Training and test data with scripts for simulation-trained deep learning and likelihood-based phylogeography comparisons

Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are computationally expensive, which limits the complexity and realism of phylodynamic models and makes them ill-suited for informing policy decisions in real-time during rapidly developing outbreaks. Likelihood-free methods using deep learning are pushing the boundaries of inference beyond these constraints. In this paper, we extend, compare and contrast a recently developed deep learning method for likelihood-free inference from trees. We trained multiple deep neural networks using phylogenies from simulated outbreaks that spread among five locations and found they achieve similar levels of accuracy to Bayesian inference under the true simulation model. We compared robustness to model misspecification of a trained neural network to that of a Bayesian method. W..., , , # System and Software:

All experiments were run on the following platform with the corresponding software versions:

* **Simulations**: On an AWS EC2 instance running Ubuntu 18.04.6 LTS (GNU/Linux 5.4.0-1092-aws x86_64).
* **Simulation Experiment Results Analyses**:
* Platform: x86_64-pc-linux-gnu (64-bit) on Windows Subsystem for Linux v2.
* Running under: Ubuntu 20.04.3 LTS.

### Training and Other Large Data Files

* Also available on github without large data files: .

### Software and Libraries

* **Beast 2 v2.6.3** with the package MASTER v6.1.2 installed for simulation.
* Assumes the executable beast2 is in a directory in your $PATH.
* In a directory in your path, make the following soft link: ln -s /path/to/bin/beast beast2
* **R, Rscript v4.1.1** with the following libraries for simulation and analysis:
* vioplot v0.3.7
* expm v0.999.6
* BEST v0.5.4
* phytools v0.7.90
* rjson v0.2.20
* **Python3 v3.8.10** with t...