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25 de abr. de 2024 · Contribution. In this paper, we provide a comprehensive overview of recent research endeavors aimed at leveraging machine learning techniques, specifically Transformer models, to enhance the prediction of human mobility patterns in the context of epidemics.
25 de abr. de 2024 · A Short Survey of Human Mobility Prediction in Epidemic Modeling from Transformers to LLMs. This paper provides a comprehensive survey of recent advancements in leveraging machine learning techniques, particularly Transformer models, for predicting human mobility patterns during epidemics.
9 de nov. de 2023 · Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.
25 de abr. de 2024 · This paper provides a comprehensive survey of recent advancements in leveraging machine learning techniques, particularly Transformer models, for predicting human mobility patterns during epidemics. Understanding how people move during epidemics is essential for modeling the spread of diseases and devising effective response strategies.
31 de ago. de 2021 · Corona Virus Disease 2019 (COVID-19) has spread rapidly to countries all around the world from the end of 2019, which caused a great impact on global health and has had a huge impact on many ...
1 de oct. de 2021 · In this paper, we propose the Temporal Fusion Transformer (TFT) – an attention-based DNN architecture for multi-horizon forecasting that achieves high performance while enabling new forms of interpretability.
This paper provides a comprehensive survey of recent advancements in leveraging machine learning techniques, particularly Transformer models, for predicting human mobility patterns during epidemics. Understanding how people move during epidemics is essential for modeling the spread of diseases and devising effective response strategies.
