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A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases
Modeling the spread of infectious diseases in space and time needs to take care of complex dependencies and uncertainties. Machine learning methods, and neural networks, in particular, are useful in modeling this sort of complex problems, although they generally lack of probabilistic interpretations...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer Berlin Heidelberg
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8787453/ https://www.ncbi.nlm.nih.gov/pubmed/35095341 http://dx.doi.org/10.1007/s00477-021-02168-w |