AI Article Synopsis
- Web search data can indicate COVID-19 outbreaks, but the effects of media coverage and co-morbid conditions on symptom searches need attention to avoid inaccuracies.
- A machine learning model was created to analyze how these factors influence online searches, allowing for dynamic simulations of COVID-19 infection rates in Australia and New Zealand.
- The findings revealed that a majority of symptom searches were influenced by media and co-morbid conditions rather than direct COVID-19 concerns, indicating that online symptom data might lead to overestimations of actual infections.
Article 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.
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| http://dx.doi.org/10.1080/23744235.2024.2311281 | DOI Listing |
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