has been cited by the following article(s):
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[1]
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Stochastic approximate inference of latent information in epidemic model: A data-driven approach
Signal Processing,
2025
DOI:10.1016/j.sigpro.2025.109919
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[2]
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Bayesian inference of a spatially dependent semi-Markovian model with application to Madagascar Covid’19 data
PLOS One,
2025
DOI:10.1371/journal.pone.0326264
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[3]
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African Conference on Research in Computer Science and Applied Mathematics
Trends in Mathematics,
2025
DOI:10.1007/978-3-031-90510-0_6
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[4]
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Spatial Markov matrices for measuring the spatial dependencies of an epidemiological spread : case Covid’19 Madagascar
BMC Public Health,
2024
DOI:10.1186/s12889-024-19654-9
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[5]
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Probabilistic analysis of COVID-19 transmission in Kenya using Markov chain
International Journal of ADVANCED AND APPLIED SCIENCES,
2023
DOI:10.21833/ijaas.2023.03.014
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[6]
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Reproduction Factor Based Latent Epidemic Model Inference: A Data-Driven Approach Using COVID-19 Datasets
IEEE Journal of Biomedical and Health Informatics,
2023
DOI:10.1109/JBHI.2022.3213175
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[7]
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Cross-sectional cycle threshold values reflect epidemic dynamics of COVID-19 in Madagascar
Epidemics,
2022
DOI:10.1016/j.epidem.2021.100533
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[8]
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Analysis of COVID-19 evolution based on testing closeness of sequential data
Japanese Journal of Statistics and Data Science,
2022
DOI:10.1007/s42081-021-00144-w
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