Agent-Based Computational Modeling and Population Health

1. Agent-Based Computational Modeling and Population Health Dr. Joshua M. Epstein Senior Fellow, Economic Studies and Director, Center on Social and Economic Dynamics The Brookings Institution External Professor Santa Fe Institute June 2008

2. Outline • Agent-Based Models • Generative Explanation • Public Health Applications • Range of Hazards • Range of Scales

3. Features of Agent-BasedComputational Models • Heterogeneity • No representative agent; no homogeneous pools, no aggregation • Every agent explicitly represented, and differ by: • Wealth, network, immunocompetence, memory, genetics, culture, ... • Autonomy • Bounded Rationality • Bounded Information • Bounded Computing • Explicit Space • Local Interactions • Non-Equilibrium Dynamics • Tipping Phenomena

4. Generative Social Science: Studies in Agent-Based Computational ModelingPrinceton University Press, November 2006 • Two Philosophy Chapters • Epidemic Dynamics (IJID, AEM) • Civil Violence: Revolutions and Ethnic Conflict (PNAS) • Histories:The Anasazi (PNAS) • Adaptive Organizations (PUP) • Networks and Retirement Dynamics ( R. Sage) • Economic Classes (MIT Press) • Demographic Games (Complexity) • Spatial zones of cooperation in demographic PD Game • Spatial norm maps in demographic Coordination Game • Thoughtless Conformity to Social Norms (Comp. Econ.) • Aspires to be > collection. An argument, about ABM generally:

5. Stakes: The Future of Explanation • What’s centrally at stake in the advent of agent-based modeling is the notion of an explanation in the social sciences. • To explain macroscopic social patterns, we “grow” them in agent models. • It does not suffice to demonstrate that, if society is placed in the pattern, no individual would unilaterally depart (Nash equilibrium/refinements). • Rather, one must show how a population of plausible agents, interacting under plausible rules, could actually arrive at the pattern—be it a segregation pattern, a wealth distribution, or a pattern of violence. • Motto: “If You Didn’t Grow It, You Didn’t Explain It”

6. Generative Explanation • To explain a macroscopic phenomenon is to furnish a microscopic (i.e., agent) specification that suffices to generate it. • Canonical experiment is to situate an initial population of autonomous heterogeneous agents in the relevant spatial environment; allow them to interact according to simple local rules and thereby generate--or “grow”--the macroscopic phenomenon from the bottom up. • Generative sufficiency is the core explanatory notion:

7. Today’s Examples: Public Health: Infectious, Non-Infectious, Multiple Scales • Playground (warm-up) • Town Scale • Smallpox • City Scale • New Orleans and LA Plume-Agent • National Scale • U.S. smallpox • Global Scale • MIDAS Pandemic Flu

8. Classical Mathematical Epidemiology Where there is a beautiful and revealing mathematical theory, learn it.

9. Classical Perfect-Mixing Contact Process • S(t) Susceptibles at t • I(t) Infectives at t • Susceptibles become infected at rate proportional to S x I. • S  I I I I ….. • S  • S  • S  • If we now add death, we have the classic model…

10. The Classic Kermack-McKendrick (1927) SIR Model Susceptible Growth Rate: dS/dt = -rSI Infective Growth Rate: dI/dt = rSI-bI Removed Growth Rate: dR/dt = bI Perfect Mixing. Very Unrealistic. So What? Very Revealing

11. Counterintuitive Insights [1] Showed that epidemics are Threshold Phenomena. Take off only if dI/dt > 0. That is: rSI-bI > 0 , or S > b/r  Fizzle if S < b/r (lethality/transmissibility) [2] For “Herd Immunity,” Vaccinate until Remaining Susceptible Pool is Less Than Threshold b/r. The Classic Result For Homogeneous and Well-Mixed Populations. [3] Excessively deadly pathogens kill their hosts before they can spread (for sufficiently large b, no vaccination necessary) Can be elegantly recast in terms of the Reproductive Number…

12. R-naught threshold Interpretation: net secondary infections for one index Function of biological and social factors. “The R0 of …”

13. V for Herd Immunity Had that Vaccinate till: Equivalently: 1918 pandemic flu R=2, so vaccination of ½ would have sufficed. Zero incubation ; perfect mixing: upper bound.

14. Truth and Beauty • Picasso said, “Art is a lie that helps us see the truth.” • Like all the best models (Malthus, Lotka-Volterra, Hooke’s Law) this one is wrong. It is an idealization. But it is simple and elegant, and yields a profound insight. Basis of all vaccination strategy. Agent model had better show it. • So, let’s replicate this.

15. Local Playground ContagionSIR bug (transmission =.15, removal =.02)

16. Influenza Epidemic Data, 1978English Boarding School Influenza epidemic, boys boarding school. British Medical Journal, 4 March, 1978. Of 763 boys, 512 were confined to bed, 22 January-4 February, 1978.

