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Co-authored-by: Andree Valle Campos <[email protected]>
following suggestion in epiverse-trace/tutorials#119 (comment)
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This was referenced Apr 19, 2024
Closed
Degoot-AM
requested changes
Apr 22, 2024
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| **Mathematical Modelling** : [Introduction to infectious disease models](https://doi.org/10.1038/s41592-020-0856-2), [state variables](../learners/reference.md#state), [model parameters](../learners/reference.md#parsode), [initial conditions](../learners/reference.md#initial), [ordinary differential equations](../learners/reference.md#ordinary). | ||
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| **Epidemic theory** : [Transmission](https://doi.org/10.1155/2011/267049), [Reproduction number](https://doi.org/10.3201/eid2501.171901). |
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| **Epidemic theory** : [Transmission](https://doi.org/10.1155/2011/267049), [Reproduction number](https://doi.org/10.3201/eid2501.171901). | |
| **Epidemic theory** : [Transmission](https://doi.org/10.1155/2011/267049), [ and reproduction number](https://doi.org/10.3201/eid2501.171901). |
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| Learners should familiarise themselves with following concept dependencies before working through this tutorial: | ||
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| **Mathematical Modelling** : [Introduction to infectious disease models](https://doi.org/10.1038/s41592-020-0856-2), [state variables](../learners/reference.md#state), [model parameters](../learners/reference.md#parsode), [initial conditions](../learners/reference.md#initial), [ordinary differential equations](../learners/reference.md#ordinary). |
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| **Mathematical Modelling** : [Introduction to infectious disease models](https://doi.org/10.1038/s41592-020-0856-2), [state variables](../learners/reference.md#state), [model parameters](../learners/reference.md#parsode), [initial conditions](../learners/reference.md#initial), [ordinary differential equations](../learners/reference.md#ordinary). | |
| **Mathematical Modelling** : [Introduction to infectious disease models](https://doi.org/10.1038/s41592-020-0856-2), [state variables](../learners/reference.md#state), [model parameters](../learners/reference.md#parsode), [initial conditions](../learners/reference.md#initial), [ and ordinary differential equations](../learners/reference.md#ordinary). |
| ## Simulating disease spread | ||
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| To simulate infectious disease trajectories, we must first select a mathematical model to use. | ||
| There is a library of models to choose from in `epidemics`. Models in `epidemics` are prefixed with `model` and suffixed by the name of infection (e.g. Ebola) or a different identifier (e.g. default), and whether the model has a R or [C++](../learners/reference.md#cplusplus) code base. |
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It is good to link the reader with the documentation of {epidemics} for further details.
| library(dplyr) | ||
| library(tidyverse) | ||
| library(DiagrammeR) | ||
| library(webshot) |
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| library(webshot) | |
| library(tracetheme) |
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actually, we use {webshot} to print the Diagrammer. See this to-keep issue epiverse-trace/tutorials#98
| color = intervention_type, | ||
| linetype = intervention_type | ||
| ) + | ||
| stat_summary( |
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| stat_summary( | |
| ggplot2::stat_summary( |
| geom = "line", | ||
| linewidth = 1 | ||
| ) + | ||
| scale_y_continuous( |
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| scale_y_continuous( | |
| ggplot2::scale_y_continuous( |
| scale_y_continuous( | ||
| labels = scales::comma | ||
| ) + | ||
| labs( |
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| labs( | |
| ggplot2::labs( |
episodes/modelling-interventions.Rmd
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| x = "Simulation time (days)", | ||
| y = "Individuals" | ||
| ) + | ||
| theme_bw( |
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| theme_bw( | |
| ggplot2::theme_bw( |
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@avallecam, I enjoyed reading this tutorials and suggest a number of changes to be made. Below are my general remarks:
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avallecam
commented
Apr 22, 2024
episodes/simulating-transmission.Rmd
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| To create our population object, we call the function `population()` specifying a name, the contact matrix, the demography vector and the initial conditions. | ||
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| ```{r population} | ||
| uk_population <- population( | ||
| name = "UK", | ||
| contact_matrix = contact_matrix, | ||
| demography_vector = demography_vector, | ||
| initial_conditions = initial_conditions | ||
| ) | ||
| ``` |
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| To create our population object, we call the function `population()` specifying a name, the contact matrix, the demography vector and the initial conditions. | |
| ```{r population} | |
| uk_population <- population( | |
| name = "UK", | |
| contact_matrix = contact_matrix, | |
| demography_vector = demography_vector, | |
| initial_conditions = initial_conditions | |
| ) | |
| ``` | |
| To create our population object, from the `{epidemics}` package, we call the function `population()`, specifying a name, the contact matrix, the demography vector and the initial conditions. | |
| ```{r population} | |
| uk_population <- epidemics::population( | |
| name = "UK", | |
| contact_matrix = contact_matrix, | |
| demography_vector = demography_vector, | |
| initial_conditions = initial_conditions | |
| ) |
episodes/simulating-transmission.Rmd
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| - `infectiousness_rate` = $\alpha$, | ||
| - `recovery_rate` = $\gamma$, | ||
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| We will simulate a strain of influenza with pandemic potential with $R_0=1.46$, a preinfectious period of 3 days and infectious period of 7 days. Therefore our inputs will be: |
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Which one is clearer? latent period or preinfectious period?
