![]() Turk previously developed a different model to predict COVID-19 hospitalizations on a local level for the Charlotte, North Carolina, metropolitan area. This keeps the model from being weighed down by older data that don't reflect how the virus acts now. It also has temporal and spatial components - terms representing the delay between today's data and the future hospitalizations it predicts, and how closely connected different states are.Įvery week, the model retrains itself using the past 56 days’ worth of data. ![]() The model considers Google search volumes for 256 COVID-19-specific terms, such as "loss of taste," "COVID-19 vaccine" and "cough," together with core statistics like case counts and vaccination rates. It's that window from the earthquake to when the tsunami hit the shore where my model really shines." I think that's the information that we are providing here. … A few hours is enough for me to get prepared, allocate resources and inform my staff. "Google search will tell me a few hours ahead that a tsunami is hitting. 14, 2004, photo shows computers displaying the Google desktop search engine at the Digitallife show at New York's Jacob K. ![]() It did a good job of predicting spikes in hospitalizations thought to be associated with new variants such as omicron, without the time delays typical of many other models.įILE - This Oct. Yang also thinks that his model will be especially useful when new variants pop up. Watching trends in how often people Google certain terms, like “cough” or “COVID-19 vaccine,” could help fill in the gaps in places with sparse testing or weak health care systems. Yang is working to add the new model to the CDC's COVID-19 forecasting hub. ![]() Centers for Disease Control and Prevention’s “national ensemble” forecast, which combines models made by many research teams - though there are some single models that outperform it.Īccording to study co-author Shihao Yang, a data scientist at the Georgia Institute of Technology, the new model's value is its unique perspective - a data source that is independent of conventional metrics. "That gives health care administrators and leaders advance warning to prepare for surges - to stock up on personal protective equipment and staffing and to anticipate a surge coming at them."įor predictions one or two weeks in advance, the new computer model stacks up well against existing ones. ![]() "If you have a bunch of people searching for 'COVID testing sites near me' … you're going to still feel the effects of that downstream at the hospital level in terms of admissions," said data scientist Philip Turk of the University of Mississippi Medical Center, who was not involved in the study. Google Trends is an online portal that provides data on Google search volumes in real time. Using the search data provided by Google Trends, scientists were able to build a computational model to forecast COVID-19 hospitalizations. In the study, researchers watched the number of COVID-related Google searches made across the country and used that information, together with conventional COVID-19 metrics such as confirmed cases, to predict hospital admission rates weeks in advance. Future waves of COVID-19 might be predicted using internet search data, according to a study published in the journal Scientific Reports. ![]()
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