Article

Rethinking respiratory disease forecasting: temporal heterogeneity between surveillance predictors and outcomes drives forecast instability

2026-08-22

Abstract excerpt

Since the COVID-19 pandemic, forecasting hubs and non-traditional respiratory disease surveillance streams have become increasingly common. However, many forecasting approaches assume that relationships between surveillance predictors and disease outcomes remain stable over time and that incorporating additional historical data will improve forecast performance. To evaluate these assumptions in a real-world settin...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: d090c79a-f6a7-50ca-84ff-dae5bac0cdc7
DOI: 10.64898/2026.08.19.26360833

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Rethinking respiratory disease forecasting: temporal heterogeneity between surveillance predictors and outcomes drives forecast instabilityDOI 10.64898/2026.08.19.26360833Read

Performance evaluation of RespiCast ensemble forecasts for primary care syndromic indicators of viral respiratory disease in EuropeSemantic proximity Read

A Machine Learning-enabled SIR Model for Adaptive and Dynamic Forecasting of COVID-19Semantic proximity Read

Forecastability of infectious disease time series: are some seasons and pathogens intrinsically more difficult to forecast?Semantic proximity Read

A Multi-pathogen Hospitalization Forecasting Model for the United States: An Optimized Geo-Hierarchical Ensemble FrameworkSemantic proximity Read

A Decade of CDC FluSight Influenza ForecastingSemantic proximity Read

Multi-model forecasting of respiratory disease activity in Germany during the 2024–2025 seasonSemantic proximity Read

Forecasting the daily COVID-19 incidence in large and small communities -- A comparative study of forecasting approaches for disease surveillanceSemantic proximity Read

A new surveillance landscape for seasonal influenza?: Comparing lab-confirmed influenza hospitalizations with other syndromic and surveillance data sources for the state of CaliforniaSemantic proximity Read

Forecasting COVID-19 with Temporal Hierarchies and Ensemble MethodsSemantic proximity Read

Inferring temporal trends of multiple pathogens, variants, subtypes or serotypes from routine surveillance dataSemantic proximity Read

A dynamic ensemble model for short-term forecasting in pandemic situationsSemantic proximity Read

Assessing the accuracy of California county level COVID-19 hospitalization forecasts to inform public policy decision makingSemantic proximity Read