Publications scientifiques
Autres affiliations : Unité de Modélisation Mathématique et Informatique des Systèmes Complexes
Kayode Oshinubi
Umar Muhammad Adam
Jacques Demongeot* (Auteur correspondant)
Diseases
OBJECTIVE: The objective of this article is to develop a robust method for forecasting the transition from endemic to epidemic phases in contagious diseases using COVID-19 as a case study. METHODS: Seven indicators are proposed for detecting the endemic/epidemic transition: variation coefficient, entropy, dominant/subdominant spectral ratio, skewness, kurtosis, dispersion index and normality index. Then, principal component analysis (PCA) offers a score built from the seven proposed indicators as the first PCA component, and its forecasting performance is estimated from its ability to predict the entrance in the epidemic exponential growth phase. RESULTS: This score is applied to the retro-prediction of endemic/epidemic transitions of COVID-19 outbreak in seven various countries for which the first PCA component has a good predicting power. CONCLUSION: This research offers a valuable tool for early epidemic detection, aiding in effective public health responses.
