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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 · 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.
3 de dic. de 2020 · The integrated approach of epidemic and machine learning models could accurately forecast the long-term trend of the COVID-19 outbreak.
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 · 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.
6 de nov. de 2019 · 184 Citations. 656 Altmetric. Metrics. Abstract. With rapidly changing ecology, urbanization, climate change, increased travel and fragile public health systems, epidemics will become more...
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.
