COVID-19 modelling by time-varying transmission rate associated with mobility trend of driving via Apple Maps
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|Title:||COVID-19 modelling by time-varying transmission rate associated with mobility trend of driving via Apple Maps||Authors:||Jing, Min; Ng, Kok Yew; MacNamee, Brian; et al.||Permanent link:||http://hdl.handle.net/10197/12763||Date:||Oct-2021||Online since:||2022-02-17T16:15:53Z||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.||Funding Details:||European Commission||Type of material:||Journal Article||Publisher:||Elsevier||Journal:||Journal of Biomedical Informatics||Volume:||122||Copyright (published version):||2021 the Authors||Keywords:||Humans; Malus; Forecasting; Pandemics; COVID-19; SARS-CoV-2; Coronavirus||DOI:||10.1016/j.jbi.2021.103905||Language:||en||Status of Item:||Peer reviewed||ISSN:||1532-0464||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Computer Science Research Collection|
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