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

arXiv: https://arxiv.org/abs/2404.16921 · HTML: https://mlsanity.com/paper/2404.16921/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 53 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://mlsanity.com/paper/2304.13712/) (2023)
- [Multimodal Learning with Transformers: A Survey](https://mlsanity.com/paper/2206.06488/) (2022)
- [Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation](https://mlsanity.com/paper/2402.14744/) (2024)
- [Where Would I Go Next? Large Language Models as Human Mobility Predictors](https://mlsanity.com/paper/2308.15197/) (2023)
- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data](https://mlsanity.com/paper/2401.06866/) (2024)
- [MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction](https://mlsanity.com/paper/2110.01401/) (2021)
- [Exploring Large Language Models for Human Mobility Prediction under Public Events](https://mlsanity.com/paper/2311.17351/) (2023)
- [ITINERA: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning](https://mlsanity.com/paper/2402.07204/) (2024)
- [Large Language Models for Spatial Trajectory Patterns Mining](https://mlsanity.com/paper/2310.04942/) (2023)
- [COLA: Cross-city Mobility Transformer for Human Trajectory Simulation](https://mlsanity.com/paper/2403.01801/) (2024)
- [How do you go where? Improving next location prediction by learning travel mode information using transformers](https://mlsanity.com/paper/2210.04095/) (2022)
- [MobilityGPT: Enhanced Human Mobility Modeling with a GPT model](https://mlsanity.com/paper/2402.03264/) (2024)
- [MPSTAN: Metapopulation-based Spatio-Temporal Attention Network for Epidemic Forecasting](https://mlsanity.com/paper/2306.12436/) (2023)
- [GeoFormer: Predicting Human Mobility using Generative Pre-trained Transformer (GPT)](https://mlsanity.com/paper/2311.05092/) (2023)
- [Predicting Infectiousness for Proactive Contact Tracing](https://mlsanity.com/paper/2010.12536/) (2020)
- [WiFiMod: Transformer-based Indoor Human Mobility Modeling using Passive Sensing](https://mlsanity.com/paper/2104.09835/) (2021)
- [Redesigning Electronic Health Record Systems to Support Developing Countries](https://mlsanity.com/paper/2302.01281/) (2023)
- [TrafFormer: A Transformer Model for Predicting Long-term Traffic](https://mlsanity.com/paper/2302.12388/) (2023)
- [Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review](https://mlsanity.com/paper/2104.04059/) (2021)

## Built on by (most cited first, 2 of 2)

- [Artificial Intelligence for Public Health Surveillance in Africa: Applications and Opportunities](https://mlsanity.com/paper/2408.02575/) (2024)
- [Eyes Will Shut: A Vision-Based Next GPS Location Prediction Model by Reinforcement Learning from Visual Map Feed Back](https://mlsanity.com/paper/2507.18661/) (2025)

## 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.