COVID-19 datasets

The 'COVID-19 datasets' is a collection of data related to infection cases and human mobility used to evaluate the effectiveness of large language models in spatio-temporal epidemic forecasting.

Papers using COVID-19 datasets (12)

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting2025Performative Time-Series Forecasting2023Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study2024 · 5 citesAdaptive County Level COVID-19 Forecast Models: Analysis and Improvement2020 · 4 citesImproving COVID-19 Forecasting using eXogenous Variables2021 · 4 citesTemporal Multiresolution Graph Neural Networks For Epidemic Prediction2022 · 3 citesBeyond Hawkes: Neural Multi-event Forecasting on Spatio-temporal Point Processes2022 · 2 citesMSGNN: Multi-scale Spatio-temporal Graph Neural Network for Epidemic Forecasting2023 · 1 citesTG-PhyNN: An Enhanced Physically-Aware Graph Neural Network framework for forecasting Spatio-Temporal Data2024 · 1 citesMP-PINN: A Multi-Phase Physics-Informed Neural Network for Epidemic Forecasting2024 · 1 citesForecasting the changes between endemic and epidemic phases of a contagious disease, with the example of COVID-192023A Multilateral Attention-enhanced Deep Neural Network for Disease Outbreak Forecasting: A Case Study on COVID-192024