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Agent-Based Modeling Approaches for Simulating Infectious Diseases

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Agent-Based Modeling Approaches for Simulating Infectious Diseases LA-UR-13-26511 Agent-based Modeling Approaches for Simulating Infectious Diseases Sara Del Valle, PhD Los Alamos National Laboratory Team: Sue Mniszewski, Mac Hyman, Reid Priedhorsky, Geoffrey Fairchild SAMSI Program on Computational Methods in Social Sciences August 18, 2013 UNCLASSIFIED Operated by Los Alamos National Security, LLC for NNSA Motivation “Previously, scientists had two pillars of understanding: theory and experiment. Now there is a third pillar: simulation” U.S. Secretary of Energy Steven Chu” Operated by Los Alamos National Security, LLC for NNSA Agent-based Model (ABM) A computational approach for simulating the actions of agents that interact within an environment. Operated by Los Alamos National Security, LLC for NNSA Agent-based Model (ABM) A computational approach for simulating the actions of agents that interact within an environment. Operated by Los Alamos National Security, LLC for NNSA ABM History The concept was originally discovered in the 1940s by Stanislaw Ulam and John von Neumann while working at LANL Von Neumann Neighborhood ABM History Due to the computational requirements, ABMs did not become widespread until the 1990s SimCity Why ABM? System is too complex to be captured by analytical expressions or experiments Experiments are too expensive or undesirable Operated by Los Alamos National Security, LLC for NNSA ABM Characteristics Heterogeneity Individual Agent Behavior Specification Explicit Space Complex Interactions Randomness Emergent Phenomena Operated by Los Alamos National Security, LLC for NNSA Ecological Systems Ecological Systems Understand the causes of this behavior Fish, ants, humans, locus, birds, honeybees, immune system, bacterial infection, tumor proliferation Locus plagues can contain up to 109 individuals Ecological Systems Buhl et al Experiments Vicsek et al. Vicsek et al. Theory Simulation Operated by Los Alamos National Security, LLC for NNSA Transportation Systems Transportation Systems Leonardi S. Experiments Theory Wilco Burghout Static Dynamic 7:00 10:00 15:00 18:00 Transportation Systems - TRANSIMS Epidemiological Studies 15,000,000 People die every year from infectious diseases Global Disease Burden UNCLASSIFIED Operated by Los Alamos National Security, LLC for NNSA MALARIAUNCLASSIFIED Operated by Los Alamos National Security, LLC for NNSA Source: http://en.wikipedia.org/wiki/Image:Anopheles_stephensi.jpeg {{PD-USGov-HHS-CDC}} DIARRHEALUNCLASSIFIED DISEASES Operated by Los Alamos National Security, LLC for NNSA RESPIRATORYUNCLASSIFIED INFECTIONS Operated by Los Alamos National Security, LLC for NNSA RESPIRATORY INFECTIONS Epidemic Modeling Experiments Theory S1 I1 R1 Susceptible Infectious Recovered Agent-based Model (ABM) Empirical Studies • National Household Transportation Survey • Workgroup size by industry classification • Student-teacher ratio • U.S. Census Data Theoretical Studies • Gravity algorithms • Monte Carlo methods Operated by Los Alamos National Security, LLC for NNSA The Demographics – Surveys Source: C.L. Barrett, S.G. Eubank, J.P. Smith (2005). If Smallpox Strikes Portland… Scientific American 292:54-61. Stroud P et al. Spatial Dynamics of Pandemic Influenza in a Massive Artificial Society. Journal of Artificial Societies and Social Simulation 2007; 10(4):9. The Locations Home Home Home Work Home School Social Home Shopping Passenger College Home Home Home Stroud P et al. Spatial Dynamics of Pandemic Influenza in a Massive Artificial Society. Journal of Artificial Societies and Social Simulation 2007; 10(4):9. A Typical Family’s Day – Surveys Work Work Lunch Carpool Carpool Shopping Home Home Daycare Car Car Bus School Bus time Stroud P et al. Spatial Dynamics of Pandemic Influenza in a Massive Artificial Society. Journal of Artificial Societies and Social Simulation 2007; 10(4):9. Others Use the Same Locations Stroud P et al. Spatial Dynamics of Pandemic Influenza in a Massive Artificial Society. Journal of Artificial Societies and Social Simulation 2007; 10(4):9. Bipartite Graph – Theory People Locations Operated by Los Alamos National Security, LLC for NNSA Bipartite Graph – Theory Operated by Los Alamos National Security, LLC for NNSA Explicitly Time Dependent – Theory [t1, t2] [t2 , t3] [t86399, t86400] Operated by Los Alamos National Security, LLC for NNSA People-people Graph – Theory [t1, t2] [t2 , t3] [t86399, t86400] [t1,t2] [t2,t3] [t86399,t86400] Operated by Los Alamos National Security, LLC for NNSA Social Contact Network Emerges Degree 1 – 6 7 – 12 13 – 18 19 – 24 25 – 30 31 – 36 37+ Degree Distribution 5 10 4 10 3 contacts 10 k 2 10 1 10 0 10 Number of people with ï1 10 10 20 30 40 50 60 70 80 90 100 Number of contacts, k Del Valle et al. Estimating Mixing Patterns for the United States for Modeling Infectious Diseases (in preparation) Demographic and Activity Differentiation FLORIDA NEW YORK UTAH WYOMING FL ï HOME NY ï HOME UT ï HOME WY ï HOME 70 70 70 70 60 60 60 60 50 50 50 50 HOME 40 40 40 40 30 30 30 30 20 20 20 20 10 10 10 10 10 20 30 40 50 60 70 10 20 30 40 50 60 70 10 20 30 40 50 60 70 10 20 30 40 50 60 70 FL ï WORK NY ï WORK UT ï WORK WY ï WORK 80 80 80 80 70 70 70 70 60 60 60 60 WORK 50 50 50 50 40 40 40 40 30 30 30 30 20 20 20 20 20 40 60 80 20 40 60 80 20 40 60 80 20 40 60 80 FL ï SCHOOL NY ï SCHOOL UT ï SCHOOL WY ï SCHOOL 20 20 20 20 18 18 18 18 16 16 16 16 14 14 14 14 12 12 12 12 10 10 10 10 SCHOOL 8 8 8 8 6 6 6 6 4 4 4 4 2 2 2 2 5 10 15 20 5 10 15 20 5 10 15 20 5 10 15 20 FL ï OTHER NY ï OTHER UT ï OTHER WY ï OTHER 90 90 90 90 80 80 80 80 70 70 70 70 60 60 60 60 50 50 50 50 OTHER 40 40 40 40 30 30 30 30 20 20 20 20 10 10 10 10 Del Valle et al.20 Estimating40 60 80 Mixing Patterns20 40 for 60the 80United States20 for 40Modeling60 80 Infectious 20Diseases40 60 (in80 preparation) Population Age Distribution 20% Florida FloridaNew York Utah 18% New YorkWyoming U.S. 16% Utah Wyoming 14% U.S. 12% 10% 8% 6% 4% Percentage Distribution of the Total Population 2% 0% 0 ï 4 5 ï 14 15 ï 24 25 ï 34 35 ï 44 45 ï 54 55 ï 64 65 ï 74 75 ï 84 85+ Age Group Del Valle et al. Estimating Mixing Patterns for the United States for Modeling Infectious Diseases (in preparation) Comparison with Empirical Data 30 MOSSONG SIMULATEDPOLYMOD SIMULATED 25 20 15 Number of Contacts 10 5 0 0ï4 5ï9 10ï14 15ï19 20ï29 30ï39 40ï49 50ï59 60ï69 70+ Age Groups Mossong et al. Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases. PLoS Med 2008. Disease Model in EpiSimS Susceptible Asymptomatic Recovered Incubating, Incubating, Symptomatic- not infectious circulating infectious 1 Half-day Histogram 0.9 Longini Symptomatic- 0.8 0.7 stay home 0.6 0.5 0.4 Convalescence 0.3 Fraction still incubating 0.2 0.1 0 Dead 0 1 2 3 4 5 Hospitalized Incubation Stage Duration (days) Incubation Symptomatic Stroud P, Del Valle S, Sydoriak S, Riese J, Mniszewski S. Spatial Dynamics of Pandemic Influenza in a Massive Artificial Society. (2007) Journal of Artificial Societies and Social Simulation, 10(9). Probability of Infection −∑TS j I i tij i Pj (t) =1− e All infected people Susceptible co-located with j Person j Stroud P, Del Valle S, Sydoriak S, Riese J, Mniszewski S. Spatial Dynamics of Pandemic Influenza in a Massive Artificial Society. (2007) Journal of Artificial Societies and Social € Simulation, 10(9). Operated by Los Alamos National Security, LLC for NNSA Attack rate red: >35% amber: 29% to 35% grey: 17% to 29% green: <17% Stroud P et al. Spatial Dynamics of Pandemic Influenza in a Massive Artificial Society. Journal of Artificial Societies and Social Simulation 2007; 10(4):9. Attack Rate Correlates with Demographics Average Household Size Per Capita Income Population Aged 5 – 18 Population Density Heterogeneous Spread Demographic Differentiation Operated by Los Alamos National Security, LLC for NNSA Impact of School Closures Relaxing the closures can generate second wave of infection Operated by Los Alamos National Security, LLC for NNSA Pharmaceutical Interventions Vaccine effectiveness and production rates Operated by Los Alamos National Security, LLC for NNSA Social Media & Behavior Demographic factors Age Gender Ethnicity Educational level “Who” is likely to change behavior and “How” is implemented Operated by Los Alamos National Security, LLC for NNSA Facemask Study Summary ABMs provide a test-bed for: • Testing hypothesis • Forecasting population demographics, infectious disease transmission, etc. • Developing complex social networks Operated by Los Alamos National Security, LLC for NNSA Challenges and Opportunities Statistical Inference Model Fitting, Validation, and Verification Uncertainty Quantification for Agent-based Simulations Prediction & Forecasting Emulators Emergent Behavior Social Media Operated by Los Alamos National Security, LLC for NNSA Thank You! J Epidemiol Community Health. 2006 January; 60(1): 6.– 7. Operated by Los Alamos National Security, LLC for NNSA .
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