The 10-Day Window: Intervention Timing, Parameter Uncertainty, and the Collapse of Epidemic Forecast Reliability
Year: 2026
Type: article
Abstract: Epidemic forecasting models are routinely used to guide public health decisions, yet the temporal boundary beyond which parameter uncertainty renders forecasts operationally useless remains poorly characterised. We introduce a formal, computable predictability horizon—defined as the first day on which the interquartile range of an ensemble of SEIR trajectories exceeds 20% of the ensemble median cumulative incidence—and demonstrate that this horizon is a stable, reproducible quantity jointly determined by the width of parameter uncertainty and the timing of nonpharmaceutical interventions (NPIs). We simulated 1,000-member ensembles of a standard SEIR model using a literature-calibrated parameter space for a generic respiratory pathogen (R0 ≈ 2.1–4.9, consistent with pre-variant SARS-CoV-2). Under nominal uncertainty, the predictability horizon is 13 days. Doubling the parameter envelope collapses it to 7–8 days; halving it extends it to 23 days. A 40% reduction in transmission rate (β), analogous to moderate NPI packages, extends the horizon by up to 6 days when applied at Day 0, with diminishing returns through Day 5, and provides no measurable gain when applied after Day 10. A complementary σ-reduction intervention (vaccination-like) produces a smaller absolute horizon gain through a mechanistically distinct pathway. A phase diagram mapping intervention timing × parameter uncertainty to predictability horizon length is presented as a decision-support contribution currently absent from the epidemiological literature.
Author: Abeja, Keegan Bryce
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7161721960
FWCI:
Cites:
Cited by:
Related to: