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

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 x.php?pid=project

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 _disease

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 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: 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

Simulation experiments and results continue

Selected Experiments in dual zone landscape

Demonstration

References [1] Roche, B., Guegan, J., and Bousquet, F., 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, [5] [6]Yergens, D., Hinger, J., Denzinger, J., and Noseworthy. Multi-Agent Simulation Systems for Rapidly Developing Infectious Disease Models in Developing Countries.