How Google search data can predict COVID-19 outbreaks

 

• New research finds that online searches can accurately predict regional increases and decreases in COVID-19 cases.
• Certain types of searches reveal the activities in which people plan to engage.
• The search volume for outside-the-home vs. stay-at-home activities forecasts the number of COVID-19 diagnoses 10–14 days later.

While some of the behaviors that lead to SARS-CoV-2 infections are clear, new waves of COVID-19 cases do not always follow predicted patterns.

Now, however, a study from researchers at New York University’s Courant Institute of Mathematical Sciences describes a possible means of spotting infection surges before they happen through the analysis of online searches.

The researchers discovered a correlation between a surge in searches relating to activities outside the home — activities that could put people at risk of SARS-CoV-2 infection — and a rise in COVID-19 cases 10–14 days afterward. Infections fell when there was an increase in searches relating to stay-at-home activities.

Study author Anasse Bari, a clinical assistant professor at the Courant Institute, notes that experts have already successfully used data mining “in finance to generate data-driven investments, such as studying satellite images of cars in parking lots to predict businesses’ earnings.”

“Our research shows the same techniques could be applied to combatting a pandemic by spotting, ahead of time, where outbreaks are likely to occur,” says senior author Megan Coffee of the Division of Infectious Disease & Immunology at the New York University (NYU) Grossman School of Medicine.

Source: Medical News Today