Bayesian inference of a spatially dependent semi-Markovian model with application to Madagascar Covid'19 data.
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...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0326264 |
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| _version_ | 1849428058852818944 |
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| author | Angelo Raherinirina Stefana Tabera Tsilefa Tsidikaina Nirilanto Solym M Manou-Abi |
| author_facet | Angelo Raherinirina Stefana Tabera Tsilefa Tsidikaina Nirilanto Solym M Manou-Abi |
| author_sort | Angelo Raherinirina |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ce6af030484545cb9bfa3d64427df05b |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-ce6af030484545cb9bfa3d64427df05b2025-08-20T03:28:48ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032626410.1371/journal.pone.0326264Bayesian inference of a spatially dependent semi-Markovian model with application to Madagascar Covid'19 data.Angelo RaherinirinaStefana Tabera TsilefaTsidikaina NirilantoSolym M Manou-AbiThis 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.https://doi.org/10.1371/journal.pone.0326264 |
| spellingShingle | Angelo Raherinirina Stefana Tabera Tsilefa Tsidikaina Nirilanto Solym M Manou-Abi Bayesian inference of a spatially dependent semi-Markovian model with application to Madagascar Covid'19 data. PLoS ONE |
| title | Bayesian inference of a spatially dependent semi-Markovian model with application to Madagascar Covid'19 data. |
| title_full | Bayesian inference of a spatially dependent semi-Markovian model with application to Madagascar Covid'19 data. |
| title_fullStr | Bayesian inference of a spatially dependent semi-Markovian model with application to Madagascar Covid'19 data. |
| title_full_unstemmed | Bayesian inference of a spatially dependent semi-Markovian model with application to Madagascar Covid'19 data. |
| title_short | Bayesian inference of a spatially dependent semi-Markovian model with application to Madagascar Covid'19 data. |
| title_sort | bayesian inference of a spatially dependent semi markovian model with application to madagascar covid 19 data |
| url | https://doi.org/10.1371/journal.pone.0326264 |
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