Agent-Based Explicitly Spatial and Temporal (A-BEST) Model

1. Agent-Based Explicitly Spatial and Temporal (A-BEST) Model Ling Bian National Center for Geographic Information Analysis (NCGIA) Department of Geography University at Buffalo, State University of New York NIH Blue Ribbon Panel, March 14, 2007, Bethesda, Maryland

2. Scenario and Objective • A laboratory worker who sustains a laboratory-acquired infection and subsequently leaves the laboratory and enters the community surrounding the lab • Potential diseases caused by these infectious agents are transmitted through close contacts or occupationally-acquired infections • This model simulates the vulnerability of individuals in communities through an individual-based, spatially explicit, network approach, predicting spatial and temporal heterogeneity under different transmission scenarios

3. A-BEST vs. Classic SIR • Agent-Based - individual-based vs. population-based modeling unit • Explicitly Spatial - spatially explicit vs. non-spatial representation • and Temporal - nighttime/daytime/pastime populations vs. daily case count • Model - social network vs. traveling wave transmission

4. The Classic SIR Models • A population is dived into three basic segments Susceptible (S) Infected/infectious (I) Recovered (R) • At any time, some individuals move from S to I and some from I to R • The change is represented by a differential equation set dS/dt = -SI, dI/dt = SI - gI, and dR/dt = gI

5. The Classic SIR Model ..- the spatial version • Assumptions: identical individuals homogeneous mixing uniform spatial distribution equal contact rate • Operation: traveling wave, “contagious diffusion” • Applications: pandemic, regional diffusion, aggregated data Kermack and McKendrick 1927; Cliff et al. 1981; Cliff et al. 1986; Anderson and May 1992

6. A-BEST Model • Assumptions: discrete individuals individualized interaction explicit spatial locations mobile individuals • Operation network • Applications local,within- community Bian 2004;Dibble and Fieldman 2004; Huang et al. 2004; Eubank, et al. 2004; Ferguson et al. 2005; 2006; Longini et al. 2005; Burke et al. 2006; Bian and Liebner 2007; Riley 2007;

7. A-BEST Model - design principles - • Communicable diseases transmit from individualto individual through a social network of human contact • The spatial and temporal heterogeneity in this process can significantly affect the outcome of population health • Individuals • Location, time, and mobility of individuals • Attributes for each individual and each household, workplace, and service place • Three daily populations and links within and between them (three-activity two-scale network) • Diseases, infection and fatality rates, and scenarios

8. Time 12:00 AM 6:00 PM Individual lifeline 1 Individual lifeline 2 9:00 AM 12:00 AM Service place 1 Home 1 Workplace 1 Home 2 Place Time Geography • Individual life lines • Intersection of life-lines forms a group (local infection) • Connection between groups forms a social network (long-distance infection)

9. Network • A three-activity and two-scale network • Three activities • Homes, workplaces, and service places • Two scales • within- and between-group networks Service places Workplaces Homes

10. Conceptual Framework Community Modeling

11. Simulation Components • Nighttime population at home - who lives with whom at where • Daytime population at workplaces - who works with whom at where • Pastime population at service-places - who “shops” with whom at where • Disease transmission - who is infected when and where

12. Nighttime Population- who lives with whom at where - • Use 2000 census data SF3 (11tables, P1-P18) • 2 miles around the medical center, including 227 block groups 104,000 households 245,842 individuals

13. Census Units County Block Census tract Block group http://www.census.gov/geo/www/tiger/tiger2002/tgr2002.pdf Page 4-17

14. Screenshots of 2000 Census Bureau Summary File 3 (SF3)

15. Nighttime Population- who lives with whom at where - • Disaggregate the block group data to assign 245,842 individuals into 104,000 households according to: - gender - age (≤5, 6-15, 16-17, 18-64, ≥65) - household type (married couple, single father, single mother, and other) - household size - relationship to the householder - children (number of children, own or other’s) • Monte Carlo method is used for the assignment given , calculate ; is given by SF3 tables for assigning household size, for assigning individuals

16. Nighttime Population .. • Household data purchased from ReferenceUSA Inc. • Merged with the simulated households to obtain additional attributes for households Additional Attributes Common Attributes - location (lat and long) - household size - income - marital status - religion - gender of householder - purchase power - age of householder - Internet user - presence of children