Suggested change
| We will simulate a strain of influenza with pandemic potential with $R_0=1.46$, a preinfectious period of 3 days and infectious period of 7 days. Therefore our inputs will be: | |
| We will simulate a strain of influenza with pandemic potential with $R_0=1.46$, a preinfectious period of 3 days and infectious period of 7 days. | |
| - `preinfectious_period <- 3.0` | |
| - `infectious_period <- 7.0` | |
| - `basic_reproduction <- 1.46` | |
| Therefore our inputs will be: |
episodes/simulating-transmission.Rmd
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| - `transmission_rate = 1.46 / 7.0`, | ||
| - `infectiousness_rate = 1.0 / 3.0`, | ||
| - `recovery_rate = 1.0 / 7.0`. |
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For consisteny with the callout box bellow
Suggested change
| - `transmission_rate = 1.46 / 7.0`, | |
| - `infectiousness_rate = 1.0 / 3.0`, | |
| - `recovery_rate = 1.0 / 7.0`. | |
| - `infectiousness_rate = 1/preinfectious_period`, | |
| - `recovery_rate = 1/infectious_period`, | |
| - `transmission_rate = basic_reproduction*recovery_rate`. |
This was referenced Apr 22, 2024
Co-authored-by: Abdoelnaser M Degoot <[email protected]>
Co-authored-by: Abdoelnaser M Degoot <[email protected]>
avallecam
commented
Apr 25, 2024
episodes/modelling-interventions.Rmd
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| There are two intervention types for `model_default()`. Rate interventions on model parameters (`transmission_rate` $\beta$, `infectiousness_rate` $\sigma$ and `recovery_rate` $\gamma$) and contact matrix reductions `contacts`. | ||
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| To implement both contact and rate interventions in the same simulation they must be passed as a list e.g. `intervention = list(transmission_rate = mask_mandate, contacts = close_schools)`. But if there are multiple interventions that target contact rates, these must be passed as one `contacts` input. See the [vignette on modelling overlapping interventions](https://epiverse-trace.github.io/epidemics/articles/multiple_interventions.html) for more detail. |
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Suggested change
| To implement both contact and rate interventions in the same simulation they must be passed as a list e.g. `intervention = list(transmission_rate = mask_mandate, contacts = close_schools)`. But if there are multiple interventions that target contact rates, these must be passed as one `contacts` input. See the [vignette on modelling overlapping interventions](https://epiverse-trace.github.io/epidemics/articles/multiple_interventions.html) for more detail. | |
| To implement both contact and rate interventions in the same simulation they must be passed as a list e.g. `intervention = list(transmission_rate = mask_mandate, contacts = close_schools)`. But if there are multiple interventions that target contact rates, these must be passed as one `contacts` input. See the [vignette on modelling overlapping interventions](https://epiverse-trace.github.io/epidemics/articles/modelling_multiple_interventions.html) for more detail. |
avallecam
commented
Apr 25, 2024
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closing as redirected to main in #29 |
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