ArticleBMC public health2024
Spatial Markov matrices for measuring the spatial dependencies of an epidemiological spread : case Covid'19 Madagascar.
Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Distribution dynamics and spatial dependence in healthcare resource allocation efficiency: evidence of path dependence and club formation in China.Frontiers in public health · 2026Article
- Bayesian inference of a spatially dependent semi-Markovian model with application to Madagascar Covid'19 data.PloS one · 2025Article
- Correction: Spatial Markov matrices for measuring the spatial dependencies of an epidemiological spread: case Covid'19 Madagascar.BMC public health · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis article applies a variant of the Markov chain that explicitly incorporates spatial effects. It is an extension of the Markov class allowing a more complete analysis of the spatial dimensions of transition dynamics. The aim is to provide a methodology for applying the explicit model to spatial dependency analysis.
methodsHere, the question is to study and quantify whether neighborhood context affects transitional dynamics. Rather than estimating a homogeneous law, the model requires the estimation of k transition laws each dependent on spatial neighbor state. This article used published data on confirmed cases of Covid'19 in the 22 regions of Madagascar. These data were discretized to obtain a discrete state of propagation intensity.
resultsThe analysis gave us the transition probabilities between Covid'19 intensity states knowing the context of neighboring regions, and the propagation time laws knowing the spatial contexts. The results showed that neighboring regions had an effect on the propagation of Covid'19 in Madagascar.
conclusionAfter analysis, we can say that there is spatial dependency according to these spatial transition matrices.
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Registered trials
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