Absolute humidity and pandemic versus epidemic influenza

Abstract

Experimental and epidemiologic evidence indicates that variations of absolute humidity account for the onset and seasonal cycle of epidemic influenza in temperate regions. A role for absolute humidity in the transmission of pandemic influenza, such as 2009 A/H1N1, has yet to be demonstrated and, indeed, outbreaks of pandemic influenza during more humid spring, summer, and autumn months might appear to constitute evidence against an effect of humidity. However, here the authors show that variations of the basic and effective reproductive numbers for influenza, caused by seasonal changes in absolute humidity, are consistent with the general timing of pandemic influenza outbreaks observed for 2009 A/H1N1 in temperate regions, as well as wintertime transmission of epidemic influenza. Indeed, absolute humidity conditions correctly identify the region of the United States vulnerable to a third, wintertime wave of pandemic influenza. These findings suggest that the timing of pandemic influenza outbreaks is controlled by a combination of absolute humidity conditions, levels of susceptibility, and changes in population-mixing and contact rates.

Figure 1.

Influenza virus survival, transmission, and the basic reproductive number, R₀, plotted as a function of absolute humidity. Influenza virus survival data are from Harper (30), influenza virus transmission data are from Lowen et al. (31, 32), and R₀ is based on best-fitting, absolute humidity-forced, susceptible-infected-recovered susceptible simulations from Shaman et al. (2). The solid line is R₀ for the best-fitting simulation; the gray region shows the range of R₀ values as a function of absolute humidity for the 10 best-fitting simulations. The measure of absolute humidity is 2 m above-ground specific humidity in kg/kg and is taken from National Center for Environmental Prediction–National Center for Atmospheric Research (NCEP-NCAR) reanalysis (23).

Figure 2.

Time series of simulated epidemic influenza in New York State from an absolute humidity-forced, susceptible-infected-recovered susceptible (SIRS) model. Simulation is shown from July 1987 through December 1990 for the best-fitting parameter combination (Table 1). The SIRS model simulates 2 influenza subtypes (A/H3N2 and A/H1N1), but only the time series for A/H3N2 is shown. Plotted lines indicate the steady rise of susceptibility (S(t)/N) to A/H3N2 in the time between outbreaks (thick gray line); the seasonal cycle of the basic reproductive number, R₀(t) (thin black line), due to seasonal changes in absolute humidity plus shorter time-scale variability due to changes in absolute humidity due to weather variability; the time series of the effective reproductive number, R_E(t) (thin gray line); and the time series of infection rate (proportion infected × 10, thick black line). During outbreaks, both S(t) and R_E(t) drop precipitously as susceptibles are infected and S(t) decreases. Once R_E(t) drops below 1, the outbreak begins to abate. Of note, A/H3N2 was not present in the simulation from April 1988 to March 1989; hence, no outbreak was possible during this winter (A/H1N1 was present and is not shown).

Figure 3.

Time series of New York City observed specific humidity, estimated basic reproductive number, and estimated effective reproductive number for 1948–2008 and 2009. Top, plots of observed specific humidity, q(t), and estimated basic reproductive number, R₀(t), from the susceptible-infected-recovered susceptible (SIRS) model best-fitting parameter combination. Bottom, plots of estimated effective reproductive number, R_E(t), for various population susceptibility levels; the dash-dot line shows R_E(t) = 1.

Figure 4.

Distributed maps of estimated and projected upper-bound effective reproductive number, R_E(t), for 2009 A/H1N1 in the United States during the winter of 2009–2010. A, 2009 week 47–49 estimates of R_E(t) (from Table 2); B, the ratio of projected 2010 week 1–3 R_E(t) to 2009 week 47–49 estimates of R_E(t) showing the proportional change of R_E(t); C, as for B, but for projected 2010 week 4–6 R_E(t); D–F, 3-week projections of upper-bound R_E(t) made by using the 2009 week 47–49 estimates of susceptibility and estimates of 3-week average basic reproductive number, R₀(t). Both R₀(t) and the upper-bound estimates of R_E(t) were made by using 2-m above-ground specific humidity, q(t), from National Center for Environmental Prediction–National Center for Atmospheric Research (NCEP-NCAR) reanalysis (2). R₀(t) was calculated by using equation 4 of Shaman et al. (2) and the best-fit susceptible-infected-recovered susceptible (SIRS) parameter estimates of maximum and minimum basic reproductive number. R_E(t) was calculated per equation 3. D, 2009 week 50–52 projections of R_E(t); E, 2010 week 1–3 projections of R_E(t); F, 2010 week 4–6 projections of R_E(t).