Höhle, M. | bioRxiv Science

bioRxiv2026

Experimental reproduction numbers disentangle vaccine effects on susceptibility and infectiousness during H5N1 transmission in geese

Vaccination against high pathogenicity avian influenza virus (HPAIV) is increasingly used to protect poultry, but vaccine performance is commonly inferred from clinical protection and virus shedding rather than measured transmission. We asked whether reproduction numbers from controlled transmission experiments can quantify how vaccination changes susceptibility and infectiousness. Domestic geese were prime-boost vaccinated with an H5 clade 2.3.4.4b RNA-replicon vaccine and challenged with homologous HPAIV H5N1. Replicated seeder-sentinel groups represented transmission among unvaccinated animals, to vaccinated contacts, and from vaccinated breakthrough-infected animals. Vaccinated directly challenged geese remained clinically protected although all became RT-qPCR-positive. Estimated reproduction numbers were R0=4.7 (95% CI, 2.8-8.0) among unvaccinated geese, Rs=2.6 (1.6-4.5) for transmission to vaccinated contacts, Ri=3.7 (2.0-6.8) for transmission from vaccinated infected geese, and Rvacc=2.0 (0.8-4.9) for a fully vaccinated population. Vaccination reduced transmission but did not reduce the point estimate for Rvacc below one under these intensive exposure conditions. Vaccinated infected geese also shed substantially less viral RNA, whereas the estimated reduction in infectiousness was more modest, indicating that RNA shedding alone may not reliably predict transmission reduction. Experimental reproduction numbers therefore provide a direct population-level complement to conventional vaccine endpoints and separate effects on susceptibility from effects on onward transmission.

microbiology↗

bioRxiv2020

Parallelized calculation of permutation tests

MotivationPermutation tests offer a straight forward framework to assess the significance of differences in sample statistics. A significant advantage of permutation tests are the relatively few assumptions about the distribution of the test statistic are needed, as they rely on the assumption of exchangeability of the group labels. They have great value, as they allow a sensitivity analysis to determine the extent to which the assumed broad sample distribution of the test statistic applies. However, in this situation, permutation tests are rarely applied because the running time of naive implementations is too slow and grows exponentially with the sample size. Nevertheless, continued development in the 1980s introduced dynamic programming algorithms that compute exact permutation tests in polynomial time. Albeit this significant running time reduction, the exact test has not yet become one of the predominant statistical tests for medium sample size. Here, we propose a computational parallelization of one such dynamic programming-based permutation test, the Green algorithm, which makes the permutation test more attractive. ResultsParallelization of the Green algorithm was found possible by nontrivial rearrangement of the structure of the algorithm. A speed-up - by orders of magnitude - is achievable by executing the parallelized algorithm on a GPU. We demonstrate that the execution time essentially becomes a non-issue for sample sizes, even as high as hundreds of samples. This improvement makes our method an attractive alternative to, e.g., the widely used asymptotic Mann-Whitney U-test. AvailabilityIn Python 3 code from the GitHub repository https://github.com/statisticalbiotechnology/parallelPermutationTest under an Apache 2.0 license. Contactlukask@kth.se Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