Lyu, Shiyang, Adegboye, Oyelola, Adhinugraha, kiki, Emeto, Theophilus I., and Tanier, David (2024) Analysing the impact of comorbid conditions and media coverage on online symptom search data: a novel AI-based approach for COVID-19 tracking. Infectious Diseases, 56 (5). pp. 348-358.

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Abstract

Background: Web search data have proven to bea valuable early indicator of COVID-19 outbreaks. However, the influence of co-morbid conditions with similar symptoms and the effect of media coverage on symptom-related searches are often overlooked, leading to potential inaccuracies in COVID-19 simulations.

Method: This study introduces a machine learning-based approach to estimate the magnitude of the impact of media coverage and comorbid conditions with similar symptoms on online symptom searches, based on two scenarios with quantile levels 10–90 and 25–75. An incremental batch learning RNN-LSTM model was then developed for the COVID-19 simulation in Australia and New Zealand, allowing the model to dynamically simulate different infection rates and transmissibility of SARS-CoV-2 variants.

Result: The COVID-19 infected person-directed symptom searches were found to account for only a small proportion of the total search volume (on average 33.68% in Australia vs. 36.89% in New Zealand) compared to searches influenced by media coverage and comorbid conditions (on average 44.88% in Australia vs. 50.94% in New Zealand). The proposed method, which incorporates estimated symptom component ratios into the RNN-LSTM embedding model, significantly improved COVID-19 simulation performance.

Conclusion: Media coverage and comorbid conditions with similar symptoms dominate the total number of online symptom searches, suggesting that direct use of online symptom search data in COVID-19 simulations may overestimate COVID-19 infections. Our approach provides new insights into the accurate estimation of COVID-19 infections using online symptom searches, thereby assisting governments in developing complementary methods for public health surveillance.

Item ID:	81732
Item Type:	Article (Research - C1)
ISSN:	2374-4243
Keywords:	infectious diseases, infection control,digital health, deep learning, COVID-19, social media
Copyright Information:	© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
Date Deposited:	01 Mar 2024 01:23
FoR Codes:	49 MATHEMATICAL SCIENCES > 4905 Statistics > 490502 Biostatistics @ 30% 32 BIOMEDICAL AND CLINICAL SCIENCES > 3202 Clinical sciences > 320211 Infectious diseases @ 50% 42 HEALTH SCIENCES > 4202 Epidemiology > 420204 Epidemiological methods @ 20%
SEO Codes:	20 HEALTH > 2001 Clinical health > 200104 Prevention of human diseases and conditions @ 40% 20 HEALTH > 2004 Public health (excl. specific population health) > 200404 Disease distribution and transmission (incl. surveillance and response) @ 60%
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