arXiv CS
open access
Asymmetric Bidirectional Predictive Dynamics in Google Search and COVID-19 Time Series: A Multi-Horizon Forecast-Error Analysis
Online search activity is widely used as a digital trace of information seeking during epidemics, but its predictive value is often reduced to correlation, a selected lag, or a single forecast-error score. We examine eight pre-specified Google Trends-COVID-19 pairs in both directions using matched L
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Forecast Number of Covid-19 Cases Worldwide#966262
- Generative diffusion models for spatiotemporal influenza forecasting#1070813
- Forecastability of infectious disease time series: are some seasons and pathogens intrinsically more difficult to forecast?#125401
- Ensemble forecasts of COVID-19 activity to support Australia's pandemic response: 2020-22.#143382
- Ensemble forecasts of COVID-19 activity to support Australia’s pandemic response: 2020–22#123697
Record · ID 1394235
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive