# Accurate estimation of influenza epidemics using Google search data via ARGO

arXiv: https://arxiv.org/abs/1505.00864 · HTML: https://mlsanity.com/paper/1505.00864/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2015.

## Built on by (most cited first, 17 of 17)

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- [Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19](https://mlsanity.com/paper/2101.10280/) (2021)
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- [PEMS: Pre-trained Epidemic Time-series Models](https://mlsanity.com/paper/2311.07841/) (2023)
- [Estimating the Uncertainty of Neural Network Forecasts for Influenza Prevalence Using Web Search Activity](https://mlsanity.com/paper/2105.12433/) (2021)
- [Combining digital data streams and epidemic networks for real time outbreak detection](https://mlsanity.com/paper/2511.07163/) (2025)
- [Single Model for Influenza Forecasting of Multiple Countries by Multi-task Learning](https://mlsanity.com/paper/2107.01760/) (2021)
- [An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamics](https://mlsanity.com/paper/2201.09394/) (2022)
- [COVID-19 Hospitalizations Forecasts Using Internet Search Data](https://mlsanity.com/paper/2202.03869/) (2022)
- [Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Deep Learning-Based Time Series Forecasting](https://mlsanity.com/paper/2211.05267/) (2022)
- [Machine Learning for Infectious Disease Risk Prediction: A Survey](https://mlsanity.com/paper/2308.03037/) (2023)

## Abstract (arXiv)

Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistical foundation, ARGO outperforms all previously available Google-search-based tracking models, including the latest version of Google Flu Trends, even though it uses only low-quality search data as input from publicly available Google Trends and Google Correlate websites. ARGO not only incorporates the seasonality in influenza epidemics but also captures changes in people's online search behavior over time. ARGO is also flexible, self-correcting, robust, and scalable, making it a potentially powerful tool that can be used for real-time tracking of other social events at multiple temporal and spatial resolutions.