Emerging Infectious Disease: A Computational Multi-agent Model

1. Emerging Infectious Disease: A Computational Multi-agent Model

2. 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

3. 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

4. Multi-agent system examples http://www.comp.hkbu.edu.hk/~aoc/index.php?pid=project http://aser.ornl.gov/research_products.shtml

5. 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

6. 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

7. 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

8. 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)

9. 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

10. 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

11. 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

12. 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

13. Simulation User Interface • Single zone landscape layout

14. Multi-zone landscape layout

15. Simulation Controls

16. 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

17. Simulation experiments and results • Selected Experiments in single zone landscape

18. Simulation experiments and results continue

19. Simulation experiments and results continue • Selected Experiments in dual zone landscape

20. Demonstration

21. 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.