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Multi-Layer Agent-Based Simulation Tool March 14, 2008

Multi-Layer Agent-Based Simulation Tool March 14, 2008. MLAB-ST. J. Patrick Vandersluis, Ph.D. HealthRx Corporation. Goals. Set context for our specific work with a primer on agent-based simulation

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Multi-Layer Agent-Based Simulation Tool March 14, 2008

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  1. Multi-Layer Agent-Based Simulation ToolMarch 14, 2008 MLAB-ST J. Patrick Vandersluis, Ph.D.HealthRx Corporation

  2. Goals • Set context for our specific work with a primer on agent-based simulation • Show how MLAB-ST uniquely implements AB simulation fundamentals with the notion of constraint maps • Present MLAB-ST agents designed for this work

  3. MLAB-ST is an Agent-Based Tool • Three things required of an agent-based tool: • An environment • One or more agents • A simulation scenario with rules and parameters

  4. Environment • Acts as container for simulation • Usually has a specific physical space or geography • Usually has a defined time span • Always has ability to manage messages across scope • Manages a set of defined events that have global scope and are fired on some schedule from • once at beginning or end of simulation • or once every time increment • Our environment is the three regions of Boston, Peterborough, and Tyngsborough • Our time span is 18 months beginning April 1st

  5. Agent • An entity that interacts with its environment and other agents based on rules and constraints • The notion of an agent is based in biological principals • Agent is created (instantiated) from some base class • Agent has properties or attributes • Height, weight, age, lifespan, etc. • Agent has methods or behaviors • Mobility, reaction to other agents, reaction to environment, etc.

  6. Simulation Scenario • Environmental settings for run • Computational settings • Startup settings for data fetch • Initial state of key variables

  7. More about Environment • Our tool constrains agent placement and movement with a unique system using layers of maps • Base master map • Big roads • Small roads • Water • Human/Mosquito • Ruminant • When combined, allow look-down one pixel at a time for maximum granularity

  8. Stacked Constraint Layers

  9. Master Map LayerVisual piece presented to the user, not used for computations

  10. Big Roads LayerFor agents that cannot be on big roads

  11. Small Roads LayerFor agents that cannot be on small roads

  12. Water Feature LayerFor agents that cannot be on water

  13. Human/Mosquito LayersUsed to place and constrain humans & mosquitoes

  14. Ruminant LayerUsed to place and constrain ruminants

  15. Stacked Constraint Layers

  16. Our AgentsMosquitoes, Ruminants & Humans • Mosquitoes • We model the female Aedes canadensis, an early spring, aggressive biter which is long lived (up to 90 days), univoltine (one generation per season), overwinters, and can transmit virus transovarially • Bites animals and man (can act as a bridge vector to introduce RVF) • Instantiated based on the hatch curve and at the rate of 100,000 per acre of water for each region (Boston 199 acres, Peterborough 6,309 acres, Tyngsborough 2,654 acres). • A small number of infected females and eggs overwinter in the simulation to model actual seasonal variation in numbers seen in the species. • Placed randomly in environment and set into motion • Time-dependent infectivity rate is modeled • RVF transmission from mosquito to ruminant is 21% at 7 days and 58% at 14 days (Gargan et al., 1988) • RVF transmission from mosquito to human is static at 1% throughout simulation. • Time between blood meals (specific for Aedes spp)

  17. Aedes canadensisDensity Curve

  18. Our Agents (Continued) • Ruminants • Created as 10% of the number specified for County (USDA Animal Census, 2002) • Placed randomly in constrained space within 1 mile of laboratory site (± 0.1 mile) and set into motion • 100% susceptible, latency 12-36 hrs, infectious 1-7 days • 10% fatality rate (in nature-100% in fetuses, 90% young sheep, 20% adult sheep, 10% adult cattle) • Humans • Created in the number specified for region • Placed randomly in constrained space and set into motion • 100% susceptible, latency 2-6 days, infectious 3-4 days • Then what?

  19. Example Agent State Chart

  20. General Simulation Flow • Model incorporates complex biology • Accurate characteristics of Aedes canadensis biology and behavior • Pathogenesis of RVF in humans and ruminants (exception-fatality rates held constant rather than vary with animal species and age) • At start of simulation, constraint maps are loaded • Agents are instantiated, placed, and set in motion • At every time step each mosquito “examines” its surroundings, and using its current state acts as follows: • If human or ruminant is present, it may be bitten based on previously established parameters (e.g., time since last blood meal) • If mosquito is infected, it passes virus to target based on its infectious state and randomly at 1% frequency • If target is infected, virus passes to mosquito based on target’s infectious state (level of viremia estimated by days post infection) • Agents change state over time and with interaction • Simulation proceeds to end (18 months)

  21. [Insert Simulation]

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