⚠️ This code is released with no support. Please submit any questions or bugs as issues and we will try to address them as quickly as possible.
⚠️ This model is in active development and so parameter name and behaviours, and output file formats will change without notice.
⚠️ The model is stochastic. Multiple runs with different seeds should be undertaken to see average behaviour.
⚠️ As with any mathematical model, it is easy to misconfigure inputs and therefore get meaningless outputs. Please contact the authors if you intend to publish results using
You can install the development version from GitHub with:
# install.packages("devtools") devtools::install_github("mrc-ide/nimue")
Nimue adds an additional dimension to the squire model, allowing us to track the vaccination status across the modelled population. This allows us to caetgorise people into 4 groups:
Nimue includes the flexibility to model different generic vaccine profiles, distribution and prioritisation approaches:
Running Nimue is very similar to squire, in the most simple case we can specify a country and run a scenario:
run1 <- run(country = "France")
To help us visualise the run we can format the raw nimue model output, in this case selecting just the output for infections:
# Format output, selecting a subset and naming the run. output1 <- format(run1, summaries = "infections", compartments = NULL) %>% mutate(Run = "Simple run") # Plot ggplot(output1, aes(x = t, y = value)) + geom_line(col = "darkblue", size = 1) + ylab("Infections") + xlab("Time") + theme_bw()
We can add in a very simple vaccination scenario, with a highly efficacious (95%), infection blocking vaccine that is distributed very quickly (500,000 individuals per day), and compare the results:
run2 <- run(country = "France", max_vaccine = 500000, vaccine_efficacy_infection = rep(0.95, 17)) output2 <- format(run2, summaries = "infections", compartments = NULL) %>% mutate(Run = "Efficacious vaccine") ggplot(bind_rows(output1, output2), aes(x = t, y = value, col = Run)) + geom_line(size = 1) + ylab("Infections") + xlab("Time") + theme_bw()
To see demonstrations of full vaccine functionality please see the articles on: