidelity US model including a 300 million agent virtual population calibrated to Census data, and including all travel among the country’s 30 thousand zip codes. Unprecedented in speed, the LSAM is in fact a sub-model of the Center’s Global-Scale Agent Model (GSAM), the only model of its kind--simulating disease spread in a population of 6.5 billion individuals on a map of the planet. Epstein has lead MIDAS’s efforts in behavioral modeling, and CAM is leading the development of new methods for mining very large social media data sets (e.g., Twitter) to project both disease incidence and attitudes toward pharmaceutical and non-pharmaceutical mitigation measures.
Modeling and Simulation of Complex Social Dynamics for Catastrophic Event Preparedness and Response
The National Center for the Study of Preparedness and Catastrophic Event Response (PACER)
Johns Hopkins University Department of Emergency Medicine
Department of Homeland Security
This University Center of Excellence for the study of Preparedness and Catastrophic Event Response (PACER) to leads a consortium studying how best to prepare for large-scale disasters. CAM has built the first models combining computational fluid dynamics and agents in high fidelity 3D representations of toxic plume scenarios in major cities, such as Los Angeles. These tools offer real-time decision support in emergencies by integrating and assimilating multiple types of information, processing that information, and presenting it in a manner useful to decision makers. We are developing user-friendly interactive versions of our national-scale models for real-time projections (“now-casting”) of infectious disease dynamics and interventions, and for teaching at all levels.
Ultrahigh-throughput Virus-Host cell PicoReactor System for Predictive Modeling of Viral Evolution
Epidemiological Models of Viral Evolution
The major goal of this project is to develop computational models for predicting viral evolution under selection pressure from multiple evolutionary stressors. Modeling work is both theoretical and empirically-driven, the latter utilizing nucleic acid sequences and other data developed by a Harvard-Johns Hopkins Medicine and Applied Physics Lab (APL) team employing state-of-the-art ultrahigh-throughput virus-host cell PicoReactor Systems based at Harvard and APL. The project is developing the first models linking viral evolution and human behavior in large-scale epidemic simulations. We are calibrating the models of transmission and phylodynamics to data from the 2009 H1N1 pandemic, and are also modeling the dynamics of norovirus.