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config.yaml

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#------------------------------------------------------------
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# Values for this lesson.
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#------------------------------------------------------------
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# Which carpentry is this (swc, dc, lc, or cp)?
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# swc: Software Carpentry
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# dc: Data Carpentry
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# lc: Library Carpentry
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# cp: Carpentries (to use for instructor training for instance)
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# incubator: The Carpentries Incubator
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carpentry: 'epiverse-trace'
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# Overall title for pages.
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title: 'Scenario modelling for outbreak analytics with R'
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# Date the lesson was created (YYYY-MM-DD, this is empty by default)
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created:
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# Comma-separated list of keywords for the lesson
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keywords: 'epidemic models, interventions'
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# Life cycle stage of the lesson
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# possible values: pre-alpha, alpha, beta, stable
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life_cycle: 'pre-alpha'
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# License of the lesson materials (recommended CC-BY 4.0)
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license: 'CC-BY 4.0'
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# Link to the source repository for this lesson
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source: 'https://github.com/epiverse-trace/tutorials-late'
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# Default branch of your lesson
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branch: 'main'
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# Who to contact if there are any issues
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contact: 'andree.valle-campos@lshtm.ac.uk'
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# Navigation ------------------------------------------------
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#
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# Use the following menu items to specify the order of
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# individual pages in each dropdown section. Leave blank to
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# include all pages in the folder.
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#
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# Example -------------
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#
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# episodes:
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# - introduction.md
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# - first-steps.md
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#
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# learners:
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# - setup.md
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#
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# instructors:
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# - instructor-notes.md
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#
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# profiles:
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# - one-learner.md
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# - another-learner.md
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# Order of episodes in your lesson
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episodes:
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- contact-matrices.Rmd
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- simulating-transmission.Rmd
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- model-choices.Rmd
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- modelling-interventions.Rmd
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- compare-interventions.Rmd
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- vaccine-comparisons.Rmd
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- disease-burden.Rmd
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# Information for Learners
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learners:
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# Information for Instructors
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instructors:
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# Learner Profiles
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profiles:
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# Customisation ---------------------------------------------
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#
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# This space below is where custom yaml items (e.g. pinning
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# sandpaper and varnish versions) should live
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varnish: epiverse-trace/varnish@epiversetheme
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sandpaper: epiverse-trace/sandpaper@patch-renv-github-bug

md5sum.txt

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"episodes/disease-burden.Rmd" "9579a4368c9b300a0cc2e02ca170630b" "site/built/disease-burden.md" "2026-03-31"
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"instructors/instructor-notes.md" "ca3834a1b0f9e70c4702aa7a367a6bb5" "site/built/instructor-notes.md" "2026-03-31"
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"learners/BF_measles.Rmd" "5725a25d664730bbce3736a3243c2182" "site/built/BF_measles.md" "2026-03-31"
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"learners/reference.md" "9e836f1ec999f95f55135cdd2c78d2e6" "site/built/reference.md" "2026-03-31"
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"learners/reference.md" "75ccb4b21868339eb1f9f4512b62f35c" "site/built/reference.md" "2026-03-31"
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"learners/setup.md" "ffd5d44669ec7f69d57de7088e6b2773" "site/built/setup.md" "2026-03-31"
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"profiles/learner-profiles.md" "31b503c4b5bd1f0960ada730eca4a25e" "site/built/learner-profiles.md" "2026-03-31"
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"renv/profiles/lesson-requirements/renv.lock" "143d1859c7b2e4c666f55209ffc1a56c" "site/built/renv.lock" "2026-03-31"

reference.md

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## N
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[Non-pharmaceutical interventions]{#NPIs}
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: Non-pharmaceutical interventions (NPIs) are measures put in place to reduce transmission that do not include the administration of drugs or vaccinations. [More information on NPIs](https://www.gov.uk/government/publications/technical-report-on-the-covid-19-pandemic-in-the-uk/chapter-8-non-pharmaceutical-interventions).
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## O
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[Ordinary differential equations]{#ordinary}
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: Ordinary differential equations (ODEs) can be used to represent the rate of change of one variable (e.g. number of infected individuals) with respect to another (e.g. time). Check out this introduction to [ODEs](https://mathinsight.org/ordinary_differential_equation_introduction). ODEs are widely used in infectious disease modelling to model the flow of individuals between different disease states.
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[Natural history of disease]{#naturalhistory}
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: Refers to the development of disease from beginning to end without any treatment or intervention. In fact, given the harmfulness of an epidemic, treatment or intervention measures are inevitable. Therefore, it is difficult for the natural history of a disease to be unaffected by the various coupling factors. ([Xiang et al, 2021](https://www.sciencedirect.com/science/article/pii/S2468042721000038))
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## O
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[Ordinary differential equations]{#ordinary}
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: Ordinary differential equations (ODEs) can be used to represent the rate of change of one variable (e.g. number of infected individuals) with respect to another (e.g. time). Check out this introduction to [ODEs](https://mathinsight.org/ordinary_differential_equation_introduction). ODEs are widely used in infectious disease modelling to model the flow of individuals between different disease states.
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[Offspring distribution]{#offspringdist}
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: Distribution of the number of secondary cases caused by a particular infected individual. ([Lloyd-Smith et al., 2005](https://www.nature.com/articles/nature04153), [Endo et al., 2020](https://wellcomeopenresearch.org/articles/5-67/v3))
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