We propose a probabilistic approach to modelling the propagation of the coronavirus disease 2019 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 R 0 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.

For Madagascar, with the uncertainty over vaccines against the novel coronavirus 2019 and its variants, non-pharmaceutical approach is widely used. Our objective is to propose a mathematical control model which will serve as a tool to help decision-makers in the strategy to be implemented to better face the pandemic. By separating asymptomatic cases which are often not reported and symptomatic who are hospitalized after tests; we develop a mathematical model of the propagation of covid-19 in Madagascar, by integrating control strategies. We study the stability of the model by expressing the basic reproduction number using the next-generation matrix. Simulation with different parameters shows the effects of non-pharmaceutical measures on the speed of the disease spread. By integrating a control parameter linked to compliance with barrier measures in the virus propagation equation, we were able to show the impacts of the implementation of social distancing measures on the basic reproduction number. The strict application of social distancing measures and total confinement is unfavorable for economic situation even if they allow the contamination to be reduced quickly. Without any restrictions, the disease spreads at high speed and the peak is reached fairly quickly. In this condition, hospitals are overwhelmed and the death rate increases rapidly. With 50% respect for non-pharmaceutical strategies such as rapid detection and isolation of positive cases and barrier gestures; the basic reproduction number R 0 can go down from 3 to 1.7. The pressures on the economic and social situation are rather viable. It is the most suitable for the Malagasy health system. The results proposed are a way to control the spread of the disease and limit its devastation in a country like Madagascar.

International audience We present a Markov model of a land-use dynamic along a forest corridor of Madagascar. A first approach by the maximum likelihood approach leads to a model with an absorbing state. We study the quasi-stationary distribution law of the model and the law of the hitting time of the absorbing state. According to experts, a transition not present in the data must be added to the model: this is not possible by the maximum likelihood method and we make of the Bayesian approach. We use a Markov chain Monte Carlo method to infer the transition matrix which in this case admits an invariant distribution law. Finally we analyze the two identified dynamics. Nous présentons un modèle de Markov d’une dynamique d’utilisation des sols le long d’uncorridor forestier de Madagascar. Une première approche par maximum de vraisemblance conduit àun modèle avec un état absorbant. Nous étudions la loi de probabilité quasi-stationnaire du modèle etla loi du temps d’atteinte de l’état absorbant. Selon les experts, une transition qui n’est pas présentedans les données doit néanmoins être ajoutée au modèle: ceci n’est pas possible par la méthodedu maximum de vraisemblance et nous devons faire appel à une approche bayésienne. Nous faisonsappel à une technique d’approximation de Monte Carlo par chaîne de Markov pour identifier la matricede transition qui dans ce cas admet une loi de probabilité invariante. Enfin nous analysons les deuxdynamiques ainsi identifiés.

In this paper, we propose a models of process chain and knowledge-based of meteorological reanalysis datasets that help scientists, working in the field of climate and in particular of the rainfall evolution, to solve uncertainty of spatial resources (data, process)

Natural disasters are not negligible factors that have significant impacts on a country's development. Madagascar cannot escape cyclones, floods and drought due to its geographical situation. The objective in this work is to assess the risks and vulnerability to these hazards in order to strengthen the resilience of the Malagasy population. Our approach is based on multi-criteria spatial analysis using the Analytical Hierarchy Process (AHP). The results form decision spatial information that can be used at the strategic level of natural risk and disaster management. This work focuses on the degree of vulnerability and it was found in this study that the Androy and Atsimo-Atsinanana regions are the most vulnerable to major hazards in Madagascar not only because of their exposure to risk but also because of their very low socio-economic status.