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Containing Pandemic Influenza at the Source

Containing Pandemic Influenza at the Source. Ira M. Longini, Jr. Dept. Biostatistics U. Washington Hutchinson Rsh Ctr. Collaborators. M. Elizabeth Halloran Azhar Nizam Shufu Xu Depts. Biostatistics, U Wash and Emory U Derek Cummings Johns Hopkins U. Kumnuan Ungchusak

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Containing Pandemic Influenza at the Source

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  1. Containing Pandemic Influenza at the Source Ira M. Longini, Jr. Dept. Biostatistics U. Washington Hutchinson Rsh Ctr

  2. Collaborators M. Elizabeth Halloran Azhar Nizam Shufu Xu Depts. Biostatistics, U Wash and Emory U Derek Cummings Johns Hopkins U. Kumnuan Ungchusak Wanna Hanshaoworakul Thai Ministry of Health Timothy C. Germann Kai Kadau Catherine A. Macken Los Alamos National Laboratory

  3. How Bad Could it Get? • Current Avian A(H5N1) Influenza is SE Asia • 165 cases, 88 deaths, 53% case fatality ratio • Global pandemic, first wave about 6 - 9 months, 2 billion cases • 1918 scenario: 10 - 50 million deaths • Other scenarios: 2 – 7 million deaths • Contrast • 20 million AIDS deaths over 25 years • 811 SARS deaths over 8 months

  4. Containing Pandemic Influenza at the Source • It is optimal to stop a potential pandemic influenza strain at the source • Longini, et al.Science309, 1083-7 (2005). • Longini and Halloran. Science310, 1117‑18 (2005). • Ferguson, et al. Nature 437, 209-14 (2005) • Targeted antiviral prophylaxis with mobile stockpile (WHO) ~ 5 million courses • Quarantine, social distancing, school closing, travel restrictions • Pre or rapid vaccination with a possibly poorly matched vaccine

  5. Pandemic Influenza in the US or Other Countries Once Spread is Global • Hard to contain as it comes in • Once widespread, slow transmission until well-match vaccine is available • Targeted antiviral prophylaxis • Quarantine, social distancing, school closing, travel restrictions • Rapid vaccination with a possibly poorly match vaccine • Germann, T.C., Kadau, K., Longini I.M. and Macken C.A.: Mitigation strategies for pandemic influenza in the United States. (accepted – Feb or March, 2006) • Halloran, M.E. and Longini, I.M.: Community studies for vaccinating School children against influenza. Science 311 (Feb. 3, 2006).

  6. CONTACTS Household Household cluster Preschool/daycare School Workplace 80% ascertainment 80% school 100% household + HH cluster 80% preschool 60% workplace TAP: Targeted antiviral prophylaxis using neuraminidase inhibitors (oseltamivir/zanamivir)

  7. Antiviral efficacies used in the model: Oseltamivir • Antiviral efficacy of reducing susceptibility to infection: AVES = 0.48, [0.17, 0.67] 95% CI* • Antiviral efficacy of reducing illness given infection: AVED = 0.56, [0.10, 0.73] 95% CI* • Antiviral efficacy of reducing illness with infection: AVESD = 0.80, [0.35, 0.94] 95% CI* • Mult.: AVESD = 1 – (1- AVES) (1- AVED) = 0.77 • Antiviral efficacy of reducing infectiousness to others: AVEI = 0.80, [0.45, 0.93] 95% CI* *data from Welliver, et al. JAMA(2001); Hayden, et al. JID (2004); analysis by Yang, Longini, Halloran, Appl Stat (in print); Halloran, et al. (in prep).

  8. Prevaccination • Prevaccination with low efficacy vaccine • Low efficacy vaccine: VES = 0.30, VEI = 0.5 • 50% and 70% prevaccination of the population and evaluate above interventions

  9. Preliminaries

  10. Basic Reproductive Number: R0 • 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 ≈ 2.0 • New variant, early spread: 1 < R0 ≤ 1.6

  11. Reed-Frost ModelStochastic process: discrete state space and time t0, t1, t2 …. • Infectious agent natural history • Infectious for one time unit • Social contact structure • Random mixing • p = 1 – q, probability two people make contact sufficient to transmit • R0 = (n-1)p

  12. Reed-Frost Model See chain binomial chapter in the Encyclopedia Biostat., Vol 1, 593-7

  13. Reed-Frost Model Threshold theorem: When R0 1, then no epidemic, When R0>1, then epidemic with probability

  14. Simulated Reed-Frost Model* • Start with (S0,I0 ≥1) • For each S0,generate random number x  [0,1] • If x ≥qIo, then person becomes infected • Repeat for next generation and update states • Stop when S0= 0 or I0= 0 *First done by Elveback and Varma (1965)

  15. * *Source: Elveback and Varma (1965)

  16. Containment in SE Asia

  17. Rural population of 500,000 in Thailand Population matched to non-municipal area household-size and age distributions.* *Population and Housing Census 2000 data used where available (www.nso.go.th); other National Statistical Office reports and tables used as necessary.

