nmode's Git Repositories - Rnaught/blob - vignettes/Rnaught.Rmd

   1 ---
   2 title: "Introduction to Rnaught"
   3 output: rmarkdown::html_vignette
   4 vignette: >
   5   %\VignetteIndexEntry{Introduction to Rnaught}
   6   %\VignetteEngine{knitr::rmarkdown}
   7   %\VignetteEncoding{UTF-8}
   8 ---
   9
  10 ```{r, include = FALSE}
  11 knitr::opts_chunk$set(
  12   collapse = TRUE,
  13   comment = "#>"
  14 )
  15 ```
  16
  17 The goal of Rnaught is to provide a collection of methods for estimating the
  18 [basic reproduction number](
  19 https://en.wikipedia.org/wiki/Basic_reproduction_number) ($R_0$) of infectious
  20 diseases.
  21
  22
  23 ## Estimators
  24
  25 The following estimators are currently available:
  26
  27   * `id()`: Incidence Decay (ID)
  28   * `idea()`: Incidence Decay and Exponential Adjustment (IDEA)
  29   * `seq_bayes()`: Sequential Bayes (seqB)
  30   * `wp()`: White and Pagano (WP)
  31
  32 Every estimator employs its own model, has its own set of parameters, and is
  33 better suited to particular scenarios. You should consult each method's
  34 documentation for technical details. Below is a short example computing
  35 estimates for a given set of case counts.
  36
  37 ```{r}
  38 library(Rnaught)
  39
  40 # Weekly case counts.
  41 cases <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4)
  42
  43 # Compute estimates of R0, assuming a serial interval of five days.
  44 mu <- 5 / 7
  45
  46 id(cases, mu)
  47 idea(cases, mu)
  48 seq_bayes(cases, mu)
  49 wp(cases, mu)
  50 ```
  51
  52
  53 ## Web Application
  54
  55 This serves as a graphical interface for the package. An instance is available
  56 at <https://immune.math.yorku.ca/Rnaught>. It can also be run locally by
  57 invoking the `web()` function.
  58
  59 Datasets can be uploaded as a CSV file, or entered manually. The data is
  60 visualized in the application through plots that show the case counts (either
  61 daily or weekly). If multiple datasets are added, the trends corresponding to
  62 these datasets are populated in the same plot. This plot can be exported as a
  63 PNG image. Furthermore, the datasets entered can be exported as a CSV.
  64
  65 Two sample datasets are included: weekly Canadian COVID-19 case count data from
  66 March 3rd, 2020 to March 31st, 2020, and weekly Ontario COVID-19 case count data
  67 from March 3rd, 2020 to March 31st, 2020.
  68
  69 To estimate the basic reproductive number, the user can choose their preferred
  70 estimator, and if applicable, must enter the known
  71 [serial interval](https://en.wikipedia.org/wiki/Serial_interval) prior to
  72 estimation. If multiple estimates of the basic reproductive number are
  73 calculated, they are all included in a table where each row represents an
  74 estimate. If multiple datasets are being considered, the basic reproduction
  75 number is estimated for all datasets and the columns of the table correspond to
  76 the different datasets. The table also consists of a column corresponding to the
  77 value of the serial interval. This table can also be exported as a CSV.
  78
  79
  80 ## Datasets
  81
  82 The package includes two datasets provided by the [COVID-19 Canada Open Data
  83 Working Group](https://github.com/ccodwg/CovidTimelineCanada).
  84 These report the national and provincial case counts of COVID-19 in Canada. For
  85 details, see `?COVIDCanada` and `?COVIDCanadaPT`. They are also available as
  86 [CSV files](https://github.com/MI2YorkU/Rnaught/tree/master/inst/extdata).