Predicting the COVID-19 epidemic in Algeria using the SIR model
Boudrioua, M. S.; Boudrioua, A.
Show abstract
The aim of this study is to predict the daily infected cases with Coronavirus (COVID-19) in Algeria. We apply the SIR model on data from 25 February 2020 to 24 April 2020 for the prediction. Following Huang et al (12), we develop two SIR models, an optimal model and a model in a worst-case scenario COVID-19. We estimate the parameters of our models by minimizing the negative log likelihood function using the Nelder-Mead method. Based on the simulation of the two models, the epidemic peak of COVID-19 is predicted to attain 24 July 2020 in a worst-case scenario, and the COVID-19 disease is expected to disappear in the period between September 2020 and November 2020 at the latest. We suggest that Algerian authorities need to implement a strict containment strategy over a long period to successfully decrease the epidemic size, as soon as possible.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Analytical Solution of a New SEIR Model Based on Latent Period-Infectious Period Chronological Order 97%
- Modeling the initial phase of COVID-19 epidemic: The role of age and disease severity in the Basque Country, Spain 97%
- Characterizing Two Outbreak Waves of COVID-19 in Spain Using Phenomenological Epidemic Modelling 97%
Similar papers in this journal
Similar papers in this journal
- Distribution of Incubation Period of COVID-19 in the Canadian Context: Modeling and Computational Study 97%
- Model Based Estimation of the SARS-CoV-2 Immunization Level in Austria and Consequences for Herd Immunity Effects 96%
- Estimation of COVID-19 recovery and decease periods in Canada using machine learning algorithms 96%
Similar papers in this journal
Similar papers in this journal
- An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City 95%
- The Burr distribution as a model for the delay between key events in an individual’s infection history 95%
- Early transmission of Plasmodium vivax sensitive strain slows down emergence of drug resistance 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.