README.md

In COVID-19-Modelling/BassSIR: Infectious disease modelling with Bass-SIR model

BassSIR

Infectious disease modelling with Bass SIR models

Install package

In R, you can install from github

devtools::install_github("COVID-19-Modelling/BassSIR", upgrade = FALSE)

Load the package before use it

library(BassSIR)

Contents

• Modelling
• Data prepartion
• Model fitting
• Simulation
• Model comparison

• Scenario analysis

• Output and visualisation

• Academic contact

Modelling

Data preparation

cases <- as_bass_data(n_covid19$Hubei, id = "Hubei")
est <- BassSIR::fit(cases, r_rec = 1/22.2, r_death = 1/22.3, type = "BassSIR")
sim <- simulate(est, nsim = 1000)

Model fitting

Fit BassSIR model to data

est_bass <- BassSIR::fit(cases, r_rec = 1/22.2, r_death = 1/22.3, type = "BassSIR")
summary(est_bass)

Fit SIR model to data

est_sir <- BassSIR::fit(cases, r_rec = 1/22.2, r_death = 1/22.3, type = "SIR")
summary(est_sir)

Model comparison

compare_models(BassSIR = est_bass, SIR = est_sir)

Simulation

sim <- simulate(est_bass, nsim = 1000)

Scenario analysis

zero_kappa <- function(pars) {
  pars$kappa <- rep(0, length(pars$kappa))
  return(pars)
}
lockdown <- run_scenario(sim, zero_kappa)

compare_scenarios will output two group of time-series

• Trajectories The trends of simulations of each variable
• Change Calculate the value changes from the baseline case

cp <- compare_scenarios(sim, Lockdown = lockdown, fn_change = "Yt")

Output and visualisation

TBA

Academic contact

Chu-Chang Ku,

Health Economic and Decision Science, University of Sheffield, UK

Email: [e-mail]

License

MIT (c) 2020 Chu-Chang Ku, Ta-Chou Ng, and Hsien-Ho Lin

COVID-19-Modelling/BassSIR documentation built on April 20, 2020, 1:57 a.m.