2022/02/17 by J. Sunil Rao, Tianhao Liu, Rao, J. Sunil +3 · 1 citation
Computer Science · Economics, Econometrics and Finance · Mathematics · #92D25 (Primary) 92C60 92B15 62P10 62M10 (Secondary) #Anomaly Detection Techniques and Applications #COVID-19 epidemiological studies #Complex Systems and Time Series Analysis #Data Analysis #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Methodology (stat.ME) #Populations and Evolution (q-bio.PE) #Quantitative Methods (q-bio.QM) #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2202.08928
openalex publication_date 2022/02/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We argue that information from countries who had earlier COVID-19 surges can be used to inform another country's current model, then generating what we call back-to-the-future (BTF) projections. We show that these projections can be used to accurately predict future COVID-19 surges prior to an inflection point of the daily infection curve. We show, across 12 different countries from all populated continents around the world, that our method can often predict future surges in scenarios where the traditional approaches would always predict no future surges. However, as expected, BTF projections cannot accurately predict a surge due to the emergence of a new variant. To generate BTF projections, we make use of a matching scheme for asynchronous time series combined with a response coaching SIR model.