Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Filtering and improved Uncertainty Quantification in the dynamic
  estimation of effective reproduction numbers   The effective reproduction number $R_t$ measures an infectious disease's
transmissibility as the number of secondary infections in one reproduction time
in a population having both susceptible and non-susceptible hosts. Current
approaches do not quantify the uncertainty correctly in estimating $R_t$, as
expected by the observed variability in contagion patterns. We elaborate on the
Bayesian estimation of $R_t$ by improving on the Poisson sampling model of Cori
et al. (2013). By adding an autoregressive latent process, we build a Dynamic
Linear Model on the log of observed $R_t$s, resulting in a filtering type
Bayesian inference. We use a conjugate analysis, and all calculations are
explicit. Results show an improved uncertainty quantification on the estimation
of $R_t$'s, with a reliable method that could safely be used by non-experts and
within other forecasting systems. We illustrate our approach with recent data
from the current COVID19 epidemic in Mexico.

1
The categories of this paper are: cs
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics