update methodology vignettes #919

sbfnk · 2025-01-10T09:40:15Z

Description

This PR closes #916.

Initial submission checklist

My PR is based on a package issue and I have explicitly linked it.
I have tested my changes locally (using devtools::test() and devtools::check()).
I have added or updated unit tests where necessary.
I have updated the documentation if required and rebuilt docs if yes (using devtools::document()).
I have followed the established coding standards (and checked using lintr::lint_package()).
I have added a news item linked to this PR.

After the initial Pull Request

I have reviewed Checks for this PR and addressed any issues as far as I am able.

seabbs · 2025-01-10T10:07:51Z

NEWS.md

@@ -33,6 +33,7 @@
 - Brought the docs on `alpha_sd` up to date with the code change from prior PR #853. By @zsusswein in #862 and reviewed by @jamesmbaazam.
 - The `...` argument in `estimate_secondary()` has been removed because it was not used. By @jamesmbaazam in #894 and reviewed by @.
 - All examples now use the natural parameters of distributions rather than the mean and standard deviation when specifying uncertain distributions. This is to eliminate warnings and encourage best practice. By @jamesmbaazam in #893 and reviewed by @sbfnk.
+- Updated the methodology vignettes, By @sbfnk in #919 and reviewed by.


Suggested change

- Updated the methodology vignettes, By @sbfnk in #919 and reviewed by.

- Updated the methodology vignettes, By @sbfnk in #919 and reviewed by @seabbs.

vignettes/estimate_infections.Rmd

jamesmbaazam · 2025-01-10T10:11:33Z

vignettes/estimate_infections.Rmd

 \end{align}

-where $I_{t}$ is the number of latent infections on day $t$, $r$ is the estimate of the initial growth rate, and $I_\mathrm{obs}$ and $r_\mathrm{obs}$ are estimated from the first week of observed data, respectively, as as the point estimates of intercept and slope from fitting a linear regression model to the first 7 days of data (or all data if fewer than 7 days of data are given),
+where $I_{t}$ is the number of latent infections on day $t$, $r$ is the estimate of the initial growth rate, $\xi$ is the proportoin reported (see [Delays and scaling]) and $I_\mathrm{init}$ and $r_\mathrm{init}$ are estimated, respectively, as the point estimates of intercept and slope from fitting a linear regression model to the first 7 days of data (or all data if fewer than 7 days of data are given),


Suggested change

where $I_{t}$ is the number of latent infections on day $t$, $r$ is the estimate of the initial growth rate, $\xi$ is the proportoin reported (see [Delays and scaling]) and $I_\mathrm{init}$ and $r_\mathrm{init}$ are estimated, respectively, as the point estimates of intercept and slope from fitting a linear regression model to the first 7 days of data (or all data if fewer than 7 days of data are given),

where $I_{t}$ is the number of latent infections on day $t$, $r$ is the estimate of the initial growth rate, $\xi$ is the proportion reported (see [Delays and scaling]) and $I_\mathrm{init}$ and $r_\mathrm{init}$ are estimated, respectively, as the point estimates of intercept and slope from fitting a linear regression model to the first 7 days of data (or all data if fewer than 7 days of data are given),

seabbs · 2025-01-10T10:11:55Z

vignettes/estimate_infections.Rmd

 \end{align}

-where $I_{t}$ is the number of latent infections on day $t$, $r$ is the estimate of the initial growth rate, and $I_\mathrm{obs}$ and $r_\mathrm{obs}$ are estimated from the first week of observed data, respectively, as as the point estimates of intercept and slope from fitting a linear regression model to the first 7 days of data (or all data if fewer than 7 days of data are given),
+where $I_{t}$ is the number of latent infections on day $t$, $r$ is the estimate of the initial growth rate, $\xi$ is the proportoin reported (see [Delays and scaling]) and $I_\mathrm{init}$ and $r_\mathrm{init}$ are estimated, respectively, as the point estimates of intercept and slope from fitting a linear regression model to the first 7 days of data (or all data if fewer than 7 days of data are given),