17. Fine for very small-scale well-mixed setting • But not…

18. Here

19. … Or Here

20. …Or Here Well-mixed models misleading

21. Example 2. Smallpox Model • Developed with Donald Burke JHSPH for The Smallpox Working Group, Secretary’s Council on Public Health Preparedness, HHS, founded and chaired by D. A. Henderson. • Intensive regular meetings to arrive at reasoned assumptions about all biomedical and critical behavioral aspects

22. Model Scale • Include Social Units that Loom Largest in Data: • Homes • Schools • Workplaces • Hospitals

23. County-Level Model • 2 Towns • Per Town Assumptions • 400 people comprised of • 100 Households, each with 2 adults and 2 kids • Non-commuting adults work at the town workplace • 10% adults of commute to the other town’s workplace • 5 adult hospital workers • Kids go to school in the town school • 1 workplace • 1 school • 1 Common Hospital • 10 adult hospital workers • 1 Common Morgue • Day = Night = 10 Rounds • Contacts Per Day: Home=3 , Work=10.

24. Morgue County Hospital “Night-Time” = All individuals at home, not at work or school

25. “Day-time” = All individuals are at work or school Morgue County Hospital All individuals go home at night, and the cycle repeats every “day”

26. Ordinary Smallpox Natural History

27. Susceptible Infected, Asymptomatic, Non-Contagious Infected, Febrile, Contagious, Early-Rash Infected, Contagious, Rash Dead Recovered and Immune Period of Vaccine Efficacy 0 4 12 14 15 19 23 Days Simplified Progression of Smallpox Highly Contagious Period

28. Base Case Run • No Policy Interventions • No Background Immunity • One commuter index case starts the epidemic in Circletown; it spreads to Squaretown • NOTE: Hospital Demand

29. Smallpox Base Case

30. Typical Results for Base Case Run: Incidents per Day and Time Series of Cumulative Infected. 800 infected. 255 dead.As in pre-vaccine Europe.

31. Applications • Vaccine Stockpile • ER Surge Demand • Detailed Reconstructions • Dendrograms • Empirical Calibration • Design of Interventions

32. No interventions Preemptive vaccination of hospital workers plus family contacts Results of Hospital and Family Only(280 Dead vs. 45 Dead)

33. City Scale Toxic Chemical • Collaborators: Dr. Bharat Soni. Chair, Dept of Mechanical Engineering, UAB. New Orleans • Dr Harry MacDonald. National Simulation Center, UTC Los Angeles • Highly realistic fluid dynamics • Plausible human behavior • Mass psychology • Non-compliance • Flight • Congestion • Design optimal response

34. New Orleans Model

35. LA Plume-Agent Hybrid (UTC)

36. LA Traffic Case

37. Novelty • The combination of high fidelity fluid dynamics and • Behaviorally rich agent layer. • Design optimal preparedness and response • …Next Scale Up…

38. The PACER Large-Scale Agent Model (LSAM) • Won 2008 NTSA Award for Outstanding Achievement in Analysis • US population = 300 Million • North America = 525 Million • US + Europe = 1 Billion • LSAM can handle 6 Billion agents on the PACER Cluster at Brookings.

39. Synthetic US • 300 million individuals • Personal: Age, gender, family members • Social: Workplace, school, network • Economic: Income • Medical: Epi status (S,E,I,R,…) • Contact Dynamic • 31,522 zip codes in the continental US • All Hospitals and Staffed Beds nationwide • Travel Matrix 31,522 X 31,522 > 900 million cells • All Inter-ZC movement encoded in Gravity Model estimated econometrically

40. The US Model: Basic Application • The Scenario LA release • The Bug: ILI (Influenza-Like Illness) patterned on inter-pandemic flu H3N2: • Black (S), Red (E or I), Blue (R) • Interactions Modeled • Family Contacts • Random Contacts • Work / School Contacts

41. Base Case Movie

42. Base case

43. Experiment: NPI Policy Choices: Which is Better? • 75% Distancing imposed for 1 month • 50% Distancing imposed for 6 months • Distancing: • School closure • Workplace leave • Shelter-in place

44. NPI SOCIAL DISTANCING • 75% • Herd Immunity • 50% • Long endemic disease • Short-term draconian containment more effective than sustained moderate level.

45. Further Flu Applications • Value of targeted anti-viral prophylaxis • Value of poorly-matched vaccine • Optimal phasing of NPIs, Anti-virals, and Vaccine • …the next topic…

46. LSAM and Emergency Surge • Populated the LSAM with every Hospital/ER in the US. • Top 2500 regions (220 million people) • Total hospitals = 3,600 • Total staffed beds = 723,000 staffed beds

47. Centrality of Dynamics • Epidemics of the same size can have radically different implications for surge capacity…

48. Centrality of Dynamics • Epidemics of the same size can have radically different implications for surge capacity… • It all depends on epidemic dynamics

49. Slow Case: 25% handled/75% overflow

50. Fast Case: 17% handled/83% overflow