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25 de abr. de 2024 · This table provides information about the methods, applications, and datasets utilized in recent studies involving Transformers and LLMs for modeling human mobility patterns in the context of epidemic modeling.
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.
Variations in disease transmission rates, mobility modes (i.e. commuting and migration), and connectivity strengths determine the threshold value and whether or not a disease may potentially ...
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.
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.
31 de ago. de 2021 · The experimental results show that the combination of the LSTM and Markov model could improve the prediction accuracy of the epidemic trend effectively, and the prediction effect is also in line...
This paper provides a comprehensive survey of recent advancements in leveraging machine learning techniques, particularly Transformer models, for predicting human mobility patterns during epidemics.
