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daedalus: Optimise economic, social and health trade-offs in a pandemic

daedalus implements the integrated epidemiological and economic model in Haw et al. (2022).

Installation

You can install the development version of daedalus from GitHub using the remotes package.

# install.packages("remotes")
remotes::install_github("j-idea/daedalus", upgrade = FALSE)

Quick start

The model can be run for any country or territory in the country_names list by passing the country name to the function. This automatically pulls country-specific demographic and economic data, which is included in the package, into the model (see the ‘Get started’ vignette for more details).

library(daedalus)

# run model for Canada
data <- daedalus("Canada", "influenza_1918")

# get pandemic costs as a total in million dollars
get_costs(data, "total")
#> [1] 739498.3

# disaggregate total for economic, education, and health costs
get_costs(data, "domain")
#>   economic  education life_years 
#>  18528.338   1235.451 719734.519

Users can select infection parameters from among seven epidemics caused by directly-transmitted viral respiratory pathogens, which are stored in the package as daedalus::infection_data, with epidemic identifiers are stored as daedalus::epidemic_names.

Users can override default country contact data and epidemic-specific infection arguments by passing custom classes to daedalus(); see the package website for more details.

Users can also model the implementation of pandemic response measures: for more on this see the documentation for the main model function daedalus(), and the vignette on modelling interventions on the package website.

daedalus is an R implementation of the scenario model from a project on the economics of pandemic preparedness.

References

Haw, David J., Giovanni Forchini, Patrick Doohan, Paula Christen, Matteo Pianella, Robert Johnson, Sumali Bajaj, et al. 2022. “Optimizing Social and Economic Activity While Containing SARS-CoV-2 Transmission Using DAEDALUS.” Nature Computational Science 2 (4): 223–33. https://doi.org/10.1038/s43588-022-00233-0.