Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description (version 1.0)
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Abstract (arXiv)
This report describes the aggregation and anonymization process applied to the initial version of COVID-19 Search Trends symptoms dataset (published at https://goo.gle/covid19symptomdataset on September 2, 2020), a publicly available dataset that shows aggregated, anonymized trends in Google searches for symptoms (and some related topics). The anonymization process is designed to protect the daily symptom search activity of every user with varepsilon-differential privacy for varepsilon = 1.68.
Key facts. Cited by 6 papers in the map (data through 13 Oct 2026). First posted on arXiv 2 Sep 2020.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 1; 2026: 1.
6 papers.
- 2022Differentiable Agent-based Epidemiologycited by 21
- 2022Data-Centric Epidemic Forecasting: A Surveycited by 8
- 2024Community search signatures as foundation features for human-centered geospatial modelingcited by 2
- 2025Combining digital data streams and epidemic networks for real time outbreak detectioncited by 1
- 2023Correcting for heterogeneity in real-time epidemiological indicatorscited by 0
- 2026Beyond Time Series: Spatial Reasoning for Epidemic Forecasting via Multimodal Learningcited by 0
- 2026Beyond Time Series: Spatial Reasoning for Epidemic Forecasting via Multimodal Learning20 Jun 2026
- 2025Combining digital data streams and epidemic networks for real time outbreak detection10 Nov 2025
- 2024Community search signatures as foundation features for human-centered geospatial modeling30 Oct 2024
- 2023Correcting for heterogeneity in real-time epidemiological indicators28 Sep 2023
- 2022Differentiable Agent-based Epidemiology20 Jul 2022
- 2022Data-Centric Epidemic Forecasting: A Survey19 Jul 2022