Modelling the COVID-19 Infection Trajectory: A Piecewise Linear Quantile Trend Model
نویسندگان
چکیده
Abstract We propose a piecewise linear quantile trend model to analyse the trajectory of COVID-19 daily new cases (i.e. infection curve) simultaneously across multiple quantiles. The is intuitive, interpretable and naturally captures phase transitions epidemic growth rate via change-points. Unlike mean least squares estimation, our quantile-based approach robust outliers, heteroscedasticity (commonly exhibited by curves) automatically delivers both point interval forecasts with minimal assumptions. Building on self-normalized (SN) test statistic, this paper proposes novel segmentation algorithm for change-point estimation. Theoretical guarantees such as consistency are established under mild verifiable Using proposed method, we curves in 35 major countries discover patterns potentially relevant implications effectiveness pandemic responses different countries. A simple change-adaptive two-stage forecasting scheme further designed generate short-term prediction cumulative shown deliver accurate forecast valuable public health decision-making.
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ژورنال
عنوان ژورنال: Journal of The Royal Statistical Society Series B-statistical Methodology
سال: 2021
ISSN: ['1467-9868', '1369-7412']
DOI: https://doi.org/10.1111/rssb.12453