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https://hdl.handle.net/10316/115271
Título: | COVID-19 Symptoms app analysis to foresee healthcare impacts: Evidence from Northern Ireland | Autor: | Sousa, José Barata, João Woerden, Hugo C van Kee, Frank |
Palavras-chave: | COVID-19; Location analytics; Mobile app; SARS-COV-2; Semantic networks; Strong structuration theory; Symptoms assessment | Data: | Fev-2022 | Editora: | Elsevier | Título da revista, periódico, livro ou evento: | Applied Soft Computing | Volume: | 116 | Resumo: | 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 | Direitos: | openAccess |
Aparece nas coleções: | FCTUC Eng.Informática - Artigos em Revistas Internacionais |
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ASOC-D-20-04955_Repository.pdf | 615.02 kB | Adobe PDF | Ver/Abrir |
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