Publications by authors named "Shiyang Lyu"
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.
Educational institutions play a significant role in the community spread of SARS-CoV-2 in Victoria. Despite a series of social restrictions and preventive measures in educational institutions implemented by the Victorian Government, confirmed cases among people under 20 years of age accounted for more than a quarter of the total infections in the state. In this study, we investigated the risk factors associated with COVID-19 infection within Victoria educational institutions using an incremental deep learning recurrent neural network-gated recurrent unit (RNN-GRU) model.
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