Controlling Epidemics                                                                 

Catherine Dibble, Philip G. Feldman, Donald S. Burke (Johns Hopkins)

Epidemics of infectious diseases such as SARS, smallpox, and periodic killer influenzas such as the 1918 Flu can cause severe harm.  From mortality rates as high as thirty percent, from lost productivity and complications even for those who recover, and especially from social and economic disruption due to panics and economic inactivity. Effective geographic deployments and timely interventions can be crucial for controlling epidemics. 

        

         We have developed a suite of tools for our GeoGraph agent-based computational laboratory that can be used to explore the spatially explicit behavior of any SEIR (Susceptible, Exposed, Infected, or Removed) epidemiological model to study the diffusion of any directly communicable infectious disease among heterogeneous spatially mobile agents, at any scale from hospital corridors to international travel.      

        

         We are able to answer the following questions for any infectious disease affecting any population on any network landscape:

• Given urban population distributions and inter-city transportation networks, which cities are at greatest risk from epidemics of known or of unknown geographic origins?

• Which cities should have priority for public health interventions such as vaccinations or isolation and quarantine enforcements in order to serve as effective epidemiological “firebreaks” to best control regional, national, and international epidemics?

• Which airline flights, train routes, or highways should be blocked or carefully monitored in order to best control the epidemic with the least disruption to transportation services and economic activity?

 

 

Figure Caption: A GeoGraph model of SARS, where individual agents travel between communities and bar charts for each community show the epidemiological status of its population. Green agents are healthy, pink are infected, red are sick, gray are dead, and white are recovered and immune. The model includes super-spreader events and seasonally adjusted infectivity. Gray links are base links, such as highways or trains, yellow links are high-speed shortcuts in the landscape such as airline routes.

 

Example publication:  Catherine Dibble and Philip G. Feldman (2004) “The GeoGraph 3D Computational Laboratory: Network and Terrain Landscapes for RePast” Journal of Artificial Societies and Social Simulation 7(1), jasss.soc.surrey.ac.uk/7/1/7.html.