Bayesian inference of a spatially dependent semi-Markovian model with application to Madagascar Covid’19 data
Angelo Raherinirina, Stefana Tabera Tsilefa, Tsidikaina Nirilanto, Solym M. Manou-Abi
TL;DR
The paper introduces a spatially dependent model to study how diseases like Covid-19 spread, using Bayesian methods on Madagascar's data.
Contribution
A novel semi-Markovian model with spatial dependence is proposed and applied to real-world disease data.
Findings
The model captures spatial propagation timescales and regional disease spread influenced by neighboring states.
Bayesian inference applied to Madagascar's data reveals the impact of neighboring regions on disease dynamics.
The study highlights the importance of spatial dependencies in understanding and modeling disease propagation.
Abstract
This article presents an approach to stochastic analysis of disease dynamics. We develop an explicit semi-Markovian model that accounts for spatial dependence, operating in discrete time over a finite state space. The model allowed us to have a propagation model conditioned by neighboring states and quantifies two key characteristics : spatial propagation timescales and propagation law in a region dependent on neighboring states. The model is inferred from data collected on the spread of Covid’19 in Madagascar’s 22 regions, using the Bayesian approach to get a better idea of model parameter values. The result has demonstrated the effect of neighborhoods on the propagation dynamics of diseases. We conclude with a discussion of potential future theoretical developments.
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Taxonomy
TopicsCOVID-19 epidemiological studies · Complex Systems and Time Series Analysis · Mathematical and Theoretical Epidemiology and Ecology Models