• Source name: google-symptoms
• Earliest issue available: November 30, 2020
• Number of data revisions since May 19, 2020: 0
• Date of last change: Never
• Available for: county, MSA, HRR, state (see geography coding docs)
• Time type: day (see date format docs)

## Overview

This data source is based on the COVID-19 Search Trends symptoms dataset. Using this search data, we estimate the volume of searches mapped to symptoms related to COVID-19 such as anosmia (lack of smell) and ageusia(lack of taste). The resulting daily dataset for each region shows the relative frequency of searches for each symptom. The signals are measured in arbitrary units that are normalized for overall search users in the region and scaled by the maximum value of the normalized popularity within a geographic region across a specific time range. Thus, values are NOT comparable across geographic regions. Larger numbers represent increased releative popularity of symptom-related searches.

Signal Description
anosmia_raw_search Google search volume for anosmia-related searches, in arbitrary units that are normalized for overall search users
Earliest date available: 2020-02-13
anosmia_smoothed_search Google search volume for anosmia-related searches, in arbitrary units that are normalized for overall search users, smoothed by 7-day average
Earliest date available: 2020-02-20
ageusia_raw_search Google search volume for ageusia-related searches, in arbitrary units that are normalized for overall search users
Earliest date available: 2020-02-13
ageusia_smoothed_search Google search volume for ageusia-related searches, in arbitrary units that are normalized for overall search users, smoothed by 7-day average
Earliest date available: 2020-02-20
sum_anosmia_ageusia_raw_search The sum of Google search volume for anosmia and ageusia related searches, in an arbitrary units that are normalized for overall search users
Earliest date available: 2020-02-13
sum_anosmia_ageusia_smoothed_search The sum of Google search volume for anosmia and ageusia related searches, in an arbitrary units that are normalized for overall search users, smoothed by 7-day average
Earliest date available: 2020-02-20

## Estimation

The sum_anosmia_ageusia_raw_search signals are simply the raw sum of the values of anosmia_raw_search and ageusia_raw_search, but not the union of anosmia and ageusia related searches. This is because the data volume is calculated based on search queries. A single search query can be mapped to more than one symptom. Currently, Google does not provide intersection/union data. Users should be careful when considering such signals.

## Geographical Aggregation

The state-level and county-level raw_search signals for specific symptoms such as anosmia and ageusia are taken directly from the COVID-19 Search Trends symptoms dataset without changes. We aggregate the county-level data to the MSA and HRR levels using the population-weighted average. For MSAs/HRRs that include counties that have no data provided due to quality or privacy issues for a certain day, we simply assume the values to be 0 during aggregation. The values for MSAs/HRRs with no counties having non-NaN values will not be reported. Thus, the resulting MSA/HRR level data does not fully match the actual MSA/HRR level data (which we are not provided).

## Lag and Backfill

Google does not currently update the search data daily, but usually twice a week. Each update will usually extend the coverage to within three days of the day of the update. As a result the delay can range from 3 to 10 days or even more. We check for updates every day and provide the most up-to-date data.

## Limitations

When daily volume in a region does not meet quality or privacy thresholds, set by Google, no daily value is reported. Weekly data may be available from Google in these cases, but we do not yet support importation using weekly data.

Google uses differential privacy, which adds artificial noise to the raw datasets to avoid identifying any individual persons without affecting the quality of results.

Google normalizes and scales time series values to determine the relative popularity of symptoms in searches within each geographical region individually. This means that the resulting values of symptom popularity are NOT comparable across geographic regions.

More details about the limitations of this dataset are available in Google’s Search Trends symptoms dataset documentation.