Real-time forecasting of data revisions in epidemic surveillance streams
Jingjing Tang, Aaron Rumack, Bryan Wilder, Roni Rosenfeld, Roger Dimitri Kouyos, Philipp Martin Altrock, Roger Dimitri Kouyos, Philipp Martin Altrock, Roger Dimitri Kouyos, Philipp Martin Altrock
TL;DR
This paper introduces Delphi-RF, a fast and accurate method for forecasting data revisions in real-time epidemic surveillance.
Contribution
Delphi-RF uses nonparametric quantile regression to model data revisions, improving accuracy and computational efficiency for public health monitoring.
Findings
Delphi-RF provides accurate forecasts of finalized surveillance values for early-stage epidemic data.
The method improves computational efficiency by 10-100x compared to existing approaches.
It works well for both count and proportion data in various disease surveillance contexts.
Abstract
Epidemic data streams undergo frequent revisions due to reporting delays (“backfill”) and other factors. Relying on tentative surveillance values can seriously degrade the quality of situational awareness, forecasting accuracy and decision-making. We introduce Delphi Revision Forecast (Delphi-RF), a real-time data revision forecasting framework using nonparametric quantile regression, applicable to both counts and proportions (fractions) in public health reporting. By incorporating all available revisions up to a given estimation date, Delphi-RF models revision dynamics and generates distributional forecasts of finalized surveillance values. Applied to daily COVID-19 data (insurance claims, antigen tests, confirmed cases) and weekly dengue and influenza-like illness (ILI) case counts, Delphi-RF delivers accurate revision forecasts, particularly in early reporting stages. In addition, it…
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Taxonomy
TopicsData-Driven Disease Surveillance · COVID-19 epidemiological studies · Respiratory viral infections research