# Tracking COVID-19 using online search

arXiv: https://arxiv.org/abs/2003.08086 · HTML: https://mlsanity.com/paper/2003.08086/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact](https://mlsanity.com/paper/2106.02359/) (2021)
- [Data-Centric Epidemic Forecasting: A Survey](https://mlsanity.com/paper/2207.09370/) (2022)
- [Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts](https://mlsanity.com/paper/2205.10408/) (2022)
- [COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction](https://mlsanity.com/paper/2105.00620/) (2021)
- [Estimating the Uncertainty of Neural Network Forecasts for Influenza Prevalence Using Web Search Activity](https://mlsanity.com/paper/2105.12433/) (2021)
- [Transfer Learning Approaches for Knowledge Discovery in Grid-based Geo-Spatiotemporal Data](https://mlsanity.com/paper/2110.00841/) (2021)
- [Combining digital data streams and epidemic networks for real time outbreak detection](https://mlsanity.com/paper/2511.07163/) (2025)

## Abstract (arXiv)

Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search query frequencies to gain insights about the prevalence of COVID-19 in multiple countries. We first develop unsupervised modelling techniques based on associated symptom categories identified by the United Kingdom's National Health Service and Public Health England. We then attempt to minimise an expected bias in these signals caused by public interest --as opposed to infections-- using the proportion of news media coverage devoted to COVID-19 as a proxy indicator. Our analysis indicates that models based on online searches precede the reported confirmed cases and deaths by 16.7 (10.2 - 23.2) and 22.1 (17.4 - 26.9) days, respectively. We also investigate transfer learning techniques for mapping supervised models from countries where the spread of disease has progressed extensively to countries that are in earlier phases of their respective epidemic curves. Furthermore, we compare time series of online search activity against confirmed COVID-19 cases or deaths jointly across multiple countries, uncovering interesting querying patterns, including the finding that rarer symptoms are better predictors than common ones. Finally, we show that web searches improve the short-term forecasting accuracy of autoregressive models for COVID-19 deaths. Our work provides evidence that online search data can be used to develop complementary public health surveillance methods to help inform the COVID-19 response in conjunction with more established approaches.