Suggested change

where $I_{t}$ is the number of latent infections on day $t$, $r$ is the estimate of the initial growth rate, $\xi$ is the proportoin reported (see [Delays and scaling]) and $I_\mathrm{init}$ and $r_\mathrm{init}$ are estimated, respectively, as the point estimates of intercept and slope from fitting a linear regression model to the first 7 days of data (or all data if fewer than 7 days of data are given),

where $I_{t}$ is the number of latent infections on day $t$, $r$ is the estimate of the initial growth rate, $\xi$ is the proportion reported (see [Delays and scaling]) and $I_\mathrm{init}$ and $r_\mathrm{init}$ are estimated, respectively, as the point estimates of intercept and slope from fitting a linear regression model to the first 7 days of data (or all data if fewer than 7 days of data are given),

seabbs · 2025-01-10T10:29:37Z

vignettes/estimate_infections.Rmd

-  r &\sim \mathrm{Normal}(r_\mathrm{obs}, 0.2)\\
-  I_{0 < t \leq t_\mathrm{seed}} &= I_0 \exp  \left(r t \right)
+  I_0  &\sim \mathrm{LogNormal}(I_\mathrm{init}, \sqrt{I_\mathrm{init}}) \\
+  r &\sim r_\mathrm{init} + (I_\mathrm{init} - I_0) \\


Suggested change

r &\sim r_\mathrm{init} + (I_\mathrm{init} - I_0) \\

r &\sim r_\mathrm{init} + (I_\mathrm{init} - I_0) \\

looking at this reminds me to ask did you look at this both normalised by the standard deviation and not?

I initially had it divided by the seeding time but that was fairly poorly motivated. I assumed that we'd replace this by the R->r solution anyway so didn't dwell on it too much but if you can think of a more appropriate scaling factor here then this would probably be a good thing to include.

my suggestion was the standard deviation so the scaling is the same regardless of the magnitude of the initial infections. I don't think we expect it to scale with the count magnitude do we?

Perhaps the best thing is to just discard the approach and go straight with #920 (comment) rather than trying to come up with something good here.

seabbs · 2025-01-10T10:32:41Z

vignettes/estimate_infections.Rmd

 \end{equation}

-where $g(\tau|\mu_{g}, \sigma_{g})$ is the distribution of generation times (with discretised gamma or discretised log normal distributions available as options) with mean (or log mean in the case of lognormal distributions) $\mu_g$, standard deviation (or log standard deviation in the case of lognormal distributions) $\sigma_g$ and maximum $g_\mathrm{max}$.
-Generation times can either be specified as coming from a distribution with uncertainty by giving mean and standard deviations of normal priors, weighted by default by the number of observations (although this can be changed by the user) and truncated to be positive where relevant for the given distribution; or they can be specified as the parameters of a fixed distribution, or as fixed values.
+where $g(\tau|\mu_{g}, \sigma_{g})$ is the discretised distribution of generation times with parameters $\theta_g$ and maximum $g_\mathrm{max}$.


Suggested change

where $g(\tau|\mu_{g}, \sigma_{g})$ is the discretised distribution of generation times with parameters $\theta_g$ and maximum $g_\mathrm{max}$.

where $g(\tau | \theta_g)$ is the discretised distribution of generation times with parameters $\theta_g$ and maximum $g_\mathrm{max}$.

sbfnk added 4 commits January 10, 2025 09:21

update methodology vignettes

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Merge branch 'main' into vignettes-methods

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update methodology vignettes #919

update methodology vignettes #919

sbfnk commented Jan 10, 2025

seabbs Jan 10, 2025

jamesmbaazam Jan 10, 2025

seabbs Jan 10, 2025

seabbs Jan 10, 2025

sbfnk Jan 10, 2025

seabbs Jan 10, 2025

sbfnk Jan 10, 2025

seabbs Jan 10, 2025

	- Updated the methodology vignettes, By @sbfnk in #919 and reviewed by.
	- Updated the methodology vignettes, By @sbfnk in #919 and reviewed by @seabbs.

	r &\sim r_\mathrm{init} + (I_\mathrm{init} - I_0) \\
	r &\sim r_\mathrm{init} + (I_\mathrm{init} - I_0) \\

	where $g(\tau\|\mu_{g}, \sigma_{g})$ is the discretised distribution of generation times with parameters $\theta_g$ and maximum $g_\mathrm{max}$.
	where $g(\tau \| \theta_g)$ is the discretised distribution of generation times with parameters $\theta_g$ and maximum $g_\mathrm{max}$.

update methodology vignettes #919

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sbfnk commented Jan 10, 2025

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