Quantifying Uncertainty in Deep Spatiotemporal Forecasting

1 code implementation • 25 May 2021 • Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu

Deep learning is gaining increasing popularity for spatiotemporal forecasting.

Decision Making Uncertainty Quantification

DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting

no code implementations • 12 Feb 2021 • Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu

We introduce DeepGLEAM, a hybrid model for COVID-19 forecasting.

Uncertainty Quantification

Predicting seasonal influenza using supermarket retail records

1 code implementation • 8 Dec 2020 • Ioanna Miliou, Xinyue Xiong, Salvatore Rinzivillo, Qian Zhang, Giulio Rossetti, Fosca Giannotti, Dino Pedreschi, Alessandro Vespignani

In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting.

Inferring high-resolution human mixing patterns for disease modeling

1 code implementation • 25 Feb 2020 • Dina Mistry, Maria Litvinova, Ana Pastore y Piontti, Matteo Chinazzi, Laura Fumanelli, Marcelo F. C. Gomes, Syed A. Haque, Quan-Hui Liu, Kunpeng Mu, Xinyue Xiong, M. Elizabeth Halloran, Ira M. Longini Jr., Stefano Merler, Marco Ajelli, Alessandro Vespignani

Mathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics.

Populations and Evolution Physics and Society Quantitative Methods