Estimating antiviral effectiveness against pandemic influenza using household data

Abstract

Current estimates of antiviral effectiveness for influenza are based on the existing strains of the virus. Should a pandemic strain emerge, strain-specific estimates will be required as early as possible to ensure that antiviral stockpiles are used optimally and to compare the benefits of using antivirals as prophylaxis or to treat cases. We present a method to measure antiviral effectiveness using early pandemic data on household outbreak sizes, including households that are provided with antivirals for prophylaxis and those provided with antivirals for treatment only. We can assess whether antiviral drugs have a significant impact on susceptibility or on infectivity with the data from approximately 200 to 500 households with a primary case. Fewer households will suffice if the data can be collected before case numbers become high, and estimates are more precise if the study includes data from prophylaxed households and households where no antivirals are provided. Rates of asymptomatic infection and the level of transmissibility of the virus do not affect the accuracy of these estimates greatly, but the pattern of infectivity in the individual strongly influences the estimate of the effect of antivirals on infectivity. An accurate characterization of the infectiousness profile--informed by strain-specific data--is essential for measuring antiviral effectiveness.

Figure 1

(a–c) Infectiousness over time with (dashed curves: (a) intervention prior to infection, (b) intervention upon symptom onset, (c) intervention at time T _A) and without (solid curves) antivirals. The plots show the effect of providing antivirals at different times in an individual's infectious period on their infectiousness.

Figure 2

Estimates of parameters f _T (effect of antivirals on infectivity), σ (effect of antivirals on susceptibility), θ (quantifies within-household transmission) and s (quantifies transmission from outside the household) for three different intervention strategies of a study comprising 500 households. In each case, the circles and lines show the estimates and confidence intervals, while the crosses show the true values. (a) Prophylaxis and treatment in households, (b) treatment only in households and (c) equal combination of the above two strategies. Parameters used to generate the data were: λ=4, μ=5, T _I=2, f _T=0.73, σ=0.5, θ=0.7 and s=0.98, which correspond to fairly low transmission rates.

Figure 3

Estimates of parameters f _T (effect of antivirals on infectivity), σ (effect of antivirals on susceptibility), θ (quantifies within-household transmission) and s (quantifies transmission from outside the household) for four different intervention strategies of a study comprising 500 households. In (a), all intervention times are included equally; in (b), none of the primary cases were given antivirals prior to infection; in (c), there were no households included in which antivirals were not provided; and in (d), individuals were either given antivirals at (or before) symptom onset or not at all. In each case, the circles and lines show the estimates and confidence intervals, while the crosses show the true values. Parameters used to generate the data were: λ=4, μ=5, T _I=2, f _T=0.73, σ=0.5, θ=0.7 and s=0.98, which correspond to fairly low transmission rates.

Figure 4

The percentage of 100 trials in which the computed 95% confidence interval did not include 1 for each parameter for (a) low and (b) high transmission rates, and for increasing numbers of households (100–1000). Black bars, 100; dark grey bars, 200; light grey bars, 500; white bars, 1000. Parameters f _T and σ measure the effect of antivirals on infectivity and susceptibility (respectively), while θ and s are the escape probabilities for infection from within and outside the household. Parameters used to generate the data were: λ=4, μ=5, T _I=2, f _T=0.73, σ=0.5, with θ=0.7 and s=0.98 for low transmission, and θ=0.5 and s=0.96 for high transmission.