Deep learning of contagion dynamics on complex networks

Abstract

Forecasting the evolution of contagion dynamics is still an open problem to which mechanistic models only offer a partial answer. To remain mathematically or computationally tractable, these models must rely on simplifying assumptions, thereby limiting the quantitative accuracy of their predictions and the complexity of the dynamics they can model. Here, we propose a complementary approach based on deep learning where effective local mechanisms governing a dynamic on a network are learned from time series data. Our graph neural network architecture makes very few assumptions about the dynamics, and we demonstrate its accuracy using different contagion dynamics of increasing complexity. By allowing simulations on arbitrary network structures, our approach makes it possible to explore the properties of the learned dynamics beyond the training data. Finally, we illustrate the applicability of our approach using real data of the COVID-19 outbreak in Spain. Our results demonstrate how deep learning offers a new and complementary perspective to build effective models of contagion dynamics on networks.

A graph convolutional network for predicting COVID-19 dynamics in 190 regions/countries

“…COVID-19 is transmitted from person to person along with human mobility via public transportation ( 6 – 8 ) and in local environment. It is important to model contagion dynamics on complex networks ( 16 ). To address this issue, we proposed a graph convolutional network (GCN) model that captures latent geographical flow of people via a public transportation network represented as a graph comprising nodes and edges.…”

Predicting COVID-19 positivity and hospitalization with multi-scale graph neural networks

“…Recently, there has been an increasing interest in machine learning and artificial intelligence approaches to address the limitations of mathematical models 7,8 . This approach involves training predictive models on collected real-world data.…”

Effective vaccination strategy using graph neural network ansatz

“…GNN has become a widely used method for network analysis because of its convincing performance in various fields, such as estimation of molecular properties [30,31], drug discovery [32], and traffic forecasting [33,34]. In the epidemic field, GNNs have been employed for the prediction of disease prevalence [35][36][37], identification of patient zero [38], and estimation of epidemic state using limited information [39]. Few studies have developed dynamic epidemic control schemes that identify epidemic hotspots from partially observed epidemic state of each individual [40,41].…”