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A compartmental epidemic model based on genetic fitting algorithm and a cross-validation method to overcome the overfitting problem are proposed. This generic enhanced SEIR model allows to estimate approximate nowcast and forecast of epidemic evolution including key epidemic parameters and non-measurable asymptomatic infected portion of the susceptible population. The model is used to study COVID-19 outbreak dynamics in Algeria between February 25th and May 24th. The Basic reproduction number is estimated to 3.78 (95% CI 3.033-4.53) and effective reproduction number on May 24th after three months of the outbreak is estimated to 0.651 (95% CI 0.539-0.761). The Infections peak time is predicted to the end of April while active cases peak time is predicted to the end of May 2020. The disease incidence, CFR and IFR are calculated. Information provided by this study could help establish a realistic assessment of the situation in Algeria for the time being, inform predictions about potential future evolution, and guide the design of appropriate public health measures.

A mathematical epidemic model using genetic fitting algorithm with cross-validation and application to early dynamics of COVID-19 in Algeria