Explore a range of infectious disease models in a consistent
framework. The primary aim of `idmodelr`

is to provide a
library of infectious disease models for researchers, students, and
other interested individuals. These models can be used to understand the
underlying dynamics and as a reference point when developing models for
research. `idmodelr`

also provides a range of utilities.
These include: plotting functionality; a simulation wrapper; scenario
analysis tooling; an interactive dashboard; tools for handling
mult-dimensional models; and both model and parameter look up tables.
Unlike other modelling packages such as `pomp`

, `libbi`

and `EpiModel`

,
`idmodelr`

serves primarily as an educational resource. It is
most comparable to `epirecipes`

but provides a more consistent framework, an `R`

based
workflow, and additional utility tooling. After users have explored
model dynamics with `idmodelr`

they may then implement their
model using one of these packages in order to utilise the model fitting
tools they provide. For newer modellers, this package reduces the
barrier to entry by containing multiple infectious disease models,
providing a consistent framework for simulation and visualisation, and
signposting
towards other, more research, focussed resources.

Install the CRAN version:

`install.packages("idmodelr")`

Alternatively install the development version from GitHub:

```
# install.packages("devtools")
::install_github("seabbs/idmodelr") devtools
```

In this quick start guide we are going to be defining, simulating and
plotting a Susceptible-Infected-Recovered deterministic compartmental
model with simple population demographics (births = deaths). The first
step is to load the `idmodelr`

package.

`library(idmodelr)`

The next step is to find the model of interest amongst those
implemented in `idmodelr`

. `model_details`

lists
all of the models implemented in `idmodelr`

and can be search
using `dplyr`

, base R, or other dataframe tools.

```
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
%>%
model_details ::filter(model_family %in% "SIR") %>%
dplyr::kable() knitr
```

model | model_family | time | type | recovered | exposed | treated | susceptible | risk_stratified | non_exponential | simple_demographics | vaccination | disease_example | language | parameters |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

SIR_ode | SIR | continuous | deterministic | no | no | no | no | no | no | no | no | none | R | beta, tau |

SIR_demographics_ode | SIR | continuous | deterministic | no | no | no | no | no | no | yes | no | none | R | beta, tau , mu |

SIR_vaccination_ode | SIR | continuous | deterministic | no | no | no | no | no | no | no | yes | none | R | beta , tau , lambda |

SIR_vaccination_demographics_ode | SIR | continuous | deterministic | no | no | no | no | no | no | yes | yes | none | R | beta , tau , lambda, alpha , mu |

Now look at the model and the model help file
(`?SIR_demographics_ode`

) to get an understanding of how the
model is constructed.

```
SIR_demographics_ode#> function (t, x, params)
#> {
#> S <- x[1]
#> I <- x[2]
#> R <- x[3]
#> with(as.list(params), {
#> N = S + I + R
#> dS = -beta * S * I/N - mu * S + mu * N
#> dI = beta * S * I/N - tau * I - mu * I
#> dR = tau * I - mu * R
#> derivatives <- c(dS, dI, dR)
#> list(derivatives)
#> })
#> }
#> <bytecode: 0x7fb5c4a7da08>
#> <environment: namespace:idmodelr>
```

Check the parameters required by the model using
`required_parameters`

. This returns a table containing all
the parameters that must be defined in order to use the model as well as
descriptive information for each parameter.

```
<- required_parameters("SIR_demographics_ode")
parameters
::kable(parameters) knitr
```

parameter | parameter_family | description | type | risk_stratified | non_exponential |
---|---|---|---|---|---|

beta | transmission | Transmission rate = the transmission probability per contact * the number of contacts each individual has. | rate | no | no |

tau | recovery | Recovery rate. The reciprocal of the time infectious. | rate | no | no |

mu | demographics | The natural mortality rate. The reciprocal of the average lifespan. (for simple demographics this is also the birth rate. | rate | no | no |

Parameterise the model.

```
<- data.frame(
parameters beta = 3, ##Transmission rate = contact rate * transmission probablity
tau = 0.5, ## Rate recovcery = 1 / duration of infection
mu = 1/81 ## Natural birth/death rate = 1 / average lifespan
)
```

Check the initial conditions required by looking at the start of the model function. In most cases this should match up to the model name (i.e S, I and R for an SIR model) but risk stratification etc. will require additional compartments.

```
<- data.frame(
inits S = 999,
I = 1,
R = 0
)
```

Specify the timespan over which to run the model.

