102 citations · 151 across the 4 of their papers we have counts for
6 papers
Quantifying Uncertainty in Deep Spatiotemporal Forecasting
Dongxia Wu, Liyao Gao, Xinyue Xiong +4
Deep learning is gaining increasing popularity for spatiotemporal forecasting. However, prior works have mostly focused on point estimates without quantifying the uncertainty of th…
DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting
Dongxia Wu, Liyao Gao, Xinyue Xiong +4
We introduce DeepGLEAM, a hybrid model for COVID-19 forecasting. DeepGLEAM combines a mechanistic stochastic simulation model GLEAM with deep learning. It uses deep learning to lea…
Finding Patient Zero: Learning Contagion Source with Graph Neural Networks
Chintan Shah, Nima Dehmamy, Nicola Perra +4
Locating the source of an epidemic, or patient zero (P0), can provide critical insights into the infection's transmission course and allow efficient resource allocation. Existing m…
A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models
Dianbo Liu, Leonardo Clemente, Canelle Poirier +5
We present a timely and novel methodology that combines disease estimates from mechanistic models with digital traces, via interpretable machine-learning methodologies, to reliably…
Inferring high-resolution human mixing patterns for disease modeling
Dina Mistry, Maria Litvinova, Ana Pastore y Piontti +12
Mathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics. The growing need for…
The Emergence of Innovation Complexity at Different Geographical and Technological Scales
Emanuele Pugliese, Lorenzo Napolitano, Matteo Chinazzi +1
We define a novel quantitative strategy inspired by the ecological notion of nestedness to single out the scale at which innovation complexity emerges from the aggregation of speci…