Skip to content

Fix glossary (learners/reference.md) entry #150

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
joshwlambert wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Fix glossary (learners/reference.md) entry #150

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
7 changes: 4 additions & 3 deletions learners/reference.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@
: The contact matrix is a square matrix consisting of rows/columns equal to the number age groups. Each element represents the frequency of contacts between age groups. If we believe that transmission of an infection is driven by contact, and that contact rates are very different for different age groups, then specifying a contact matrix allows us to account for age specific rates of transmission.

[C++]{#cplusplus}
: C++ is a high-level programming language that can be used within R to speed up sections of code. To learn more about C++ check out these [tutorials](https://cplusplus.com/doc/tutorial/) and learn more about the integration of C++ and R [here](https://www.rcpp.org/).

Check warning on line 25 in learners/reference.md

View workflow job for this annotation

GitHub Actions / Build markdown source files if valid 

 [uninformative link text]: [here](https://www.rcpp.org/)
[Censoring]{#censoring}
:
Means that we know an event happened, but we do not know exactly when it happened. Most epidemiological data are “doubly censored” because there is uncertainty surrounding both primary and secondary event times. Not accounting for censoring can lead to biased estimates of the delay’s standard deviation ([Park et al., in progress](https://github.com/parksw3/epidist-paper)).
Expand Down Expand Up @@ -95,17 +95,18 @@


## N

[Non-pharmaceutical interventions]{#NPIs}
: 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).

## O
[Ordinary differential equations]{#ordinary}
: 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.
[Natural history of disease]{#naturalhistory}
: 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))

## O

[Ordinary differential equations]{#ordinary}
: 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.

[Offspring distribution]{#offspringdist}
: 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))

Expand Down
Loading