Forecasting the changes between endemic and epidemic phases of a contagious disease, with the example of COVID-19
Jacques Demongeot, Pierre Magal, and Kayode Oshnubi

TL;DR
This paper introduces a new forecasting method using statistical indicators and PCA to predict the transition from endemic to epidemic phases in contagious diseases like COVID-19, demonstrated on data from France, India, and Japan.
Contribution
The study develops a novel PCA-based score from distribution shape indicators to forecast epidemic transitions, improving prediction accuracy over existing methods.
Findings
The PCA-based score effectively predicts epidemic wave onset.
The method accurately retro-predicts phase transition dates.
Applied to COVID-19 data from three countries with promising results.
Abstract
Predicting the endemic/epidemic transition during the temporal evolution of a contagious disease. Methods: Defining indicators for detecting the transition endemic/epidemic, with four scalars to be compared, calculated from the daily reported news cases: coefficient of variation, skewness, kurtosis, and entropy. The indicators selected are related to the shape of the empirical distribution of the new cases observed over 14 days. This duration has been chosen to smooth out the effect of weekends when fewer new cases are registered. For finding a forecasting variable, we have used the PCA (principal component analysis), whose first principal component (a linear combination of the selected indicators) explains a large part of the observed variance and can then be used as a predictor of the phenomenon studied (here the occurrence of an epidemic wave). Results: A score has been built…
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Taxonomy
TopicsCOVID-19 epidemiological studies · Yersinia bacterium, plague, ectoparasites research
