Modelling epidemic spread in cities using public transportation as a proxy for generalized mobility trends

We study how public transportation data can inform the modeling of the spread of infectious diseases based on SIR dynamics. We present a model where public transportation data is used as an indicator of broader mobility patterns within a city, including the use of private transportation, walking etc...

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Autores principales: Malik, Omar, Gong, Bowen, Moussawi, Alaa, Korniss, Gyorgy, Szymanski, Boleslaw K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9012993/
https://www.ncbi.nlm.nih.gov/pubmed/35430595
http://dx.doi.org/10.1038/s41598-022-10234-8
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author Malik, Omar
Gong, Bowen
Moussawi, Alaa
Korniss, Gyorgy
Szymanski, Boleslaw K.
author_facet Malik, Omar
Gong, Bowen
Moussawi, Alaa
Korniss, Gyorgy
Szymanski, Boleslaw K.
author_sort Malik, Omar
collection PubMed
description We study how public transportation data can inform the modeling of the spread of infectious diseases based on SIR dynamics. We present a model where public transportation data is used as an indicator of broader mobility patterns within a city, including the use of private transportation, walking etc. The mobility parameter derived from this data is used to model the infection rate. As a test case, we study the impact of the usage of the New York City subway on the spread of COVID-19 within the city during 2020. We show that utilizing subway transport data as an indicator of the general mobility trends within the city, and therefore as an indicator of the effective infection rate, improves the quality of forecasting COVID-19 spread in New York City. Our model predicts the two peaks in the spread of COVID-19 cases in NYC in 2020, unlike a standard SIR model that misses the second peak entirely.
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spelling pubmed-90129932022-04-18 Modelling epidemic spread in cities using public transportation as a proxy for generalized mobility trends Malik, Omar Gong, Bowen Moussawi, Alaa Korniss, Gyorgy Szymanski, Boleslaw K. Sci Rep Article We study how public transportation data can inform the modeling of the spread of infectious diseases based on SIR dynamics. We present a model where public transportation data is used as an indicator of broader mobility patterns within a city, including the use of private transportation, walking etc. The mobility parameter derived from this data is used to model the infection rate. As a test case, we study the impact of the usage of the New York City subway on the spread of COVID-19 within the city during 2020. We show that utilizing subway transport data as an indicator of the general mobility trends within the city, and therefore as an indicator of the effective infection rate, improves the quality of forecasting COVID-19 spread in New York City. Our model predicts the two peaks in the spread of COVID-19 cases in NYC in 2020, unlike a standard SIR model that misses the second peak entirely. Nature Publishing Group UK 2022-04-16 /pmc/articles/PMC9012993/ /pubmed/35430595 http://dx.doi.org/10.1038/s41598-022-10234-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Malik, Omar
Gong, Bowen
Moussawi, Alaa
Korniss, Gyorgy
Szymanski, Boleslaw K.
Modelling epidemic spread in cities using public transportation as a proxy for generalized mobility trends
title Modelling epidemic spread in cities using public transportation as a proxy for generalized mobility trends
title_full Modelling epidemic spread in cities using public transportation as a proxy for generalized mobility trends
title_fullStr Modelling epidemic spread in cities using public transportation as a proxy for generalized mobility trends
title_full_unstemmed Modelling epidemic spread in cities using public transportation as a proxy for generalized mobility trends
title_short Modelling epidemic spread in cities using public transportation as a proxy for generalized mobility trends
title_sort modelling epidemic spread in cities using public transportation as a proxy for generalized mobility trends
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9012993/
https://www.ncbi.nlm.nih.gov/pubmed/35430595
http://dx.doi.org/10.1038/s41598-022-10234-8
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