  18. 12.5km 12.5km 12.5km 12.5km 12.5km 12.5km 12.5km • Population Characteristics • 36 localities each of size ~14,000 • Total area: 75 km X 75 km = 5,625 km2 • Population density ~89/km2

  19. Locality Characteristics • ~ 28 villages, each of size ~ 138 households, ~ 500 people • Villages are clustered • Within village clusters: • Household are clustered • Small & large playgroups • Elementary, lower-secondary and upper-secondary school mixing groups • Social groups • Work groups

  20. Social network incorporated from the Nang Rong District study* • 310 villages under study • Village size average  100 households • Main mixing groups under study • Households • Villages • Hiring tractors • Temples • Elementary schools • Secondary schools • Workplaces *Faust, et al., Soc Net (1999)

  21. Distribution of travel distance to work, school and social groups

  22. Zone6 Zone5 Zone4 Zone3 Zone2 Zone1 Secondary school, work and social group assignment • Localities are linked by secondary schools, work groups and social groups • For residents of each locality, secondary school, work group and social group locality is selected according to distance distribution shown below (using most Southwesterly locality as an example) • Zone % • 90 • 7 • 2 • 4-6 1

  23. Distribution of travel distance to work, school and social groups* For residents of most Southwesterly locality: • Zone % • 90 • 7 • 2 • 4-6 1 Zone6 1% go beyond zone 3 Zone5 Zone4 Zone3 2% go to zone 3 Zone2 7% go to zone 2 Zone1 90% stay in zone 1

  24. Model calibration Illness Attack Rate Modeled Asian A(H2N2) Pandemic HK-Like 1957-58 Strain ’68-69 Young Children 35% 32% 34% Older Children 62% 46% 35% Adults 24% 29% 33% Overall 33% 33% 34%

  25. Social Connectivity

  26. Transmission • c daily adequate contact probability • c(n-1) average mixing group degree • x transmission probability given adequate contact • y relative susceptibility • p = cxy overall transmission probability

  27. Bipartite Graph People Places 1 1 2 2 ••••• ••••• n m

  28. Weighted Person-to-Person Graph c12 1 2 c2n c2j c1n n 3 c3r c4s 4

  29. Mean degree 4.6 Clustering coefficient 0.2 Mean shortest path 10.6 Small World Network

  30. Infection Transmission Process

  31. Natural History Used for Influenza Probability of infecting others Case serial interval = 3.2 days Symptomatic (67%) Asymptomatic (33%) 0 days Latency 1.2d Incubation Possibly symptomatic 1.7d 3.5d Exposure and infection

  32. Intervention

  33. Interventions considered • All interventions carried out in the localities as triggered • 80% targeted antiviral prophylaxis (TAP) • 90% geographically targeted antiviral prophylaxis (GTAP) • Localized household and household cluster quarantine. Lifted when there are no more local cases.

  34. Interventions considered • TAP + pre-vaccination • TAP + localized household quarantine • TAP + localized household quarantine + pre-vaccination • Localized interventions begin 7, 14 and 21 days after outbreak is recognized, one day after as cases appear locally

  35. Results

  36. R0*: Number of people infected by a single initial infective Frequency Average R0=1.4 Number of secondary infections * Based on 1000 simulations

  37. No Intervention R0= 1.4 166,408 total cases Day # Cases 11 6 18 47 25 153 Day 11 18 25 No. of cases Day Day 11 18 25 No. of cases Day

  38. Illness attack rate by age-group and R0 (No Intervention) R0=2.4 R0=2.1 R0=1.7 R0=1.4 R0=1.1

  39. Contained GTAP 14 days after the first detected case(~ day 18) R0= 1.4 44 total cases Day 18 No. of cases Day Day 18 No. of cases Day

  40. Not contained GTAP 14 days after the first detected case(~ day 18) R0= 1.4 1925 total cases Day 18 No. of cases Day Day 18 No. of cases

  41. No Intervention, R0 = 1.4

  42. 80% TAP, 14 day delay, R0 = 1.4

  43. Simulated mean cases, escapes, courses and containment proportion for various interventions

  44. R0 sensitivity - cases

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