`<- seq(0, 50, 0.1) times `

Simulate the model.

```
<- simulate_model(model = SIR_demographics_ode,
traj sim_fn = solve_ode, ##as solving an ode
inits = inits,
params = parameters,
times = times)
traj#> # A tibble: 501 × 4
#> time S I R
#> <dbl> <dbl> <dbl> <dbl>
#> 1 0 999 1 0
#> 2 0.1 999. 1.28 0.0567
#> 3 0.2 998. 1.64 0.129
#> 4 0.3 998. 2.11 0.222
#> 5 0.4 997. 2.70 0.342
#> 6 0.5 996. 3.46 0.494
#> 7 0.6 995. 4.43 0.690
#> 8 0.7 993. 5.67 0.940
#> 9 0.8 991. 7.25 1.26
#> 10 0.9 989. 9.28 1.67
#> # … with 491 more rows
#> # ℹ Use `print(n = ...)` to see more rows
```

Summarise the model.

```
summarise_model(traj) %>%
::kable() knitr
```

Final size: S | Final size: I | Final size: R | Epidemic peak time | Epidemic peak | Epidemic duration |
---|---|---|---|---|---|

136 | 31 | 833 | 3.5 | 533 | Inf |

Plot the model trajectory.

```
plot_model(traj, facet = FALSE)
#> Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
#> "none")` instead.
```

Vary the model parameters, by increasing the mortality rate, and then simulate the updated model.

```
<- parameters
parameters_up "mu"]] <- 1 / 20
parameters_up[[
<- simulate_model(model = SIR_demographics_ode,
traj_up sim_fn = solve_ode,
inits,
parameters_up, times)
```

Plot the original trajectory and the updated trajectory. What has the impact of increasing mortality been?

```
plot_model(traj, traj_up, facet = TRUE)
#> Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
#> "none")` instead.
```

See the package vignettes for more help getting started and some additional ideas for exploring infectious disease model dynamics.

A shiny application has been developed that showcases some of the
functionality of the `idmodelr`

package. This application
allows the parameter spaces of a range of models built into
`idmodelr`

to be explored in an interactive session. It is
designed to be used as a teaching aid when introducing people to the
concepts behind infectious disease models without requiring them to
interact with the underlying code. The code for the dashboard can be
found here. It
can be run locally using the following (*Note: this will install
required packages to your system*),

```
#install.packages("shiny")
::runGitHub("exploreidmodels", "seabbs") shiny
```

Additional models are extremely welcome!

To add models in the same family as those already implemented (i.e `SIR_ode`

)
please follow the implemented coding style closely (alternatively open
an issue
explaining why this style needs updating). Models should be named using
their compartments in capitals followed by lower case model details, and
finally the model type. An example of this is the
`SIR_demographics_ode`

model. For highly complex models only
the most major model details should be listed (aim for less than 40
characters). An entry for `model_details`

is also required
(see `model_details.R`

).
If new parameters have been used then a description must be added to
`parameter_details`

(see `parameter_details.R`

).
Please consider also adding to the testing suite for your new model (or
flagging the lack of tests). Models can either be added via a pull
request or via an issue.

To add a new family of models (i.e stochastic models) please open an
issue outlining
your proposed approach. A new family of models is likely to require at
least its own `solve_`

(equivalent to `solve_ode`

)
function and may also require other package changes. Models implemented
in other languages (i.e C) are also very welcome.

File an issue here if there is any other feature, that you think is missing from the package, or better yet submit a pull request!

Please note that the `idmodelr`

project is released with a
Contributor
Code of Conduct. By contributing to this project, you agree to abide
by its terms.

This packge was developed in a docker container based on the tidyverse docker image. To run the docker image run:

`docker run -d -p 8787:8787 --name idmodelr -e USER=seabbs -e PASSWORD=seabbs seabbs/idmodelr`

The rstudio client can be found on port `:8787`

at your
local machines ip. The default username:password is seabbs:seabbs, set
the user with `-e USER=username`

, and the password with
`- e PASSWORD=newpasswordhere`

. The default is to save the
analysis files into the user directory. Alternatively, access the
development environment via binder.