Download Emerging Infectious Disease: A Computational Multi

Emerging Infectious Disease: A
Computational Multi-agent Model
Agenda
 Multi-agent systems and modeling
 Multi-agent modeling and Epidemiology of infectious
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diseases
Focus of our multi-agent simulation system
Benefits of our system
The architecture of system
Results
Demo
Q&A
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
http://www.comp.hkbu.edu.hk/~aoc/inde
x.php?pid=project
http://aser.ornl.gov/research_products.shtml
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
 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:
 Mimics virus transmission paths in the real world
 Allows for studying patterns in virus epidemiology among
agents based on:
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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 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
 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
 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
 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
 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
 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:
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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
 Selected Experiments in single zone landscape
Infection Distance vs. Never infected agents
Infection Distance (pixels)
9.6
9.4
9.2
9
8.8
Infection Distance vs. Susceptable
Agents at the end of experiment
8.6
8.4
8.2
8
0
20
40
60
80
Never infected agents(%)
100
120
Virulence Duration (time steps)
Virulence Duration vs. Never infected agents
-5
1200
1000
800
600
Virulence Duration vs. Susceptible
Agents at end of experiment
400
200
0
0
5
10
Never infected agents(%)
15
20
Simulation experiments and results
continue
Infection Probability vs. Recovered Agents
Infection Probability (%)
120
100
80
Recovery Probablity 20%
60
Recovery Probability 50%
40
Recovery Probability 75%
Recovery Probability 96%
20
0
0
20
40
60
80
Recovered Agents (%)
100
120
Animal Agents vs. Never infected agents
16
Animal Agents
14
12
10
8
Animal to Human Agent
Transmission
6
4
2
0
0
5
10
Never infected agents (%)
15
20
Simulation experiments and results
continue
Percent of Human agents travel (%)
 Selected Experiments in dual zone landscape
Percent of Human agents traveled vs. Never
infected agents
16
14
12
10
8
6
4
2
0
Percent of traveling Host agents 0%
0
5
10
15
Never infected agents (%)
20
25
Demonstration
References
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[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.
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[2]
Luke, S., Cioffi-Revilla, C., Panait, L., and Sullivan, K. MASON: A New MultiAgent Simulation Toolkit. Department of Computer Science and Center for Social
Complexity, George Mason University.
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[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.
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[4]
Wolfram Math World. Kermack-McKendrick
Model,
http://mathworld.wolfram.com/Kermack-McKendrickModel.html
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[5]
http://en.wikipedia.org/wiki/Multi-agent_system
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[6]
Yergens, D., Hinger, J., Denzinger, J., and Noseworthy. Multi-Agent Simulation
Systems for Rapidly Developing Infectious Disease Models in Developing Countries.