Preliminary estimates of R E for influenza in the US from ILI data

1. BARDA PRESENTATION 16 December 2009 Preliminary estimates of RE for influenza in the US from ILI data Edward Goldstein, Marc Lipsitch

2. Estimating RE from weeks 46-48: ILI ILI% from CDC sitrep Estimate r= exponential decay rate Assume mean serial interval Tg = 2.3 RE≤ exp(rTg) RE≤ 0.8-1.0 for end Nov. Declining in most regions

3. Estimating RE from weeks 46-48: flu visits ILI% from CDC sitrep % of flu tests positive from CDC sitrep “flu visits” = ILI% * % positive (caveats) Same estimation procedure RE≤ 0.6-1.0 for end Nov. Declining in most regions

4. Epidemic peaking associated with 40% decline in relative importance of school-age children from peak share of ILI visits Source: CDC Sitrep

5. Seasonal variation in reproductive number of influenza Jeffrey Shaman1, Virginia Pitzer2,3, Cecile Viboud3, Bryan Grenfell2,3,4, Marc Lipsitch5 1 Oregon State University, 2 Penn State University, 3 NIH/Fogarty, 4 Princeton University, 5 Harvard School of Public Health

6. Absolute humidity affects flu virus survival and transmission Survival Transmission Data of Harper (1961) Data of Lowen (2008) Shaman & Kohn (2009)

7. Is AH a key driver of the epidemiology of influenza in the US? Is the onset of flu season correlated, in each state, with unseasonably dry conditions? Can an SIRS model parameterized to vary R0 as a function of AH reproduce seasonal patterns by state? DATA: Weekly excess P&I mortality by state. Daily specific humidity averaged over each state.

8. Unseasonably Dry Weather Precedes Flu Season Onset Anomalies - deviations from mean daily conditions Average daily AH anomalies (~1200 onset events) are negative during the period 3-4 weeks prior to onset (p < 0.0005) Depending on the threshold, 55-60% of onset dates have negative AH anomalies averaged over the 4 weeks prior onset. Negative AH anomalies are not necessary for onset but instead presage an increased likelihood of onset Negative AH anomalies boost virus survival and transmission above typical local wintertime levels and may further facilitate virus spread

9. SIRS Model with AH as driver Stochastic, compartmental SIRS Model, no age structure, 2 strains 4free parameters: 2 for AH- R0 relationship, duration of infectiousness (D) and immunity (L), then CFR-scaling Stochastic seeding of influenza all year p=0.1/day Fit to daily mean excess P&I deaths for each state 5000 randomly chosen parameter sets fit by least squares Fit to individual states, and common fit to AZ, WA, NY, IL, FL, cross validated to other states

10. Top 10 parameter fits are reasonable and fairly consistent

11. AH, R0 and RE

12. Model fits: 5 states Best 10 statewise fits capture seasonal cycle, Dec-Jan and Feb-Mar peaks Cross-validation of best common fit to other 43 contiguous + DC is successful (worst fits to low-workflow states, likely to be anomalous per Viboud et al. Science 2006)

13. Model caveats • No age structure • Fit to P&I mortality • Strains modeled very simply • Some seasonal variation may be due to schools • Unlikely to be the whole story, but may account for some humidity effects

14. Why we think AH is a key driver of flu Empirical (and model) association of unseasonably low AH with flu season onset Relative consistency of response to AH in 5 individual states Cross validation of common fit to these 5 states shows a good fit to other 44 AH fits better than RH or temperature in lab RH cycle outdoors is opposite to that predicted by lab experiments (expect summer peak) Solar insolation has same problem: anomaly analysis shows high insolation = flu onset; predicts summer peak Temperature varies little indoors

15. Why we don’t think school terms are the whole story Clearly school terms affect flu transmission (Longini, Cauchemez) Seasonal cycle attenuated in the tropics, where there are school terms, but less AH variation School term only model fits considerably less well School term only model requires 40-90% change in transmission, larger than Cauchemez et al.

16. Model predictions • R0 (also RE) increases ~11% (range 2-17%) from 12/1 to peak in a typical year • Peaks mainly Jan-Feb • Caveats: • this fall was unseasonably warm/wet in many places (next slide) • In a typical year there are several day excursions of predicted R0 +- 10% from seasonal average

17. 2009 was unusually humid especially in NE:R0 has been 10-15% below its seasonal mean

18. Bottom line R0is typically ~11% higher at its Jan-Feb peak than on Dec. 1 2009 autumn wet and warm: R0 perhaps 10-15% below its seasonal mean If winter 2009-10 is typical, R0 may be up to 20-25% higher than Dec 1, and may for several days go up to 30-35% higher Regional variation both in R0 so far, and in weather during the winter will depart from these means

19. Combining these analyses RE nationally about 0.8-0.9 or less at week 48 (includes Dec. 1) Expect 10-25% increase in RE from Dec. 1 to peak, with possibly up to 30-35% increase sustained for several days RE<1.1 consistently, probably RE<1.0 nationally for winter Regional variation expected Continuing vaccination will further reduce RE Clusters of unexposed, unvaccinated (eg elderly) may still be at risk for outbreaks Cautiously optimistic, but this is flu, so bet 20% on whatever the models say won’t happen