Modelling Pandemic Influenza in the United States

1. Modelling Pandemic Influenza in the United States Timothy C. Germann, Kai Kadau, and Catherine A. Macken Los Alamos National Laboratory Ira M. Longini, Jr. Fred Hutchinson Cancer Center and University of Washington, Seattle

2. Outline • EpiCast (“Epidemiological Forecasting”) model design and parameterization • Simulated pandemics in a fully susceptible population • Assessment of various mitigation strategies

3. What is EpiCast? • A stochastic agent-based simulation model of the United States population of 281 million individuals (implemented on modern parallel supercomputers), to predict the nationwide spread of infectious diseases and to assess various mitigation strategies. T. C. Germann, K. Kadau, I. M. Longini, and C. A. Macken, “Mitigation Strategies for Pandemic Influenza in the United States,” submitted to Proceedings of the National Academy of Sciences.

4. Oversimplified Perspective of Various Epi-models Computational Cost Supercomputer Workstation/PC High (individual, minute-by-minute) EpiSims Moderate (individual, with mixing groups) Fidelity or Resolution Elveback, Longini, Epstein, … EpiCast Low (homogeneously mixed population) SIR equations (PDE’s) S'(t) = -rSI I'(t) = rSI - γI R'(t) = γI Community City State Nation World Spatial Scale

5. The four key elements of our model • Community-level transmission between people, through various contact groups (household, work group, school, …) • Disease natural history model and parameters • U.S. Census demographics (where people live) and workerflow data (where they work), at tract-level resolution • DOT statistics on long-distance travel

6. WG WG WG WG WG WG WG WG WG WG WG WG WG WG WG WG WG WG WG WG WG: Work group Person-to-person transmission is described by contact groups within a ~2000-person model community* *M. E. Halloran et al, Science298, 1428 (2002); I. M. Longini et al, Science309, 1083 (2005).

7. Stochastic Transmission • Each susceptible individual (blue) has a daily probability of becoming infected, based on all of their potential contacts with infectious individuals (red):

8. Stochastic Transmission • For the susceptible individual shown in blue, the probability of becoming infected is: These may be further modified if the infectious and/or susceptible individuals have been vaccinated, are taking antivirals, …

9. The four key elements of our model • Community-level transmission between people, through various contact groups (household, work group, school, …) • Disease natural history model and parameters • U.S. Census demographics (where people live) and workerflow data (where they work), at tract-level resolution • DOT statistics on long-distance travel

10. Persons who become ill may self-isolate to household-only contacts Natural History for Pandemic Influenza Probability of infecting others Symptomatic (67%) Asymptomatic (33%) 0 days Latency Exposure and infection 1.2d Incubation Possibly symptomatic 1.9d 4.1d

11. Case Serial Interval • Time between illness onset times for a case and the person infected • Latent, incubation and infectious period lengths • Distribution of infectiousness • Our model, mean = 3.5 days • Ferguson, et al. mean = 2.6 days • Determines the speed of the epidemic, but not the final size • Current Avian A(H5N1), seems to have longer serial interval than current human strains

12. Basic Reproductive Number: R0 • Number of secondary infections due to a singletypical infected person in a totally susceptible population R0 > 1 for sustained transmission • For pandemic influenza: 1< R0 ≤ 2.4 • A(H3N2) 1968-69, R0 ≈ 1.7 • A(H1N1) 1918, second wave, R0 ≈ 2

13. Rapid Real Time Evaluation • Important to rapidly estimate key parameters of pandemic strain • Pathogenecity, virulence, natural history parameters • Transmissibility parameters • R0 • Serial interval • Secondary attack rates • Others

14. The four key elements of our model • Community-level transmission between people, through various contact groups (household, work group, school, …) • Disease natural history model and parameters • U.S. Census demographics (where people live) and workerflow data (where they work), at tract-level resolution • DOT statistics on long-distance travel

15. Census tract-level resolution The US census tract level provides a finer-scale resolution than counties, with more uniform population sizes that correspond to the 2,000-person community granularity (so that on average, each tract is modeled by two communities): Average tract population: 4,300 65,433 U.S. census tracts

16. Constructing the model U.S. population We use U.S. Census Bureau data on tract-level demographics and worker-flow, and Dept. of Transportation data on irregular long-range travel to assign fixed residential and workplace communities to each individual, in addition to infrequent visits to more distant communities. 1,344 Cook County (IL) census tracts

