Sensitivity of the DeepDynaForecast model to cluster size and risk group transmission type.

A: Balanced accuracy when predicting leaves within groups of different external node sizes. Binned intervals for quantitative data were generated using eight quantiles. Left panel: model performance among all groups. Middle panel: model performance on decaying clusters only. Right panel: model performance on growing clusters only. B: Performance of varying risk groups in ARI and TB simulations (see Supplementary S1 Table).</p

Distribution of the edge features on TB simulations.

a, and b, are distributions of the raw edge features: time and genetic distance. c, and d, are distributions of the edge features processed by an ArcSinh transformation and a z-score normalization. (TIFF)</p

Summary statistics for edge features in TB.

In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of transmission growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used in the detection of transmission chains and high-risk populations. Moreover, tree topology and the incorporation of population parameters (phylodynamics) can be useful in reconstructing the evolutionary dynamics of an epidemic across space and time among individuals. We now demonstrate the utility of phylodynamic trees for transmission modeling and forecasting, developing a phylogeny-based deep learning system, referred to as DeepDynaForecast. Our approach leverages a primal-dual graph learning structure with shortcut multi-layer aggregation, which is suited for the early identification and prediction of transmission dynamics in emerging high-risk groups. We demonstrate the accuracy of DeepDynaForecast using simulated outbreak data and the utility of the learned model using empirical, large-scale data from the human immunodeficiency virus epidemic in Florida between 2012 and 2020. Our framework is available as open-source software (MIT license) at github.com/lab-smile/DeepDynaForcast.</div

Label distribution on ARI simulations.

a-c are histograms of the number of static, decay, and growth nodes among the trees, and d-f show the distribution of classes’ ratio. Plot g is the histogram of the number of nodes on the trees. (TIFF)</p

Florida HIV-1 subtype B <i>pol</i> sequence phylogeny (2012–2017).

The maximum likelihood phylogeny was generated as described in Rich et al. [19] for 27, 115 partial pol sequences sampled from individuals across the state of Florida, for whom metadata were provided. County of residence, categorized according to EHE prioritization, is shown in the corresponding heatmap. Cluster status for each external branch according to MicrobeTrace [33] and dynamic prediction using the DeepDynaForecast are also shown. Branches are scaled in substitutions/site.</p

Summary statistics for edge features in ARI.

In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of transmission growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used in the detection of transmission chains and high-risk populations. Moreover, tree topology and the incorporation of population parameters (phylodynamics) can be useful in reconstructing the evolutionary dynamics of an epidemic across space and time among individuals. We now demonstrate the utility of phylodynamic trees for transmission modeling and forecasting, developing a phylogeny-based deep learning system, referred to as DeepDynaForecast. Our approach leverages a primal-dual graph learning structure with shortcut multi-layer aggregation, which is suited for the early identification and prediction of transmission dynamics in emerging high-risk groups. We demonstrate the accuracy of DeepDynaForecast using simulated outbreak data and the utility of the learned model using empirical, large-scale data from the human immunodeficiency virus epidemic in Florida between 2012 and 2020. Our framework is available as open-source software (MIT license) at github.com/lab-smile/DeepDynaForcast.</div

Distribution of the edge features on ARI simulations.

a, and b, are distributions of the raw edge features: time and genetic distance. c, and d, are distributions of the edge features processed by an ArcSinh transformation and a z-score normalization. (TIFF)</p

Ranging complexity of tree topological features resulting from a structured infected population.

n transmission clusters, distinct from the background population (maroon), can vary in effective reproductive number (Re) over time (x-axis) and rate of infection (m) by or to individuals from other groups. Variations in transmission dynamics are imprinted in branch patterns within the corresponding phylogeny and can aid in identifying groups of interest.</p

Simulation information for each outbreak and risk group.

Simulation information for each outbreak and risk group.</p

Figurative performance comparison of five models on combined ARI and TB test sets.

A: Confusion matrices with row-wise normalized elements. B: One-verse-rest receiver operator characteristic curve (ROC) for each class and a macro averaged ROC curve with magenta dash lines. The corresponding AUCs are indicated for each curve. C: UMAP visualization of aggregation of learned node representations in message-passing iterations. Plots were generated in randomly sampled 50 phylogenetic trees in ARI and TB test sets.</p