The Global Epidemic Model (GEM) uses airline travel data and population data from 2000 to create a realistic air travel network among the major metropolitan areas of the world. The purpose of GEM is to model the time course of a possible epidemic as it spreads across the globe from an initial small outbreak in one city. GEM also allows for testing of various disease intervention strategies. 

GEM calculates the daily worldwide number of persons in each disease state (i.e., susceptible, exposed, infectious, and recovered [SEIR]). In addition, the model calculates and displays the strength of the epidemic in all airport metropolitan areas. 

GEM is currently configured with parameters estimated to describe a hypothetical avian influenza A (H5N1) outbreak. 

GEM Applet

The Global Epidemic Model applet can be run on any platform that supports Java. Users will need to install the Java Runtime Environment version 1.4.2 and up that can be downloaded from the Oracle Java site.

View Global Epidemic Model Applet




In addition to the above Java Applet, a functionally equivalent versions of GEM written in AnyLogic is available:

AnyLogic GEM: This version of GEM is a stochastic, equation-based, and SEIR epidemic model. For more technical details, download the GEM factsheet and the AnyLogic GEM User's Manual. The fully functional model is available on request by contacting the MIDAS Web site administrator, either as a set of Java files or as the AnyLogic source code. The Java code can be run on any Java-enabled computer platform. The source code currently available was written using AnyLogicTM version 5.5, and is not compatible with AnyLogicTM version 6.x or later.

We encourage your comments so we can improve future versions

The Global Epidemic Model is based on research published in:

Epstein JM, Goedecke DM, Yu F, Morris RJ, Wagener DK, et al. (2007)
Controlling Pandemic Flu: The Value of International Air Travel Restrictions.
PLoS ONE 2(5): e401. doi:10.1371/journal.pone.0000401


Planning for a possible influenza pandemic is an extremely high priority, as social and economic effects of an unmitigated pandemic would be devastating. Mathematical models can be used to explore different scenarios and provide insight into potential costs, benefits, and effectiveness of prevention and control strategies under consideration. Methods and Findings. A stochastic, equation-based epidemic model is used to study global transmission of pandemic flu, including the effects of travel restrictions and vaccination. Economic costs of intervention are also considered. The distribution of First Passage Times (FPT) to the United States and the numbers of infected persons in metropolitan areas worldwide are studied assuming various times and locations of the initial outbreak. International air travel restrictions alone provide a small delay in FPT to the U.S. When other containment measures are applied at the source in conjunction with travel restrictions, delays could be much longer. If in addition, control measures are instituted worldwide, there is a significant reduction in cases worldwide and specifically in the U.S. However, if travel restrictions are not combined with other measures, local epidemic severity may increase, because restriction-induced delays can push local outbreaks into high epidemic season. The per annum cost to the U.S. economy of international and major domestic air passenger travel restrictions is minimal: on the order of 0.8% of Gross National Product. Conclusions. International air travel restrictions may provide a small but important delay in the spread of a pandemic, especially if other disease control measures are implemented during the afforded time. However, if other measures are not instituted, delays may worsen regional epidemics by pushing the outbreak into high epidemic season. This important interaction between policy and seasonality is only evident with a global-scale model. Since the benefit of travel restrictions can be substantial while their costs are minimal, dismissal of travel restrictions as an aid in dealing with a global pandemic seems premature.

PubMed Link:
PMID: 17476323