Causalgnn: Causal-based graph neural networks for spatio-temporal epidemic forecasting

L Wang, A Adiga, J Chen, A Sadilek… - Proceedings of the …, 2022 - ojs.aaai.org
Infectious disease forecasting has been a key focus in the recent past owing to the COVID-
19 pandemic and has proved to be an important tool in controlling the pandemic. With the …

A survey of deep learning and foundation models for time series forecasting

JA Miller, M Aldosari, F Saeed, NH Barna… - arXiv preprint arXiv …, 2024 - arxiv.org
Deep Learning has been successfully applied to many application domains, yet its
advantages have been slow to emerge for time series forecasting. For example, in the well …

Time-conditioned dances with simplicial complexes: Zigzag filtration curve based supra-hodge convolution networks for time-series forecasting

Y Chen, Y Gel, HV Poor - Advances in Neural Information …, 2022 - proceedings.neurips.cc
Graph neural networks (GNNs) offer a new powerful alternative for multivariate time series
forecasting, demonstrating remarkable success in a variety of spatio-temporal applications …

[HTML][HTML] Statistical study for Covid-19 spread during the armed crisis faced by Ukrainians

M Kamal, MN Atchadé, YM Sokadjo, E Hussam… - Alexandria Engineering …, 2023 - Elsevier
Russia and Ukraine got into an armed conflict on 24 th February 2022. In addition, the World
Health Organisation still warns of a fast growth in infections and deaths. Infectious disease …

Graph Neural Network for spatiotemporal data: methods and applications

Y Li, D Yu, Z Liu, M Zhang, X Gong, L Zhao - arXiv preprint arXiv …, 2023 - arxiv.org
In the era of big data, there has been a surge in the availability of data containing rich spatial
and temporal information, offering valuable insights into dynamic systems and processes for …

Improving future travel demand projections: a pathway with an open science interdisciplinary approach

S Yeh, J Gil, P Kyle, P Kishimoto, P Cazzola… - Progress in …, 2022 - iopscience.iop.org
Transport accounts for 24% of global CO 2 emissions from fossil fuels. Governments face
challenges in developing feasible and equitable mitigation strategies to reduce energy …

[HTML][HTML] Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review

EC Clark, S Neumann, S Hopkins… - JMIR Public Health …, 2024 - publichealth.jmir.org
Background: Public health surveillance plays a vital role in informing public health decision-
making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in …

Phase-Informed Bayesian ensemble models improve performance of COVID-19 forecasts

A Adiga, G Kaur, L Wang, B Hurt, P Porebski… - Proceedings of the …, 2023 - ojs.aaai.org
Despite hundreds of methods published in the literature, forecasting epidemic dynamics
remains challenging yet important. The challenges stem from multiple sources, including …

Analysis of performance improvements and bias associated with the use of human mobility data in covid-19 case prediction models

SM Abrar, N Awasthi, D Smolyak… - ACM Journal on …, 2023 - dl.acm.org
The COVID-19 pandemic has mainstreamed human mobility data into the public domain,
with research focused on understanding the impact of mobility reduction policies as well as …

Continually learning out-of-distribution spatiotemporal data for robust energy forecasting

A Prabowo, K Chen, H Xue… - … Conference on Machine …, 2023 - Springer
Forecasting building energy usage is essential for promoting sustainability and reducing
waste, as it enables building managers to adjust energy use to improve energy efficiency …