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Modeling COVID-19 daily cases in Senegal using a generalized Waring regression model
Lucien Gning1, Cheikh Ndour2, J M Tchuenche3,4
1Laboratoire d'études et de recherches en statistiques et développement, Université Gaston BERGER, Saint-Louis, Senegal.
Physica A
|March 22, 2022
Summary
This study forecasts daily COVID-19 cases in Senegal using the generalized Waring regression model. This statistical model better explains disease dynamics and fits the data compared to other count regression models.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- The COVID-19 pandemic caused global economic and social disruptions.
- Senegal and West Africa experienced relatively low infection-induced mortality rates.
- Accurate forecasting of daily COVID-19 cases is crucial for public health interventions.
Purpose of the Study:
- To forecast daily confirmed COVID-19 cases in Senegal.
- To evaluate the performance of the generalized Waring regression model for COVID-19 case forecasting.
- To compare the generalized Waring model with other count regression models.
Main Methods:
- Utilized count regression statistical models.
- Specifically employed the generalized Waring regression model.
- Compared its performance against models like negative binomial regression.
Main Results:
- The generalized Waring regression model demonstrated a superior fit to the COVID-19 case data in Senegal.
- The model effectively captured intrinsic characteristics of the disease dynamics.
- It outperformed other common count regression models in this context.
Conclusions:
- The generalized Waring regression model is a robust tool for forecasting daily COVID-19 cases in Senegal.
- This model's ability to account for unobserved factors enhances its predictive power.
- Findings support the use of advanced statistical models for understanding and managing infectious disease outbreaks.
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