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. Understanding how people move during epidemics is essential for modeling the spread of diseases and d...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Mayemba, Christian N, Nkashama, D'Jeff K, Tshimula, Jean Marie, Dialufuma, Maximilien V, Muabila, Jean Tshibangu, Didier, Mbuyi Mukendi, Kanda, Hugues, Galekwa, René Manassé, Fita, Heber Dibwe, Mundele, Serge, Kalala, Kalonji, Ilunga, Aristarque, Ntobo, Lambert Mukendi, Muteba, Dominique, Abedi, Aaron Aruna
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Mayemba, Christian N
Nkashama, D'Jeff K
Tshimula, Jean Marie
Dialufuma, Maximilien V
Muabila, Jean Tshibangu
Didier, Mbuyi Mukendi
Kanda, Hugues
Galekwa, René Manassé
Fita, Heber Dibwe
Mundele, Serge
Kalala, Kalonji
Ilunga, Aristarque
Ntobo, Lambert Mukendi
Muteba, Dominique
Abedi, Aaron Aruna
description 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.
doi_str_mv 10.48550/arxiv.2404.16921
format Article
fullrecord <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2404_16921</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2404_16921</sourcerecordid><originalsourceid>FETCH-LOGICAL-a671-8a7e5fdd38b0f69fae6d6ced503eb2c4e82cc99b6cf28864d4c0bd9878b1fd833</originalsourceid><addsrcrecordid>eNotz71OwzAYhWEvDKhwAUx8N5BgJ47jjFVVKFKqIjUTS-Sfz2ApjisnrcjdA4XpDK90pIeQB0ZzLquKPqn05S95wSnPmWgKdkve13D8jGmG4zldcIHoYHcOaoR91H7w8wJvCa03s48j-BG2J28xePPTLQ5-_ACXYoAuqXFyMQVME8wR2nY_3ZEbp4YJ7_93RbrnbbfZZe3h5XWzbjMlapZJVWPlrC2lpk40TqGwwqCtaIm6MBxlYUzTaGFcIaXglhuqbSNrqZmzsixX5PHv9orrT8kHlZb-F9lfkeU3pgBN3w</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>A Short Survey of Human Mobility Prediction in Epidemic Modeling from Transformers to LLMs</title><source>arXiv.org</source><creator>Mayemba, Christian N ; Nkashama, D'Jeff K ; Tshimula, Jean Marie ; Dialufuma, Maximilien V ; Muabila, Jean Tshibangu ; Didier, Mbuyi Mukendi ; Kanda, Hugues ; Galekwa, René Manassé ; Fita, Heber Dibwe ; Mundele, Serge ; Kalala, Kalonji ; Ilunga, Aristarque ; Ntobo, Lambert Mukendi ; Muteba, Dominique ; Abedi, Aaron Aruna</creator><creatorcontrib>Mayemba, Christian N ; Nkashama, D'Jeff K ; Tshimula, Jean Marie ; Dialufuma, Maximilien V ; Muabila, Jean Tshibangu ; Didier, Mbuyi Mukendi ; Kanda, Hugues ; Galekwa, René Manassé ; Fita, Heber Dibwe ; Mundele, Serge ; Kalala, Kalonji ; Ilunga, Aristarque ; Ntobo, Lambert Mukendi ; Muteba, Dominique ; Abedi, Aaron Aruna</creatorcontrib><description>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.</description><identifier>DOI: 10.48550/arxiv.2404.16921</identifier><language>eng</language><subject>Computer Science - Computation and Language ; Computer Science - Learning</subject><creationdate>2024-04</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2404.16921$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2404.16921$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Mayemba, Christian N</creatorcontrib><creatorcontrib>Nkashama, D'Jeff K</creatorcontrib><creatorcontrib>Tshimula, Jean Marie</creatorcontrib><creatorcontrib>Dialufuma, Maximilien V</creatorcontrib><creatorcontrib>Muabila, Jean Tshibangu</creatorcontrib><creatorcontrib>Didier, Mbuyi Mukendi</creatorcontrib><creatorcontrib>Kanda, Hugues</creatorcontrib><creatorcontrib>Galekwa, René Manassé</creatorcontrib><creatorcontrib>Fita, Heber Dibwe</creatorcontrib><creatorcontrib>Mundele, Serge</creatorcontrib><creatorcontrib>Kalala, Kalonji</creatorcontrib><creatorcontrib>Ilunga, Aristarque</creatorcontrib><creatorcontrib>Ntobo, Lambert Mukendi</creatorcontrib><creatorcontrib>Muteba, Dominique</creatorcontrib><creatorcontrib>Abedi, Aaron Aruna</creatorcontrib><title>A Short Survey of Human Mobility Prediction in Epidemic Modeling from Transformers to LLMs</title><description>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.