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Simple Summary  The intrinsic dynamics of the propagation of a disease changes along an epidemic course, especially for long lasting epidemics such as the COVID-19. Indeed, the natural evolution of the pathogen and countermeasures such as quarantining, lockdown, social distancing and vaccination modify the transmission dynamics of the disease. With a view to match these theoretical changes to potential changes in observed epidemiological data, we designed a hybrid modeling framework where we integrated: (1) two growth curves for daily reported positive cases, differentiating the early epidemic phase and a second phase with a potentially different dynamics; (2) two logistic regression models for daily recoveries and deaths; and (3) a SIQR (Susceptible, Infective, Quarantined, Recovered) mechanistic model to provide an overview of the dynamics of the disease in the target population. This joint modeling approach allows explicit analytical expressions for the different compartments of the SIQR model, circumventing common identifiability issues in such models. The changes in the disease transmission pattern can be subjected to countermeasures so as to assess their effectiveness along time. For illustrative purposes, we applied the approach to COVID-19 data from West Africa. It turned out that the first imported COVID-19 case(s) in West Africa likely entered the region between 28 January and 7 February 2020. Moreover, the first measures implemented by West African authorities impacted the dynamics of the disease one month after the outbreak.    Abstract  The widely used logistic model for epidemic case reporting data may be either restrictive or unrealistic in presence of containment measures when implemented after an epidemic outbreak. For flexibility in epidemic case reporting data modeling, we combined an exponential growth curve for the early epidemic phase with a flexible growth curve to account for the potential change in growth pattern after implementation of containment measures. We also fitted logistic regression models to recoveries and deaths from the confirmed positive cases. In addition, the growth curves were integrated into a SIQR (Susceptible, Infective, Quarantined, Recovered) model framework to provide an overview on the modeled epidemic wave. We focused on the estimation of: (1) the delay between the appearance of the first infectious case in the population and the outbreak (“epidemic latency period”); (2) the duration of the exponential growth phase; (3) the basic and the time-varying reproduction numbers; and (4) the peaks (time and size) in confirmed positive cases, active cases and new infections. The application of this approach to COVID-19 data from West Africa allowed discussion on the effectiveness of some containment measures implemented across the region.

A Hybrid Modeling Technique of Epidemic Outbreaks with Application to COVID-19 Dynamics in West Africa