Sample size considerations for one-to-one animal transmission studies of the influenza A viruses. 
Nishiura H, Yen H-L, Cowling BJ (2013) 
PLoS ONE 8(1): e55358. doi:10.1371/journal.pone.0055358

A very detailed scientific analysis of the number of ferrets needed to generate statistically significant results. 

“The most important caveat in the present study in relation to the common practice is that n = 3 is not enough to show a significant difference as well as Ro>1 for one-sample comparison while it can demonstrate Ro>0. Moreover, comparing a group with n = 3 against a reference group with the same sample size does not allow researchers to demonstrate any significant difference in the transmissibility between two sample groups. If two samples have to be compared, n = 4 would be regarded as minimum, and moreover, k = n for n = 4 and k = n or k= n-1 for n = 5, respectively, would have to be required along with the absence of infected pairs in the control group.”

More prosaically, this says that in an influenza A virus transmission experiment the use of three pairs of ferrets (infected donor and uninfected receiver animal, n = 3) is enough to demonstrate transmission (Ro>0) but not to know whether the virus would spread (Ro>1). If a GOF flu virus is to be compared to a another, say a human influenza virus, a minimum of four pairs of ferrets (n = 4) with all four receiver ferrets becoming infected (k = 4, and hence k = n), would be necessary. If five ferrets were used (n = 5) then a significant result would be obtained if 4 of 5 receiver ferrets were infected (k = n-1 for n = 5).

As n=2 or 3 in the Fouchier and Kawaoka experiments little can be deduced except to say that the viruses were transmitted. Nothing can be said about their potential to spread. In short the conclusions are qualitative.

January 3, 2013

Safety survey reveals lab risks
Van Noorden R.
Nature. 2013 Jan 3;493(7430):9-10. doi: 10.1038/493009a.

The study, which was partly financed by Nature, shows that scientists are overconfident and systematically underestimate risk. This is why scientists need help form outside in assessing risk.