Causalgnn: Causal-based graph neural networks for spatio-temporal epidemic forecasting
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 …
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
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 …
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
Graph neural networks (GNNs) offer a new powerful alternative for multivariate time series
forecasting, demonstrating remarkable success in a variety of spatio-temporal applications …
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
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 …
Health Organisation still warns of a fast growth in infections and deaths. Infectious disease …
Graph Neural Network for spatiotemporal data: methods and applications
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 …
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
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 …
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 …
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
Despite hundreds of methods published in the literature, forecasting epidemic dynamics
remains challenging yet important. The challenges stem from multiple sources, including …
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
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 …
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
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 …
waste, as it enables building managers to adjust energy use to improve energy efficiency …