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NobBS: Nowcasting by Bayesian Smoothing
A Bayesian approach to estimate the number of occurred-but-not-yet-reported cases from incomplete, time-stamped reporting data for disease outbreaks. 'NobBS' learns the reporting delay distribution and the time evolution of the epidemic curve to produce smoothed nowcasts in both stable and time-varying case reporting settings, as described in McGough et al. (2020) <doi:10.1371/journal.pcbi.1007735>.
Package details |
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| Author | Rami Yaari [cre, aut], Rodrigo Zepeda Tello [aut, ctb], Sarah McGough [aut, ctb], Nicolas Menzies [aut], Marc Lipsitch [aut], Michael Johansson [aut], Teresa Yamana [ctb], Matteo Perini [ctb] |
| Maintainer | Rami Yaari <ry2460@cumc.columbia.edu> |
| License | MIT + file LICENSE |
| Version | 1.1.0 |
| Package repository | View on CRAN |
| Installation |
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