17. Census worker flow data Raw data represents a snapshot at the particular week the survey was carried out; restrict daily commuter traffic to a “reasonable” distance (e.g., 100 miles):

18. People go to work according to the distance to work survey data

19. The four key elements of our model • Community-level transmission between people, through various contact groups (household, work group, school, …) • Disease natural history model and parameters • U.S. Census demographics (where people live) and workerflow data (where they work), at tract-level resolution • DOT statistics on long-distance travel

20. Long Distance Travel Model* • Trip Generation: Which individuals/households make a long distance trip? Use age-dependent average number of trips per year to determine the daily probability of making a long-distance trip, then roll the dice for each person every day. • Destination Choice: Where do they go? Simplistic gravity model: choose a random community within the simulation (either a 2,000-person residential or a 1,000-person workgroup-only community), without any distance dependence. • Trip Duration: How long do they stay there? Use the national statistics on trip duration to choose a duration from 0-13 nights. *An advanced model, including household income in step 1, distance and median destination income in step 2, and trip purpose and distance in step 3, has been developed and is currently being implemented.

21. Capturing long-range (irregular) travel behavior Use Bureau of Transportation Statistics data on travel frequency and duration (in lieu of detailed city-to-city transportation data):

22. Influenza in the US: Simulated and Historical Pandemics

23. Baseline (R0 = 1.9) Each Census tract is represented by a dot colored according to its prevalence (number of symptomatic cases at any point in time) on a logarithmic color scale, from 0.3-30 cases per 1,000 residents.

24. Baselinesimulatedpandemics Most of the epidemic activity is in a 2-3 month period, starting 1-2 months after introduction

25. Asian Influenza A(H2N2) 1957-1958* • July 1957, sporadic cases, West Coast and Louisiana • Aug. 1957, local small epidemics begin • Sept. 1957 – Oct. 1957, peaks occur • Most epidemic activity over this 60 day period *Source: Kilbourne (1975)

26. Hong Kong Influenza A(H3N2) 1968-1969* • July 1968, sporadic cases, West Coast • Oct. 1968, local epidemics begin • Dec. 1968 – Jan. 1969, peaks occur • Most epidemic activity over this 60 day period • March. 1968, end of epidemic activity *Source: WHO (1968-1970), Rvachev and Longini (1985)

27. Introduction of 40 infecteds on day 0, either in NY or LA, with and without nationwide travel restrictions Day 60 Day 80 Day 100 Day 120

28. Assessment of Mitigation Strategies

29. Assessment of Mitigation Strategies (single or in combination) In the following, we assume (and simulation results confirm) that disease spread is so rapid that all interventions are done on a nationwide basis simultaneously; however, a state-by-state (or more local, down to tract-by-tract) staged response can also be studied with our model. • Vaccination (with a fixed rate of production and distribution) • Targeted antiviral prophylaxis (from a limited national stockpile) • School closure • Social distancing, either a voluntary response to an ongoing pandemic, or as the result of an imposed quarantine or travel restrictions

30. Dynamic Vaccination Options • Production capacity 4/10/20M doses per week • Dose (and efficacy) of vaccine - 1 vs. 2 doses • Timing of vaccination - relative to start of pandemic Simulated protection by vaccination 30d -60d 60d Zoonoses Pandemic spread Pandemic in U.S. Time Low efficacy - one dose High efficacy - two doses Low efficacy

31. Dynamic Vaccination Distribute the available supply of vaccine (with a specified starting date, rate, and limit for production and distribution) to the eligible population (neither sick nor previously vaccinated) using two strategies: • Random distribution to the entire (eligible) population • Distribute preferentially to children first, then any remaining supply to the adult population Also consider two different scenarios: • The early production of a low-efficacy, single-dose vaccine, with: • Vaccine efficacy for susceptibility VEs = 0.30 • Vaccine efficacy for infectiousness VEi = 0.50 • The delayed production of a higher-effficacy, 2-dose vaccine, with: • Vaccine efficacy for susceptibility VEs = 0.70 (VEs = 0.50 for elderly) • Vaccine efficacy for infectiousness VEi = 0.80

