COVID-19 Search Intensity Monitoring

Dear Colleagues,

The interactive figure below superimposes search data in UpToDate with a world map. It is based upon normalized search intensity (NSI) for selected COVID-19 terms, which is derived from the number of COVID-19 term searches divided by the total searches for a location. Previous studies^1,2 have suggested that this approach correlated with other outbreaks (such as MERS and Influenza) and in some cases an increase in search intensity preceded the documented outbreak. Our preliminary analysis shows that the search intensity correlates with actual COVID-19 cases as reported by Johns Hopkins University. In some regions that have been examined closely, the increase in search intensity was apparent before the cases were documented. We hope that the data offer a novel perspective on tracking the pandemic.

Circles represent search intensity (and presumed disease activity) with larger circles indicating more activity. The time frames can be adjusted to show changes in search intensity. Search intensity appears to correlate with disease activity at the beginning of an outbreak but whether this correlation continues over time is unknown.

The analysis is based upon more than 1.9 million UpToDate users, most of whom are front-line healthcare professionals. Regions where search data are insufficient for analysis are indicated. For comments or feedback please email covid19map@wolterskluwer.com.

References:

1. Thorner AR, Cao B, Jiang T, Warner AJ, Bonis PA. Correlation between UpToDate searches and reported cases of Middle East respiratory syndrome during outbreaks in Saudi Arabia. Open Forum Infectious Diseases 2016; 3(1): ofw043.
2. Santillana M, Nsoesie EO, Mekaru SR, Scales D, Brownstein JS. Using clinicians’ search query data to monitor influenza epidemics. Clinical Infectious Diseases 2014; 59(10): 1446.
3. Source of data for confirmed cases: https://github.com/CSSEGISandData/COVID-19

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