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

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Operated by Los Alamos National Security, LLC for NNSA Explicitly Time Dependent – Theory

[t1, t2] [t2 , t3] [t86399, t86400]

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[t1, t2] [t2 , t3] [t86399, t86400]

[t1,t2] [t2,t3] [t86399,t86400]

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Degree

1 – 6 7 – 12 13 – 18 19 – 24 25 – 30 31 – 36 37+ Degree Distribution

5 10

4 10

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%

4% Percentage Distribution of the Total Population

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

Number of Contacts 10

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

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

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Relaxing the closures can generate second wave of infection

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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

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 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.

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