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Raberahona, M., Rakotomalala, R., Rakotomijoro, E., Rahaingoalidera, T., Andry, C. E., Mamilaza, N., Razafindrabekoto, L., Rafanomezantsoa, E., Andriananja, V., Andrianasolo, R.L., Razafimahefa, S.H., Rakotoarivelo, R.A. and Randria, M. (2020) Clinical and Epidemiological Features Discriminating Confirmed COVID-19 Patients from SARS-CoV-2 Negative Patients at Screening Centers in Madagascar. International Journal of Infectious Diseases, 103, 6-8.
https://doi.org/10.1016/j.ijid.2020.11.151

has been cited by the following article:

• TITLE: Probabilistic Modelling of COVID-19 Dynamic in the Context of Madagascar

  AUTHORS: Angelo Raherinirina, Tsilefa Stefana Fandresena, Aimé Richard Hajalalaina, Haja Rabetafika, Rivo Andry Rakotoarivelo, Fontaine Rafamatanantsoa

  KEYWORDS: Modified SEIR Model, COVID-19 Madagascar, Basic Reproduction Number, Markov Chain Continuous Time

  JOURNAL NAME: Open Journal of Modelling and Simulation, Vol.9 No.3, May 24, 2021

  ABSTRACT: We propose a probabilistic approach to modelling the propagation of the coronavirus disease 2019 (COVID-19) in Madagascar, with all its specificities. With the strategy of the Malagasy state, which consists of isolating all suspected cases and hospitalized confirmed case, we get an epidemic model with seven compartments: susceptible (S), Exposed (E), Infected (I), Asymptomatic (A), Hospitalized (H), Cured (C) and Death (D). In addition to the classical deterministic models used in epidemiology, the stochastic model offers a natural representation of the evolution of the COVID-19 epidemic. We inferred the models with the official data provided by the COVID-19 Command Center (CCO) of Madagascar, between March and August 2020. The basic reproduction number R0 and the other parameters were estimated with a Bayesian approach. We developed an algorithm that allows having a temporal estimate of this number with confidence intervals. The estimated values are slightly lower than the international references. Generally, we were able to obtain a simple but effective model to describe the spread of the disease.