Time warping between main epidemic time series in epidemiological surveillance

• Published in: PLoS computational biology 2023-12, Vol.19 (12), p.e1011757-e1011757
• Main Authors: Morel, Jean-David, Morel, Jean-Michel, Alvarez, Luis
• Format: Article
• Language: English
• Subjects: Analysis; Biology and Life Sciences; Casualties; Control; COVID-19; COVID-19 - epidemiology; Dynamic programming; Epidemics; Epidemiology; Fatalities; Forecasts and trends; France; Hospital patients; Hospitalization; Humans; Incidence; Mathematical models; Medicine and Health Sciences; Methods; Mortality; Numerical methods; Pandemics; People and places; Research and Analysis Methods; Severe acute respiratory syndrome coronavirus 2; Signal processing; Spain; Surveillance; Time Factors; Time series; Time-series analysis; Timing; United Kingdom
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1011757

The most common reported epidemic time series in epidemiological surveillance are the daily or weekly incidence of new cases, the hospital admission count, the ICU admission count, and the death toll, which played such a prominent role in the struggle to monitor the Covid-19 pandemic. We show that pairs of such curves are related to each other by a generalized renewal equation depending on a smooth time varying delay and a smooth ratio generalizing the reproduction number. Such a functional relation is also explored for pairs of simultaneous curves measuring the same indicator in two neighboring countries. Given two such simultaneous time series, we develop, based on a signal processing approach, an efficient numerical method for computing their time varying delay and ratio curves, and we verify that its results are consistent. Indeed, they experimentally verify symmetry and transitivity requirements and we also show, using realistic simulated data, that the method faithfully recovers time delays and ratios. We discuss several real examples where the method seems to display interpretable time delays and ratios. The proposed method generalizes and unifies many recent related attempts to take advantage of the plurality of these health data across regions or countries and time, providing a better understanding of the relationship between them. An implementation of the method is publicly available at the EpiInvert CRAN package.