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Assessing individual risk and the latent transmission of COVID-19 in a population with an interaction-driven temporal model
Interaction-driven modeling of diseases over real-world contact data has been shown to promote the understanding of the spread of diseases in communities. This temporal modeling follows the path-preserving order and timing of the contacts, which are essential for accurate modeling. Yet, other import...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10415258/ https://www.ncbi.nlm.nih.gov/pubmed/37563358 http://dx.doi.org/10.1038/s41598-023-39817-9 |