Online appendix and 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. 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 PyBDEI and the theoretical
findings behind it are described in A Zhukova, F Hecht, Y Maday, and O
Gascuel. Fast and Accurate Maximum-Likelihood Estimation of Multi-Type
Birth-Death Epidemiological Models from Phylogenetic Trees Syst Biol 2023.
This dataset contains the online Appendix (Fig S1-S3 and Table S1).