32. Vaccination Baseline

33. Random vaccination, R0 = 1.6

34. CONTACTS Household Household cluster Preschool/daycare School Workplace 60% ascertainment 60% school 100% household + HH cluster 100% preschool 60% workplace TAP: Targeted antiviral prophylaxis using neuraminidase inhibitors (oseltamivir/relenza)

35. Targeted Antiviral Prophylaxis (TAP) • Close contacts of symptomatic individuals are treated prophylactically, until the national stockpile is exhausted • Assume X% of symptomatic cases can be identified, then: • 100% of household, household cluster, and preschool / playgroup contacts are treated • Y% of workgroup and school contacts are treated • We will focus on two cases: X = Y = 60% or 80% • Each course consists of 10 tablets, 2/day for treatment of symptomatic cases and 1/day for prophylaxis • Antiviral treatment reduces the sick period by 1 day • 5% of patients stop taking antiviral after 1 day • Antiviral efficacy for susceptibility AVEs = 0.30 • Antiviral efficacy for infectiousness AVEi = 0.62 • Antiviral efficacy for illness given infection AVEd = 0.60

36. TAP (20M courses) Baseline

37. Rapid intervention can preserve the limited antiviral stockpile and reduce the attack rate: 60% TAP R0 = 1.9 U.S. Strategic National Stockpile of Tamiflu® Now: 2.3M courses Planned: 20M courses Pandemic virus arrives in U.S. Pandemic alert

38. School closure We assume that once schools are closed, they remain closed for the duration of the epidemic. School closure includes: • High schools • Middle schools • Elementary schools • Preschools • Regular preschool-age playgroups

39. Social distancing / quarantine As a result of either a formal quarantine program, or voluntary changes in social and hygienic behavior in the event of a widespread pandemic, we assume that: • School, preschool, and playgroup contact rates are cut in half. • Workgroup contact rates are cut in half. • Household contact rates double. • Household cluster contact rates remain unchanged. Once initiated, this alteration in normal behavior is assumed to last throughout the remainder of the epidemic.

40. Travel restrictions The random long-range travel frequency can be reduced at any time, either due to imposed travel restrictions or behavioral changes (as occurred during the SARS scare). While by itself this can only slow the spread, it can potentially be useful to buy time for other interventions.

41. 90% travel cut Baseline

42. TAP, vaccination, or school closure can contain an outbreak for R0 ≤ 1.6 (cumulative ill per 100) 160% TAP, 7 days after pandemic alert, unlimited antiviral supply. 210 million doses of a low-efficacy vaccine (single-dose regimen) per week for 25 weeks, beginning such that the first persons treated develop an immune response on the date of the first U.S. introduction. 310 million doses of a high-efficacy vaccine (2-dose regimen) per week for 25 weeks, beginning such that the first persons treated develop a full immune response 30 days after the first U.S. introduction. 4Intervention starting 7 days after pandemic alert. 5Reduction in long-distance travel, to 10% of normal frequency.

43. An aggressive combination of therapeutic and social measures can succeed for R0 ≤ 2.4 210 million doses of a low-efficacy vaccine (single-dose regimen) per week for 25 weeks, beginning such that the first persons treated develop an immune response on the date of the first U.S. introduction. 4Intervention starting 7 days after pandemic alert. 5Reduction in long-distance travel, to 10% of normal frequency. 6Intervention starting 14 days after pandemic alert. *Exhausted the available supply of 20M antiviral courses.

44. Epi curves (note log scale)

45. Recommendations • For R0≥ 1.9, we would need at least 182 million courses of oseltamivir to have an impact on spread • For R0≤ 1.6, spread can be controlled by dynamic vaccination with low efficacy vaccine (10 million doses per week), school closure • For 1.9 ≤ R0 ≤ 2.4, only combinations of TAP, vaccination, social distancing measures and travel restrictions are effective • Social distancing and travel restrictions are not effective when used alone

46. Recommendations (cont.) • For limited quantities of vaccine • Rapid vaccination of one-dose low efficacy is more effective than two-dose high efficacy • Vaccination of school children first is much better than random vaccination • Vaccination alone requires high vaccination rates and production total • Rapid use of TAP preserves limited antiviral stockpiles • We can effectively divert antivirals and vaccines to the critical workforce within limits

47. The End

48. Popular Press