Slide 1

Emerging Infectious Disease: A Computational Multi-agent Model

Agenda

Multi-agent systems and modelingMulti-agent modeling and Epidemiology of infectious diseasesFocus of our multi-agent simulation systemBenefits of our systemThe architecture of systemResultsDemoQ & A

Multi-agent systemsAlso known as Agent-based model (ABM)The system contains agents that are at least partially autonomousNo agent in the system has a full global view of the systemThere is no designated controlling agentAgents are given traits and initial behavior rules that organize their actions and interactions

Multi-agent system examples

http://aser.ornl.gov/research_products.shtmlhttp://www.comp.hkbu.edu.hk/~aoc/index.php?pid=projectAgent-based modeling and Epidemiology of infectious diseases

Multi-agent system help with studying infectious diseasesComputational modeling approach for epidemiological modeling too complex!Agent-based approach can be easily adopted and extendedThe standard SIR model developed by Kermack and McKendrick

Our Multi-agent system

Studies the transmission paths of an infectious disease via:Human to human disease transmissionVector-borne disease transmission

http://www.firstchoiceland.comhttp://www.enotes.com/topic/Infectious_diseaseBenefits of our system:

Mimics virus transmission paths in the real worldAllows for studying patterns in virus epidemiology among agents based on:Number of susceptible and host agentsAgent travel speedInfection distanceInfection probabilityRecovery probabilityVirus incubation durationVirulence durationMultiple or single zone agent interactionAllows for visual virus transmission analysis with real time dataServes as a good education toolCan be extended to handle specific virus transmission

The architecture of our system

The system is designed and implemented with the help of MASON - a single-process discrete-event simulation core and visualization toolkit written in JavaTwo visual components:Virus infection display shows agent interactionControl console allows to setup simulation and adjust all the variable parameters during simulation runThe model is based on the SIR model: N = S(t) + I(t) + R(t)

The agents in our simulation

Our simulation has two kinds of agents:Human agentHost agentThe life of the Human agent is defined by its state transition mechanismThe state of the Host agent is persistent throughout the simulation run

Our agent movement algorithm

Carefully constructed random walk algorithmAvoided pure random walk direction changing that leads to jitteriness The algorithm:An agent picks a random location at time step and achieves itThen an agent repeats the first step over The movement rate is controlled by the rate factor that is set by the user at start of simulation

Interaction among agents

Defined by the set of agents that surround the current agentIf susceptible agent is within the infection distance of an infectious agent, then the host agent infects the susceptible agentThe infection of a susceptible agent is based on the infection probability defined by the userIf a susceptible agent is infected its state starts transition into incubation -> infectious -> recovered/death

Single vs. multiple zone landscapes

The need to adequately model the real world environments Humans have a tendency to move from one area to another:From home to workFrom one city to another and backA virus can be easily transmitted by the traveling agent from one zone into another A virus can also be transmitted by air vector borne virus transmission

Simulation User Interface

Single zone landscape layout

Multi-zone landscape layout

Simulation Controls

Questions to be answered

Examine the effect of pathogen transmissibility on epidemics with following variable parameters:The rate of infection spreadThe infection distanceThe number of pathogen agentsThe number of susceptible agentsSingle vs. dual zone agent travelThe travel rateRecovery ratesExamine the effect of transmission paths based on:Human to human transmission pathAnimal to human transmission path

Simulation experiments and results

Selected Experiments in single zone landscape

Simulation experiments and results continue

Simulation experiments and results continueSelected Experiments in dual zone landscape

Demonstration

References

[1] Roche, B., Guegan, J., and Bousquet, F., 2008. Multi-agent systems in epidemiology: a first step for computational biology in the study of vector-borne disease transmission.

[2]Luke, S., Cioffi-Revilla, C., Panait, L., and Sullivan, K. MASON: A New Multi-Agent Simulation Toolkit. Department of Computer Science and Center for Social Complexity, George Mason University.

[3]Panait, L. Virus Infection simulation. A simulation of intentional virus infection and disinfection in a population. The simulation is part of the sample simulations included in the MASON multi-agent simulation toolkit.

[4]Wolfram Math World. Kermack-McKendrick Model, http://mathworld.wolfram.com/Kermack-McKendrickModel.html

[5] http://en.wikipedia.org/wiki/Multi-agent_system

[6]Yergens, D., Hinger, J., Denzinger, J., and Noseworthy. Multi-Agent Simulation Systems for Rapidly Developing Infectious Disease Models in Developing Countries.