17. Nighttime Population .. 2000 Census block group data ReferenceUSA household data

18. Nighttime Population .. • Individual attributes: identification, household identification, age range (5 categories), gender, relationship to the householder (5 categories), and occupation (27 NAICS categories) • Household attributes: identification, size, age range composition, gender composition, household type (5 categories), income level (18 categories), number of vehicles, religion (5 categories), address, block group, and census tract

19. Daytime Population at Workplaces- who works with whom at where - • Business data purchased from ESRI Inc./ReferenceUSA • 310,400 businesses within an hour driving distance from the three communities • Attributes of the businesses - name - address - employee size - estimated sales - type of business NAICS (North America Industry Classification System)

20. Daytime Population ESRI business data (from InfoUSA)

21. Daytime Population .. • 2000 census data SF3 (13 tables, P30-P53, H44-H45) • Assign individuals to workplaces according to: - Who works - How to go to work - How long it takes to go to work

22. Daytime Population .. • Who works, according to: - gender of workers - age of workers - labor force (armed forces, employed, unemployed) - workers by household type - number of workers in household - presence of children in household • Who walks, takes bus, drives, or takes subway to work, according to: - number of vehicles in households - age of householder with or without vehicles - median household income • How far, according to: - workers who work outside home - time takes to travel to work - transportation mode, actual speed and transportation system

23. Daytime Population .. • Who works • How to go to work (speed) • How long it takes to go to work (time) • Who goes where to work, according to: - distance between homes and workplaces - match between worker’s occupation (NAICS) and business type (NAICS) • Monte Carlo method is used for the assignment given , calculate ; is given by SF3 and RefUSA tables for assigning workplace size, for assigning individuals

24. Daytime Population .. • Worker attributes: individuals in a household as workers, number of workers in a household, occupation of each worker (27 categories), travel mode of each worker (7 categories), time taken to work for each worker (12 categories), travel speed of each worker, travel distance of each worker, and travel route of each worker • Workplace attributes: identification, address, workplace size, and workplace type (27 categories, including schools).

25. Pastime Population- who shops with whom at where - • 1991Travel Diary of the area • 3,906 households, 9,256 individuals, 39,934 trips • Type of activities/services (89,159 businesses) - pick up or drop off - shopping - eat out - recreation - personal - social

26. Pastime Population .. • Type of service trips - home to service - work to service - service to service - multi-person trips - multi-purpose trips

27. Pastime Population

28. Pastime Population .. • Assign individuals to service-places by the type of services and the type of service trips according to: - age of individuals - gender of individuals - number of vehicles in a household - number of workers in a household - size of a household • Monte Carlo method is used for the assignment

29. Pastime Population .. • Where to shop, according to - transportation mode - nearest service-place from home or workplace • When - daytime - pastime - weekdays and weekend

30. Pastime Population .. • Individual shopper attributes: type of trip by service type (6 categories), type of trip by origin-destination (3 categories), type of trip by number of co-shoppers, number of daily trips, number of daily trips of the household, day of week (2 categories), transportation mode, travel time, travel speed, travel distance, travel route, sequence of daily trips, and time period of trips (2 categories). • Service-place attributes: identification, address, service type (11 categories), number of workers, number of customers, shift of workers (2 categories), day of week (2 categories), and time period (2 categories).

31. Disease Transmission- who’s infected when and where - • With the three populations and links between them, the first case is introduced into the community, according to the scenarios • Transmission occurs by time and location daytime (9am-5pm) workplaces or homes pastime (5pm-12am) shops or homes nighttime (12am-9am) homes • Mixing pattern full mix at home partial mix at workplaces and service-places

32. Disease Transmission .. • Number of contacts Average personal daily = 9.74 Maximum average personal daily = 11.49 Minimum average personal daily = 7.08 Average total daily = 2,394,994 Maximum total daily = 2,824,339 Minimum total daily = 1,740,232

33. Disease Transmission .. • Infectious diseases - Ebola, Monkeypox, Sabia, Rift Valley Fever, and common flu • Infection rate - primary, secondary, tertiary, and fatality • Infection period - incubation period - infectious period - immune period

34. Influenza-like agent: infection rates: adults 0.1, senior and children 0.15 Incubation period : 2 days; Infectious period: adults 4 days, senior: 4 days, children 7 days

35. Disease Transmission .. • The probability of an individual becoming infected after being in contact with an infected individual is based on the primary, secondary, and tertiary infection rate of a disease • the Monte Carlo method is used to assign the infected status to individuals who are in direct contact with an infected individual given , calculate ; by infection rates

36. Community Modeling Simulation Results

37. [Insert Simulation]