Fig 1.
Transmission chain and identical sequence clusters.
The branching process model creates transmission chains. Viral mutation splits the transmission chains into identical sequence clusters. The numbers indicate the size of the observed identical sequence clusters, i.e., the number of cases in the clusters that were tested and sequenced.
Fig 2.
Validation of the Bayesian inference model to estimate , k and
from the size distribution of identical sequence clusters.
(A) Estimate of the effective reproduction number . (B) Estimate of the dispersion parameter k. (C) Estimate of the testing probability
. True values are shown as black lines. For each parameter combination, we ran the model 10 times on 3,000 simulated clusters each. The generated samples of the posterior distributions are summarized by mean and 95% credible interval.
Fig 3.
Identical sequence cluster size distribution and sequencing coverage for SARS-CoV-2 in Switzerland, Denmark, and Germany in 2021.
(A) Range, mean, 90th percentile and 99th percentile of the identical sequence cluster size distribution and number of clusters by month based on data from GISAID. (B) Sequencing coverage (dots) and proportion of SARS-CoV-2 variants (background color) by month.
Fig 4.
Parameter estimates based on the size distribution of identical SARS-CoV-2 sequence clusters in Switzerland, Denmark and Germany in 2021.
A: Effective reproduction number . B: Dispersion parameter k. C: Testing probability. The estimates are based on monthly time windows of identical sequence clusters. For each month the estimated mean and 95% credible interval of the posterior distribution (in black) are shown.
values are compared to external estimates based on laboratory-confirmed cases (in blue, from github.com/covid-19-Re [7]).