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https://hdl.handle.net/10316/115271
Title: | COVID-19 Symptoms app analysis to foresee healthcare impacts: Evidence from Northern Ireland | Authors: | Sousa, José Barata, João Woerden, Hugo C van Kee, Frank |
Keywords: | COVID-19; Location analytics; Mobile app; SARS-COV-2; Semantic networks; Strong structuration theory; Symptoms assessment | Issue Date: | Feb-2022 | Publisher: | Elsevier | Project: | This work uses non-identifiable data provided through the use of the DoH NI app, ‘COVID-19 NI’. The app was produced on behalf of the DoH by Digital Health and Care Northern Ireland (DHCNI) and the Public Health Agency for Northern Ireland, working with commercial partners Civica and BigMotive. We wish to acknowledge the access granted to the non-identifiable data, which led to this output. This work is partially funded by national funds through the FCT — Foundation for Science and Technology, I.P., Portugal, within the scope of the project CISUC — UID/CEC/00326/2020 and by European Social Fund , through the Regional Operational Program Centro 2020. | Serial title, monograph or event: | Applied Soft Computing | Volume: | 116 | Abstract: | Mobile health (mHealth) technologies, such as symptom tracking apps, are crucial for coping with the global pandemic crisis by providing near real-time, in situ information for the medical and governmental response. However, in such a dynamic and diverse environment, methods are still needed to support public health decision-making. This paper uses the lens of strong structuration theory to investigate networks of COVID-19 symptoms in the Belfast metropolitan area. A self-supervised machine learning method measuring information entropy was applied to the Northern Ireland COVIDCare app. The findings reveal: (1) relevant stratifications of disease symptoms, (2) particularities in health-wealth networks, and (3) the predictive potential of artificial intelligence to extract entangled knowledge from data in COVID-related apps. The proposed method proved to be effective for near real-time in-situ analysis of COVID-19 progression and to focus and complement public health decisions. Our contribution is relevant to an understanding of SARS-COV-2 symptom entanglements in localised environments. It can assist decision-makers in designing both reactive and proactive health measures that should be personalised to the heterogeneous needs of different populations. Moreover, near real-time assessment of pandemic symptoms using digital technologies will be critical to create early warning systems of emerging SARS-CoV-2 strains and predict the need for healthcare resources. | URI: | https://hdl.handle.net/10316/115271 | ISSN: | 1568-4946 | DOI: | 10.1016/j.asoc.2021.108324 | Rights: | openAccess |
Appears in Collections: | FCTUC Eng.Informática - Artigos em Revistas Internacionais |
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