mlsanityData to 7 Oct 2026

A Short Survey of Human Mobility Prediction in Epidemic Modeling from Transformers to LLMs

arXiv 2404.16921 ↗First posted 25 Apr 2024cs.LG cs.CLcited by 2

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Abstract (arXiv)

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. Forecasting population movement is crucial for informing epidemiological models and facilitating effective response planning in public health emergencies. Predicting mobility patterns can enable authorities to better anticipate the geographical and temporal spread of diseases, allocate resources more efficiently, and implement targeted interventions. We review a range of approaches utilizing both pretrained language models like BERT and Large Language Models (LLMs) tailored specifically for mobility prediction tasks. These models have demonstrated significant potential in capturing complex spatio-temporal dependencies and contextual patterns in textual data.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 53 references that are in the map. First posted on arXiv 25 Apr 2024.

Builds on

22 of 53 references.

References · 22

  1. 2017Attention Is All You Needcited by 59,932
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  3. 2023Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyondcited by 409
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  8. 2024Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Datacited by 65
  9. 2021MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Predictioncited by 48
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  11. 2024ITINERA: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planningcited by 33
  12. 2023Large Language Models for Spatial Trajectory Patterns Miningcited by 27
10 more
  1. 2024COLA: Cross-city Mobility Transformer for Human Trajectory Simulationcited by 25
  2. 2022How do you go where? Improving next location prediction by learning travel mode information using transformerscited by 12
  3. 2024MobilityGPT: Enhanced Human Mobility Modeling with a GPT modelcited by 11
  4. 2023MPSTAN: Metapopulation-based Spatio-Temporal Attention Network for Epidemic Forecastingcited by 5
  5. 2023GeoFormer: Predicting Human Mobility using Generative Pre-trained Transformer (GPT)cited by 5
  6. 2020Predicting Infectiousness for Proactive Contact Tracingcited by 4
  7. 2021WiFiMod: Transformer-based Indoor Human Mobility Modeling using Passive Sensingcited by 4
  8. 2023Redesigning Electronic Health Record Systems to Support Developing Countriescited by 2
  9. 2023TrafFormer: A Transformer Model for Predicting Long-term Trafficcited by 2
  10. 2021Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Reviewcited by 1

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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Artificial Intelligence for Public Health Surveillance in Africa: Applications and Opportunitiescited by 1
  2. 2025Eyes Will Shut: A Vision-Based Next GPS Location Prediction Model by Reinforcement Learning from Visual Map Feed Backcited by 1