BioWar: Scalable Agent-Based Model of Bioattacks

1. CASOS Alignment of model structure Construct empirical data sets from literature Align model components Detection & Planning Calibrate model parameters Develop scenarios for comparisons Run BioWar model Run IPF model Display Geographic Chart Locations proportional to population People 10% Diseases 60 background 2 weaponized Time 2 year Alignment of model outputs First order analysis - compare final results Second order analysis - compare dynamics of populations over time % of those who would have died who are saved • Attack Profile • Anthrax medium • Smallpox medium Comparison What Output to Save - Standard Challenge Alert Status - none • City Profile • Pittsburgh • San Diego CASOS Number of Days After Attack Until Detection BioWar: Scalable Agent-Based Model of Bioattacks Project Investigators: Kathleen M. Carley (Project Director and PI) – CMU, ISRI, CASOS Douglas Fridsma – University of Pittsburgh, BMSI Elizabeth Casman – CMU, EPP • Students: • Alex Yahja – CMU, ISRI, CASOS, Tiffany Tummino – CMU, EPP • Post Doc: • Li-Chiou Chen – CMU, ISRI, CASOS • Programming Staff: • Boris Kaminsky -- CMU, Demian Nave -- PSC, Neal Altman -- CMU Potential Usage Estimating the Impact of Detection Description Tool for evaluation of response policies, data efficacy, attack severity, and detection tools relating to weaponized biological attacks against the background of naturally-occurring diseases Process of aligning BioWar and population-based Incubation-Prodromal-Fulminant (IPF) model Generate possible attacks – to layer on existing data or complete simulation of all data Examine effectiveness/costliness of response policies Pre-evaluate possible data sets for detection Pre-evaluate whether more detailed data collection might be useful Training for intel officers and health workers about what an attack might look like • Anthrax attack ~19,000 exposed (pop. ~170,000) • Three (3) kilograms of anthrax, released in an explosion with an efficiency of 0.05, giving a 150 gram effective mass • Time of  attack: 4 PM, Place: Stadium in Hampton, VA • Wind speed is low, ~0.63 meters/second • The dosage is inversely proportional to the wind speed • The number of people saved is calculated as 0.55 * (total_fatalities - fatalities_up_to_that_day) • It reflects that the intervention brings the death_rate down from 0.85 to 0.45. • 0.85 is the untreated death rate published in various papers including Meselson's Sverdlovsk paper • 0.45 is the quick treatment death rate corresponds to the US mail case • Estimate %saved of those likely to die with general diagnosis -- given detection on that day by number of days since attack. BioWar – conceptualization city scale multi-agent network model of weaponized attacks Validation & Tuning Done Conclusion: Detection cannot occur soon enough to prevent a significant numbers of deaths BioWar vs. IPF, based on time to death for Sverdlovsk data • Approach • Multi-Agent Network Model • - Cognitively realistic • Socially realistic – embedded in social, knowledge, • & task networks • - Spatio-temporally realistic • - Organizational network • - Communication technologies • Hybrid of many models: disease, spatial, network, cost, agent, social, geographical, media, etc. Simulator Comparison BioWar vs. Incubation-Prodromal-Fulminant (IPF) Model BioWar vs. IPF, based on time to death for US Mail data • Advantages • BioWar • the disease progression model of anthrax can be reused for different cities and with different scales • the disease duration of anthrax changes with policy responses • calculates secondary data streams such as OTC purchases and population responses • IPF • quick disease model prototyping • Disadvantages • BioWar • BioWar takes more time and computer resources • IPF • simulates only disease progression • cannot calculate the infected rate based on the given demographics of a population, geography, weather, & the released mass of an anthrax attack