Genetic algorithm with cross validation-based epidemic model and application to early diffusion of COVID-19 in Algeria

Rouabah, Mohamed Taha; Tounsi, Abdellah; Belaloui, Nacer Eddine

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.