Probabilistic Modelling of COVID-19 Dynamic in the Context of Madagascar

建模与仿真(英文) Pub Date : 2021-05-21 DOI:10.4236/OJMSI.2021.93014

Angelo Raherinirina, Tsilefa Stefana Fandresena, A. R. Hajalalaina, H. Rabetafika, R. Rakotoarivelo, Fontaine Rafamatanantsoa

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引用次数: 5

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.

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马达加斯加地区COVID-19动态概率建模

我们提出了一种概率方法来模拟2019年冠状病毒病(COVID-19)在马达加斯加的传播及其所有特异性。根据马达加斯加国家隔离所有疑似病例和住院确诊病例的策略，我们得到了一个由七个隔间组成的流行病模型:易感(S)、暴露(E)、感染(I)、无症状(A)、住院(H)、治愈(C)和死亡(D)。除了流行病学中使用的经典确定性模型外，随机模型还提供了COVID-19流行病演变的自然表征。我们根据2020年3月至8月马达加斯加COVID-19指挥中心(CCO)提供的官方数据推断了这些模型。用贝叶斯方法估计了基本繁殖数R0和其他参数。我们开发了一种算法，可以对这个数字进行具有置信区间的时间估计。估计值略低于国际参考值。总的来说，我们能够得到一个简单而有效的模型来描述疾病的传播。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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建模与仿真(英文)

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