</description><subject>Computer Science - Computation and Language</subject><subject>Computer Science - Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz71OwzAYhWEvDKhwAUx8N5BgJ47jjFVVKFKqIjUTS-Sfz2ApjisnrcjdA4XpDK90pIeQB0ZzLquKPqn05S95wSnPmWgKdkve13D8jGmG4zldcIHoYHcOaoR91H7w8wJvCa03s48j-BG2J28xePPTLQ5-_ACXYoAuqXFyMQVME8wR2nY_3ZEbp4YJ7_93RbrnbbfZZe3h5XWzbjMlapZJVWPlrC2lpk40TqGwwqCtaIm6MBxlYUzTaGFcIaXglhuqbSNrqZmzsixX5PHv9orrT8kHlZb-F9lfkeU3pgBN3w</recordid><startdate>20240425</startdate><enddate>20240425</enddate><creator>Mayemba, Christian N</creator><creator>Nkashama, D'Jeff K</creator><creator>Tshimula, Jean Marie</creator><creator>Dialufuma, Maximilien V</creator><creator>Muabila, Jean Tshibangu</creator><creator>Didier, Mbuyi Mukendi</creator><creator>Kanda, Hugues</creator><creator>Galekwa, René Manassé</creator><creator>Fita, Heber Dibwe</creator><creator>Mundele, Serge</creator><creator>Kalala, Kalonji</creator><creator>Ilunga, Aristarque</creator><creator>Ntobo, Lambert Mukendi</creator><creator>Muteba, Dominique</creator><creator>Abedi, Aaron Aruna</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20240425</creationdate><title>A Short Survey of Human Mobility Prediction in Epidemic Modeling from Transformers to LLMs</title><author>Mayemba, Christian N ; Nkashama, D'Jeff K ; Tshimula, Jean Marie ; Dialufuma, Maximilien V ; Muabila, Jean Tshibangu ; Didier, Mbuyi Mukendi ; Kanda, Hugues ; Galekwa, René Manassé ; Fita, Heber Dibwe ; Mundele, Serge ; Kalala, Kalonji ; Ilunga, Aristarque ; Ntobo, Lambert Mukendi ; Muteba, Dominique ; Abedi, Aaron Aruna</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a671-8a7e5fdd38b0f69fae6d6ced503eb2c4e82cc99b6cf28864d4c0bd9878b1fd833</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Science - Computation and Language</topic><topic>Computer Science - Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Mayemba, Christian N</creatorcontrib><creatorcontrib>Nkashama, D'Jeff K</creatorcontrib><creatorcontrib>Tshimula, Jean Marie</creatorcontrib><creatorcontrib>Dialufuma, Maximilien V</creatorcontrib><creatorcontrib>Muabila, Jean Tshibangu</creatorcontrib><creatorcontrib>Didier, Mbuyi Mukendi</creatorcontrib><creatorcontrib>Kanda, Hugues</creatorcontrib><creatorcontrib>Galekwa, René Manassé</creatorcontrib><creatorcontrib>Fita, Heber Dibwe</creatorcontrib><creatorcontrib>Mundele, Serge</creatorcontrib><creatorcontrib>Kalala, Kalonji</creatorcontrib><creatorcontrib>Ilunga, Aristarque</creatorcontrib><creatorcontrib>Ntobo, Lambert Mukendi</creatorcontrib><creatorcontrib>Muteba, Dominique</creatorcontrib><creatorcontrib>Abedi, Aaron Aruna</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Mayemba, Christian N</au><au>Nkashama, D'Jeff K</au><au>Tshimula, Jean Marie</au><au>Dialufuma, Maximilien V</au><au>Muabila, Jean Tshibangu</au><au>Didier, Mbuyi Mukendi</au><au>Kanda, Hugues</au><au>Galekwa, René Manassé</au><au>Fita, Heber Dibwe</au><au>Mundele, Serge</au><au>Kalala, Kalonji</au><au>Ilunga, Aristarque</au><au>Ntobo, Lambert Mukendi</au><au>Muteba, Dominique</au><au>Abedi, Aaron Aruna</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Short Survey of Human Mobility Prediction in Epidemic Modeling from Transformers to LLMs</atitle><date>2024-04-25</date><risdate>2024</risdate><abstract>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.</abstract><doi>10.48550/arxiv.2404.16921</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier DOI: 10.48550/arxiv.2404.16921
ispartof
issn
language eng
recordid cdi_arxiv_primary_2404_16921
source arXiv.org
subjects Computer Science - Computation and Language
Computer Science - Learning
title A Short Survey of Human Mobility Prediction in Epidemic Modeling from Transformers to LLMs
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T00%3A53%3A44IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Short%20Survey%20of%20Human%20Mobility%20Prediction%20in%20Epidemic%20Modeling%20from%20Transformers%20to%20LLMs&rft.au=Mayemba,%20Christian%20N&rft.date=2024-04-25&rft_id=info:doi/10.48550/arxiv.2404.16921&rft_dat=%3Carxiv_GOX%3E2404_16921%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true