Description Usage Arguments Details Value Warning Note Author(s) References Examples

Input script for using the weibull4 module. Weibull4 fit data from daily new cases and deaths of an epidemic peak. It was firstly designed to model data from COVID-19

1 2 |

`x` |
Vector: time data (may be date/time or numeric) |

`y` |
Vector: observed/measure event |

`shape` |
Starting value for Weibull's shape parameters. If it is NA, weibull4.fit will try to calculate it from x and y data. |

`scale` |
Starting value for Weibull's scale parameters. If it is NA, weibull4.fit will try to calculate it from x and y data. |

`loc` |
Starting value for Weibull's location parameters. If it is NA, weibull4.fit will try to calculate it from x and y data. |

`area` |
Starting value for Weibull's area parameters or the area under the PDF curve. If it is NA, weibull4.fit will try to calculate it from x and y data. |

`shape2` |
Starting value for the shape parameters of the second mode of the Weibull's distribution. If it is NA, weibull4.fit will try to calculate it from x and y data. It works only if modes=2. |

`scale2` |
Starting value for the scale parameters of the second mode of the Weibull's distribution. If it is NA, weibull4.fit will try to calculate it from x and y data. It works only if modes=2. |

`loc2` |
Starting value for the location parameters of the second mode of the Weibull's distribution. If it is NA, weibull4.fit will try to calculate it from x and y data. It works only if modes=2. |

`area2` |
Starting value for the area parameters of the second mode of the Weibull's distribution. If it is NA, weibull4.fit will try to calculate it from x and y data. It works only if modes=2. |

`iter` |
Number of iterations to perform Metropolis-MCMC. |

`split` |
The date (x axis) in which the data will be split to be calculated by two distinct distribution. This option was implemented to fit data feom the second wave of infections and deaths for COVID-19. With split set, it is possible to analyze curve pattern with up to 4 waves of infection, since both modes and modes2 are set to 2. In this case, the data will be analyzed as two bimodal Weibull distribution. |

`modes` |
Sets whether data defore the split date (if the split is not NA) may be fit with unimodal or bimodal Weibull distribution. If split is not set, modes is the number of modes of the unique distribution to be used. This option was implemented to fit data feom the second wave of infections and deaths for COVID-19. Use modes=1 for unimodal distribution (single peak) and modes=2 for bimodal distribution (two peaks, with no or small valley between them). |

`modes2` |
Sets whether data after the split date (if split is not NA) may be fit with unimodal or bimodal Weibull distribution. If split is not set, modes2 will not be used. This option was implemented to fit data feom the second wave of infections and deaths for COVID-19. Use modes=1 for unimodal distribution (single peak) and modes=2 for bimodal distribution (two peaks). |

`xmax` |
Forecast date to be calculated after x data. It must be in the same format than x. |

This package was specially built to fit COVID-19 data on the number of daily new cases and deaths in countries. So x must be integer. Alternatively, Date format is allowed

LIST containning:

`Fit data` |
Matrix with x and y fitted data |

`Estimates` |
Matrix containning shape, scale, location, area and SD of the MEtropolis-MCMC in the row 1; and standard deviation for each parameter in the row 2 |

`Markov chains` |
Matrix containning the Markov chains for shape, scale, location, area and SD parameters |

This package is a secondary product of the reffered science paper. Please, note that there is no warrants or professional support on its use

Comments, suggestions and doubts must be sent to vitorhmc@ufba.br

Vitor Hugo Moreau, Ph.D.

MOREAU, V. H. (2021) Using the Weibull distribution to model COVID-19 epidemic data, Model Assisted Statistic Applications, in press.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
## Perform non-linear curve fitting with US' data for daily new deaths of COVID-19, with split date
## on Aug, 15th, bimodal distribution before and unimodal distribution after the split date.
## Examples below are with low number of iterations (1000), because of CRAN rules limitations.
## They will possibily give poor results. For best results set iter=10000.
fit <- weibull4(US_COVID$date, US_COVID$new_deaths, split=as.Date("2020-09-15"),
modes=2, modes2=1,iter=1000)
plot(US_COVID$date, US_COVID$new_deaths, ylab="US' daily new deaths", xlab="Date")
lines(fit[[1]][,1], fit[[1]][,2], col="red")
## Perform non-linear curve fitting with Canada's data for daily new cases of COVID-19 using two
## unimodal Weibull distribution, with split date on Aug, 1st
fit <- weibull4(Canada_COVID$date, Canada_COVID$new_cases, split=as.Date("2020-08-01"),
modes=1, modes2=1, iter=1000)
plot(Canada_COVID$date, Canada_COVID$new_cases, ylab="Canada's daily new cases", xlab="Date")
lines(fit[[1]][,1], fit[[1]][,2], col="red")
## Perform non-linear curve fitting with Brazil's data for daily new deaths of COVID-19 using a
## single bimodal Weibull distribution
fit <- weibull4(Brazil_COVID$date, Brazil_COVID$new_deaths, modes=2, iter=1000)
plot(Brazil_COVID$date, Brazil_COVID$new_deaths, ylab="Brazil's daily new deaths", xlab="Date")
lines(fit[[1]][,1], fit[[1]][,2], col="red")
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.