Sun Chaoyue, Fang Ruogu, Salemi Marco, Prosperi Mattia, Rife Magalis Brittany
Department of Electrical and Computer Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States of America.
J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States of America.
PLoS Comput Biol. 2024 Apr 10;20(4):e1011351. doi: 10.1371/journal.pcbi.1011351. eCollection 2024 Apr.
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.
在疫情爆发或持续流行期间，可靠预测传播风险和传播模式对于指导公共卫生项目至关重要。预测特定风险群体中的传播增长或下降情况有助于优化干预措施，尤其是在资源有限的情况下。系统发育树已广泛用于检测传播链和高风险人群。此外，树形拓扑结构以及种群参数的纳入（系统发育动力学）有助于重建个体间疫情在时空上的进化动态。我们现在展示系统发育动力学树在传播建模和预测中的效用，开发一种基于系统发育的深度学习系统，称为深度动态预测（DeepDynaForecast）。我们的方法利用了具有捷径多层聚合的原始对偶图学习结构，适用于新兴高风险群体中传播动态的早期识别和预测。我们使用模拟疫情数据展示了深度动态预测的准确性，并使用2012年至2020年佛罗里达州人类免疫缺陷病毒疫情的实证大规模数据展示了所学模型的效用。我们的框架作为开源软件（麻省理工学院许可）可在github.com/lab-smile/DeepDynaForcast获取。
