COVID-19 modelling by time-varying transmission rate associated with mobility trend of driving via Apple Maps

Min Jing; Kok Yew Ng; Brian Mac Namee; Pardis Biglarbeigi; Rob Brisk; Raymond Bond; Dewar Finlay; James McLaughlin

doi:10.1016/j.jbi.2021.103905

COVID-19 modelling by time-varying transmission rate associated with mobility trend of driving via Apple Maps

Min Jing (Lead / Corresponding author), Kok Yew Ng, Brian Mac Namee, Pardis Biglarbeigi, Rob Brisk, Raymond Bond, Dewar Finlay, James McLaughlin

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Abstract

Compartment-based infectious disease models that consider the transmission rate (or contact rate) as a constant during the course of an epidemic can be limiting regarding effective capture of the dynamics of infectious disease. This study proposed a novel approach based on a dynamic time-varying transmission rate with a control rate governing the speed of disease spread, which may be associated with the information related to infectious disease intervention. Integration of multiple sources of data with disease modelling has the potential to improve modelling performance. Taking the global mobility trend of vehicle driving available via Apple Maps as an example, this study explored different ways of processing the mobility trend data and investigated their relationship with the control rate. The proposed method was evaluated based on COVID-19 data from six European countries. The results suggest that the proposed model with dynamic transmission rate improved the performance of model fitting and forecasting during the early stage of the pandemic. Positive correlation has been found between the average daily change of mobility trend and control rate. The results encourage further development for incorporation of multiple resources into infectious disease modelling in the future.

Original language	English
Article number	103905
Number of pages	13
Journal	Journal of Biomedical Informatics
Volume	122
Early online date	2 Sept 2021
DOIs	https://doi.org/10.1016/j.jbi.2021.103905
Publication status	Published - Oct 2021

Keywords

COVID-19
Data integration
Dynamic transmission rate
Infectious disease modelling
Mobility trend

ASJC Scopus subject areas

Computer Science Applications
Health Informatics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.jbi.2021.103905Licence: CC BY-NC-ND

Final Published VersionFinal published version, 4.44 MBLicence: CC BY-NC-ND

Cite this

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title = "COVID-19 modelling by time-varying transmission rate associated with mobility trend of driving via Apple Maps",

abstract = "Compartment-based infectious disease models that consider the transmission rate (or contact rate) as a constant during the course of an epidemic can be limiting regarding effective capture of the dynamics of infectious disease. This study proposed a novel approach based on a dynamic time-varying transmission rate with a control rate governing the speed of disease spread, which may be associated with the information related to infectious disease intervention. Integration of multiple sources of data with disease modelling has the potential to improve modelling performance. Taking the global mobility trend of vehicle driving available via Apple Maps as an example, this study explored different ways of processing the mobility trend data and investigated their relationship with the control rate. The proposed method was evaluated based on COVID-19 data from six European countries. The results suggest that the proposed model with dynamic transmission rate improved the performance of model fitting and forecasting during the early stage of the pandemic. Positive correlation has been found between the average daily change of mobility trend and control rate. The results encourage further development for incorporation of multiple resources into infectious disease modelling in the future.",

keywords = "COVID-19, Data integration, Dynamic transmission rate, Infectious disease modelling, Mobility trend",

author = "Min Jing and Ng, {Kok Yew} and Namee, {Brian Mac} and Pardis Biglarbeigi and Rob Brisk and Raymond Bond and Dewar Finlay and James McLaughlin",

note = "Funding Information: This research is carried out under the project of Eastern Corridor Medical Engineering Centre (ECME) and funded by the European Unions INTERREG VA Programme (Grant ID:IVA5034), managed by the Special EU Programmes Body (SEUPB). Publisher Copyright: {\textcopyright} 2021 The Authors. Published by Elsevier Inc.",

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AU - Jing, Min

AU - Ng, Kok Yew

AU - Namee, Brian Mac

AU - Biglarbeigi, Pardis

AU - Brisk, Rob

AU - Bond, Raymond

AU - Finlay, Dewar

AU - McLaughlin, James

N1 - Funding Information: This research is carried out under the project of Eastern Corridor Medical Engineering Centre (ECME) and funded by the European Unions INTERREG VA Programme (Grant ID:IVA5034), managed by the Special EU Programmes Body (SEUPB). Publisher Copyright: © 2021 The Authors. Published by Elsevier Inc.

PY - 2021/10

Y1 - 2021/10

N2 - Compartment-based infectious disease models that consider the transmission rate (or contact rate) as a constant during the course of an epidemic can be limiting regarding effective capture of the dynamics of infectious disease. This study proposed a novel approach based on a dynamic time-varying transmission rate with a control rate governing the speed of disease spread, which may be associated with the information related to infectious disease intervention. Integration of multiple sources of data with disease modelling has the potential to improve modelling performance. Taking the global mobility trend of vehicle driving available via Apple Maps as an example, this study explored different ways of processing the mobility trend data and investigated their relationship with the control rate. The proposed method was evaluated based on COVID-19 data from six European countries. The results suggest that the proposed model with dynamic transmission rate improved the performance of model fitting and forecasting during the early stage of the pandemic. Positive correlation has been found between the average daily change of mobility trend and control rate. The results encourage further development for incorporation of multiple resources into infectious disease modelling in the future.

AB - Compartment-based infectious disease models that consider the transmission rate (or contact rate) as a constant during the course of an epidemic can be limiting regarding effective capture of the dynamics of infectious disease. This study proposed a novel approach based on a dynamic time-varying transmission rate with a control rate governing the speed of disease spread, which may be associated with the information related to infectious disease intervention. Integration of multiple sources of data with disease modelling has the potential to improve modelling performance. Taking the global mobility trend of vehicle driving available via Apple Maps as an example, this study explored different ways of processing the mobility trend data and investigated their relationship with the control rate. The proposed method was evaluated based on COVID-19 data from six European countries. The results suggest that the proposed model with dynamic transmission rate improved the performance of model fitting and forecasting during the early stage of the pandemic. Positive correlation has been found between the average daily change of mobility trend and control rate. The results encourage further development for incorporation of multiple resources into infectious disease modelling in the future.

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KW - Mobility trend

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COVID-19 modelling by time-varying transmission rate associated with mobility trend of driving via Apple Maps

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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