Figure 3. Weighted logistic regression predicting the probability of a supercritical classification based on ferret SAR.

(A) Respiratory droplet SAR and (B) direct contact SAR. Solid black line gives the fit of the weighted logistic regression, where model weights are given by the number of ferrets in each experiment. Dashed black lines give the 95% confidence interval on the model predictions. Shading in the prediction interval represents values of SAR for which the 95% confidence intervals for predicted model fit do not overlap a probability of 0.5 (the dashed red line) indicating a high probability of being supercritical (red shading) or subcritical (blue shading). The gray shading represents SAR values where the 95% CI on the prediction overlaps 0.5, providing equivocal classification. Circles show the individual ferret SAR estimates (See Figure 1—source data 2, 3) for supercritical (top in red) and subcritical viruses (bottom in blue).

DOI: http://dx.doi.org/10.7554/eLife.07969.011

Figure 3—figure supplement 1. Comparison of ferret SAR via respiratory droplet and direct contact transmission for single influenza isolates.

Each point represents a single set of experiments that tested an isolate for transmission in ferrets under both respiratory droplet and direct contact transmission with other experimental protocols held fixed. Isolates belonging to subcritical subtypes are depicted by blue squares, and supercritical subtypes are depicted by red circles. Note that some points are jittered for clarity (see Figure 1—source data 2, 3 for full data).

DOI: http://dx.doi.org/10.7554/eLife.07969.012

Figure 3—figure supplement 2. Effect of uncertainty in ferret SAR on its relationship with the probability of being classified as supercritical.

(A) Respiratory droplet SAR and (B) direct contact SAR. To assess the impact of binomial uncertainty in ferret SAR estimates, we simulated 1000 datasets by taking binomial samples from each data point. Here, the binomial probability for each was given by the observed SAR and the number of trials was the number of ferrets used. To introduce binomial uncertainty into those experiments with an SAR of 0 or 1, we set the binomial probability to 0.1 or 0.9, respectively. The solid line is the average model fit to all of the simulated datasets and is nearly identical to that in Figure 3. Dashed lines give the 97.5 and 0.025 percentiles of the upper and lower bounds, respectively, of the 95% confidence intervals on model predictions from each of the simulated datasets. These indicate much more uncertainty in model predictions across datasets that generates a larger equivocal region of ferret SARs than observed in Figure 3. However, values of ferret SAR indicative of subcritical and supercritical strains still exist, indicating that our qualitative results are robust to binomial uncertainty.

DOI: http://dx.doi.org/10.7554/eLife.07969.013

Figure 3—figure supplement 3. ROC curves for classifying pandemic potential using different definitions of transmission and transmission routes.

Receiver operating characteristic (ROC) curves and area under the curve (AUC) using (A) seroconversion and/or viral isolation or (B) viral isolation alone as evidence for transmission in ferrets when classifying influenza isolates as either supercritical or subcritical in humans. Lines indicate ferret respiratory droplet SAR (red) or ferret direct contact SAR (black). Curves were calculated from raw data shown in Figure 3, using a range of SAR classification thresholds from 0 to 1. Numbers indicate the threshold values for which the true positive rate (i.e. the sensitivity) and false positive rate (i.e. the complement of the specificity) changed. Threshold values intermediate to any of those depicted have true positive and false positive rates identical to that of the next lowest value shown. The dashed gray line is the one-to-one line corresponding to random classification. AUC values are shown in the figure legend with higher values corresponding to higher predictive power.

DOI: http://dx.doi.org/10.7554/eLife.07969.014