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Emerging Infectious Disease: A Computational Multi-agent Model. Agenda. Multi-agent systems and modeling Multi-agent modeling and Epidemiology of infectious diseases Focus of our multi-agent simulation system Benefits of our system The architecture of system Results Demo Q & A.

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Presentation Transcript
agenda
Agenda
  • Multi-agent systems and modeling
  • Multi-agent modeling and Epidemiology of infectious diseases
  • Focus of our multi-agent simulation system
  • Benefits of our system
  • The architecture of system
  • Results
  • Demo
  • Q & A
multi agent systems
Multi-agent systems
  • Also known as Agent-based model (ABM)
  • The system contains agents that are at least partially autonomous
  • No agent in the system has a full global view of the system
  • There is no designated controlling agent
  • Agents are given traits and initial behavior rules that organize their actions and interactions
multi agent system examples
Multi-agent system examples

http://www.comp.hkbu.edu.hk/~aoc/index.php?pid=project

http://aser.ornl.gov/research_products.shtml

agent based modeling and epidemiology of infectious diseases
Agent-based modeling and Epidemiology of infectious diseases
  • Multi-agent system help with studying infectious diseases
  • Computational modeling approach for epidemiological modeling – too complex!
  • Agent-based approach – can be easily adopted and extended
  • The standard SIR model developed by Kermack and McKendrick
our multi agent system
Our Multi-agent system
  • Studies the transmission paths of an infectious disease via:
    • Human to human disease transmission
    • Vector-borne disease transmission

http://www.enotes.com/topic/Infectious_disease

http://www.firstchoiceland.com

benefits of our system
Benefits of our system:
  • Mimics virus transmission paths in the real world
  • Allows for studying patterns in virus epidemiology among agents based on:
    • Number of susceptible and host agents
    • Agent travel speed
    • Infection distance
    • Infection probability
    • Recovery probability
    • Virus incubation duration
    • Virulence duration
    • Multiple or single zone agent interaction
  • Allows for visual virus transmission analysis with real time data
  • Serves as a good education tool
  • Can be extended to handle specific virus transmission
the architecture of our system
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 Java
  • Two visual components:
    • Virus infection display – shows agent interaction
    • Control console – allows to setup simulation and adjust all the variable parameters during simulation run
  • The model is based on the SIR model:

N = S(t) + I(t) + R(t)

the agents in our simulation
The agents in our simulation
  • Our simulation has two kinds of agents:
    • Human agent
    • Host agent
  • The life of the Human agent is defined by its state transition mechanism
  • The state of the Host agent is persistent throughout the simulation run
our agent movement algorithm
Our agent movement algorithm
  • Carefully constructed random walk algorithm
  • Avoided pure random walk direction changing that leads to jitteriness
  • The algorithm:
    • An agent picks a random location at time step and achieves it
    • Then 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
Interaction among agents
  • Defined by the set of agents that surround the current agent
  • If susceptible agent is within the infection distance of an infectious agent, then the host agent infects the susceptible agent
  • The infection of a susceptible agent is based on the infection probability defined by the user
  • If a susceptible agent is infected its state starts transition into incubation -> infectious -> recovered/death
single vs multiple zone landscapes
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 work
    • From one city to another and back
  • A 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
Simulation User Interface
  • Single zone landscape layout
questions to be answered
Questions to be answered
  • Examine the effect of pathogen transmissibility on epidemics with following variable parameters:
    • The rate of infection spread
    • The infection distance
    • The number of pathogen agents
    • The number of susceptible agents
    • Single vs. dual zone agent travel
    • The travel rate
    • Recovery rates
  • Examine the effect of transmission paths based on:
    • Human to human transmission path
    • Animal to human transmission path
simulation experiments and results
Simulation experiments and results
  • Selected Experiments in single zone landscape
simulation experiments and results continue1
Simulation experiments and results continue
  • Selected Experiments in dual zone landscape
references
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.