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. 2023 Oct 3;11(4):135.
doi: 10.3390/diseases11040135.

Forecasting the Endemic/Epidemic Transition in COVID-19 in Some Countries: Influence of the Vaccination

Jules Waku  1 , Kayode Oshinubi  2 , Umar Muhammad Adam  3 , Jacques Demongeot  2
Affiliations

Forecasting the Endemic/Epidemic Transition in COVID-19 in Some Countries: Influence of the Vaccination

Jules Waku et al. Diseases. .

Abstract

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.

Keywords: COVID-19 wave prediction; contagious disease; endemic phase; endemic/epidemic transition forecasting; epidemic phase.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
COVID-19 outbreak. Daily new cases in the world (after [35,36]).
Figure 2
Figure 2
COVID-19 outbreak in Japan with Cumulative (resp. Daily new) cases in grey with a 7-day moving average in orange (resp. in grey with a 7-day moving average in light blue) (after [35]).
Figure 3
Figure 3
During France third (left) and USA first (right) endemic/epidemic transitions, the co-evolution of the Coefficient of Variation CV and Daily new cases. The x-axis represents time in days and the y-axes the Coefficient of Variation (in blue) and the Daily New Cases (in green).
Figure 4
Figure 4
The empirical distributions of daily new cases for France 3rd wave and USA 1st wave.
Figure 5
Figure 5
Co-evolution of CV and Entropy during France third (left) and USA first (right) waves.
Figure 6
Figure 6
Breakdown Parameters and New Cases (in grey) in Japan during COVID-19 Outbreak.
Figure 7
Figure 7
First Principal Component (blue) as predictor of COVID-19 Daily new case waves (green) in various countries: Japan (A), Nigeria (B), Cameroon (C), France (D), UK (E), USA (F) and India (G). The x-axis represents the time in days and the y-axis the PCA principal component. The red arrows correspond to local maxima of the first principal component.
Figure 8
Figure 8
ID index (in blue) as predictor of the epidemic waves for Japan COVID-19 outbreak, with Daily new cases superimposed (in green). The x-axis represents the time in days. The red arrows correspond to local maxima of the first principal component.
Figure 9
Figure 9
(A) Breakdown parameters of new cases before (left) and after (right) vaccination during Japan COVID-19 outbreak; (B) Influence of vaccination on waves, with PCA1 (in blue) and new cases (in green) before (left) and after (right) vaccination with percentage of fully vaccinated superimposed (in light blue); (C) PCA1 and ID for new cases before and after vaccination (fully vaccinated superimposed); (D) PCA1 for deaths before (left) and after (right) vaccination (fully vaccinated superimposed); (E) PCA1 and ID for deaths before (left) and after (right) vaccination (fully vaccinated superimposed). The x-axis represents the time (in months). The red arrows correspond to local maxima of the first principal component.
Figure 10
Figure 10
(A) Influence of vaccination on waves of Nigeria COVID-19 outbreak, with PCA1 (in blue) and daily new cases (in green) before (left) and after (right) vaccination with percentage of fully vaccinated people superimposed (in black); (B) PCA1 and ID for new cases and deaths before and after vaccination (percentage of fully vaccinated superimposed); (C) PCA1 for deaths before (left) and after (right) vaccination (fully vaccinated superimposed); (D) same as (A) and (C) for Cameroon with new cases (left) and deaths (right) superimposed (in green) with fully vaccinated superimposed (in black). The x-axis represents the time (in months). The red arrows correspond to local maxima of the first principal component.
Figure 11
Figure 11
(A) Influence of vaccination on waves of France COVID-19 outbreak, with daily new cases superimposed (in green) before (left) and after (right) vaccination with percentage of fully vaccinated people superimposed (in red); (B) same for deaths before and after vaccination; (C) same as (A) for the United Kingdom; (D) same as (C) for the US. The x-axis represents the time (in months). The red arrows correspond to local maxima of the first principal component.
Figure 12
Figure 12
(A) Influence of vaccination on waves USA COVID-19 outbreak, with daily new cases superimposed (in green) before (left) and after (right) vaccination with percentage of fully vaccinated people superimposed (in red); (B) same for deaths before and after vaccination; (C) same as (A) for India; (D) same as (C) for India. The x-axis represents the time (in months). The red arrows correspond to local maxima of the first principal component.
Figure 12
Figure 12
(A) Influence of vaccination on waves USA COVID-19 outbreak, with daily new cases superimposed (in green) before (left) and after (right) vaccination with percentage of fully vaccinated people superimposed (in red); (B) same for deaths before and after vaccination; (C) same as (A) for India; (D) same as (C) for India. The x-axis represents the time (in months). The red arrows correspond to local maxima of the first principal component.
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