Trending on the use of Google mobility data in COVID-19 mathematical models

Yang Deng; Hefei Lin; Daihai He; Yi Zhao

doi:10.1186/s13662-024-03816-5

Trending on the use of Google mobility data in COVID-19 mathematical models

Yang Deng
, Hefei Lin
, Daihai He^*
, Yi Zhao^*

^*Corresponding author for this work

Hong Kong Polytechnic University
Harbin Institute of Technology

Research output: Contribution to journal › Review article › peer-review

3 Scopus citations

Abstract

Google mobility data has been widely used in COVID-19 mathematical modeling to understand disease transmission dynamics. This review examines the extensive literature on the use of Google mobility data in COVID-19 mathematical modeling. We mainly focus on over a dozen influential studies using Google mobility data in COVID-19 mathematical modeling, including compartmental and metapopulation models. Google mobility data provides valuable insights into mobility changes and interventions. However, challenges persist in fully elucidating transmission dynamics over time, modeling longer time series and accounting for individual-level correlations in mobility patterns, urging the incorporation of diverse datasets for modeling in the post-COVID-19 landscape.

Original language	English
Article number	21
Journal	Advances in Continuous and Discrete Models
Volume	2024
Issue number	1
DOIs	https://doi.org/10.1186/s13662-024-03816-5
State	Published - Dec 2024
Externally published	Yes

Keywords

COVID-19
Contact matrix
Google mobility data
Mathematical models
The basic reproduction number
Transmission rate

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10.1186/s13662-024-03816-5

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abstract = "Google mobility data has been widely used in COVID-19 mathematical modeling to understand disease transmission dynamics. This review examines the extensive literature on the use of Google mobility data in COVID-19 mathematical modeling. We mainly focus on over a dozen influential studies using Google mobility data in COVID-19 mathematical modeling, including compartmental and metapopulation models. Google mobility data provides valuable insights into mobility changes and interventions. However, challenges persist in fully elucidating transmission dynamics over time, modeling longer time series and accounting for individual-level correlations in mobility patterns, urging the incorporation of diverse datasets for modeling in the post-COVID-19 landscape.",

keywords = "COVID-19, Contact matrix, Google mobility data, Mathematical models, The basic reproduction number, Transmission rate",

author = "Yang Deng and Hefei Lin and Daihai He and Yi Zhao",

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AU - Lin, Hefei

AU - He, Daihai

AU - Zhao, Yi

N1 - Publisher Copyright: © The Author(s) 2024.

PY - 2024/12

Y1 - 2024/12

N2 - Google mobility data has been widely used in COVID-19 mathematical modeling to understand disease transmission dynamics. This review examines the extensive literature on the use of Google mobility data in COVID-19 mathematical modeling. We mainly focus on over a dozen influential studies using Google mobility data in COVID-19 mathematical modeling, including compartmental and metapopulation models. Google mobility data provides valuable insights into mobility changes and interventions. However, challenges persist in fully elucidating transmission dynamics over time, modeling longer time series and accounting for individual-level correlations in mobility patterns, urging the incorporation of diverse datasets for modeling in the post-COVID-19 landscape.

AB - Google mobility data has been widely used in COVID-19 mathematical modeling to understand disease transmission dynamics. This review examines the extensive literature on the use of Google mobility data in COVID-19 mathematical modeling. We mainly focus on over a dozen influential studies using Google mobility data in COVID-19 mathematical modeling, including compartmental and metapopulation models. Google mobility data provides valuable insights into mobility changes and interventions. However, challenges persist in fully elucidating transmission dynamics over time, modeling longer time series and accounting for individual-level correlations in mobility patterns, urging the incorporation of diverse datasets for modeling in the post-COVID-19 landscape.

KW - COVID-19

KW - Contact matrix

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KW - Mathematical models

KW - The basic reproduction number

KW - Transmission rate

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Trending on the use of Google mobility data in COVID-19 mathematical models

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Keywords

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