Simulated data sets for assesment of Birth-Death Exposed-Infectious (BDEI) phylodynamic model estimators

The birth-death exposed-infectious (BDEI) phylodynamic model describes the transmission of pathogens featuring an incubation period (when there is a delay between the moment of infection and becoming infectious, as for Ebola and SARS-CoV-2), and permits its estimation along with other parameters, from time-scaled phylogenetic trees. We implemented a highly parallelizable estimator for the BDEI model in a maximum likelihood framework (PyBDEI) using a combination of numerical analysis methods for efficient equation resolution. Our implementation estimates epidemiological parameter values and their confidence intervals in four minutes on a phylogenetic tree of 10,000 samples. This dataset contains the assessment of PyBDEI in comparison with a Bayesian implementation in BEAST2 (mtbd package) and a deep learning estimator PhyloDeep: the parameter values estimated by the 3 tools.The data are uploaded as zip archives. All files within the archives can be opened with a text editor.Simulated data We assessed the performance of our estimator on two data sets from Voznica et al. 2021 (accessible at doi.org/10.5281/zenodo.7358555): medium, a data set of 100 medium-sized trees (200−500 tips) large, a data set of 100 large trees (5,000−10,000 tips). The data were downloaded from github.com/evolbioinfo/phylodeep (also accessible at doi.org/10.5281/zenodo.7358555, under GNU GPL v3 licence). To produce medium trees, Voznica et al. generated 10,000 trees with 200−500 tips under the BDEI model, with the parameter values sampled uniformly at random within the following boundaries: incubation period 1/µ ∈ [0.2, 50] basic reproductive number R0 = λ/ψ ∈ [1, 5] infectious period 1/ψ ∈ [1, 10]. Then randomly selected 100 out of those 10,000 trees to evaluate them with the gold standard method, BEAST2. For 100 large tree generation, the same parameter values as for the 100 medium ones were used, but the tree size varied between 5000 and 10 000 tips. PhyloDeep's maximal pre-trained tree size is 500, however, for larger trees, it estimates BDEI parameters by extracting the largest non-intersecting set of subtrees of sizes covered by the pre-trained set (50–500 tips), estimating parameters on each of the subtrees independently, and averaging each parameter's value over the subtrees (weighted by subtree sizes). To compare our method's performance on the forest of such subtrees, we generated a large forest data set by dissecting each large tree into subtrees with Phylo Deep. As the BDEI model requires one of the parameters to be fixed in order to become asymptomatically identifiable, ρ was fixed to the real value. Data preparation and parameter estimation pipelines are available at github.com/evolbioinfo/bdei This dataset contains: a forest of subtrees for each large tree: large/cluster.[0-99].nwk the estimated and real parameter values for fixed ρ: medium/estimates.tab and large/estimates.tab tab-separated table