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. 2022 Jun 21;24(6):e37004. doi: 10.2196/37004

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©Ting Dang, Jing Han, Tong Xia, Dimitris Spathis, Erika Bondareva, Chloë Siegele-Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, R Andres Floto, Pietro Cicuta, Cecilia Mascolo. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 21.06.2022.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Overview of study design: COVID-19 detection and progression were assessed from audio data. Voice, cough, and breathing sound recordings were collected from each participant over a period, together with self-reported COVID-19 test results. During model development, audio recordings were chunked into segments consisting of 5 samples covering a few days and processed using sequential modeling techniques (GRUs) for COVID-19 monitoring. Two subtasks were evaluated: (1) COVID-19 detection (positive vs negative) and (2) disease progression monitoring. GRU: gated recurrent unit.