  A dynamical epidemic model optimized using genetic algorithm and cross
validation method to overcome the overfitting problem is proposed. The cross
validation procedure is applied so that available data are split into a
training subset used to fit the algorithm's parameters, and a smaller subset
used for validation. This process is tested on the countries of Italy, Spain,
Germany and South Korea before being applied to Algeria. Interestingly, our
study reveals an inverse relationship between the size of the training sample
and the number of generations required in the genetic algorithm. Moreover, the
enhanced compartmental model presented in this work is proven to be a reliable
tool to estimate key epidemic parameters and non-measurable asymptomatic
infected portion of the susceptible population in order to establish realistic
nowcast and forecast of epidemic's evolution. The model is employed to study
the COVID-19 outbreak dynamics in Algeria between February 25th and May 24th,
2020. The basic reproduction number and effective reproduction number on May
24th, after three months of the outbreak, are estimated to be 3.78 (95% CI
3.033-4.53) and 0.651 (95% CI 0.539-0.761) respectively. Disease incidence, CFR
and IFR are also calculated. Numerical programs developed for the purpose of
this study are made publicly accessible for reproduction and further use.
