A mathematical epidemic model using genetic fitting algorithm with cross-validation and application to early dynamics of COVID-19 in Algeria
Year: 2020
Type: preprint
Abstract: A compartmental epidemic model based on genetic fitting algorithm and using cross-validation method to overcome the overfitting problem is 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. Basic reproduction number on Feb. 25th 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). Infection 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.
Source: arXiv (Cornell University)
Institution: University Frères Mentouri Constantine 1
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3035695664