Update README.md

README.md

@@ -5,11 +5,11 @@ Simon L. Grimm, Jeff T. Kaufman, Daniel Rice, Michael M. McLaren, Charlie Whitta
 Detecting novel pathogens at an early stage requires robust early warning that is both sensitive and pathogen-agnostic. Wastewater metagenomic sequencing (W-MGS) could enable highly pathogen-agnostic disease monitoring, but its sensitivity and financial feasibility are dependent on the relative abundance of novel pathogen sequences in W-MGS data. Here we collate W-MGS data from a diverse range of studies to characterize the relative abundance of known viruses in wastewater samples. We develop a Bayesian statistical model to integrate a subset of these data with epidemiological estimates of incidence and prevalence, and use it to estimate the expected relative abundance of different viral pathogens for a given prevalence or incidence in the community. Our results reveal pronounced variation between sites and studies, with estimates differing by one to three orders of magnitude for the same pathogen. For example, the expected relative abundance of SARS-CoV-2 at weekly incidence of 1% of the population varied between 10-7 and 10-10. Integrating these estimates with a simple cost model highlights substantial variation in the volume of W-MGS required to detect these pathogens. The mean sequencing cost of identifying 100 reads from a new SARS-CoV-2-like pathogen at 1% cumulative incidence was $2,579,000. A Norovirus-like pathogen which sheds more would require sequencing costs of $18,000. This model, and its parameter estimates, represent an important resource for future investigation into the performance of wastewater MGS, and can be extended to incorporate new wastewater datasets as they become available.
 
 ### Repository structure
-This work is split across two repositories: in this repo we are
-collecting prevalence estimates and building the model, while determining
-relative abundances from existing data is in the
+This work is split across two repositories: This repo contains scripts to collect incidence and prevalence estimates, and to build the model, while determining relative abundances from existing data is in the [INSERT FINAL MGS_PIPELINE REPO}
 [mgs-pipeline](https://github.com/naobservatory/mgs-pipeline) repo.
 
+Scripts to create the manuscript figures are found in the `figures/` directory. Scripts to generate epidemiological estimates are in the `pathogens/` directory.
+
 ### Working with prevalence data
 
 In python, run `import pathogens` and then iterate over `pathogens.pathogens`.
@@ -30,46 +30,20 @@ To fit the model, run `./fit.py`. This will create:
 * `fig/`, a directory containing a large number plots of posterior distributions and samples from posterior predictive distributions
   (see [model.md](model.md) for details)
 
-Once the model has been fit, run `./plot_summaries.py` to create plots of the posterior distribution of $RA(1\perthousand)$ for the write-up:
+Once the model has been fit, run `./plot_summaries.py` to create plots of the posterior distribution of $RA(1\perhundred)$.
 
 * `fig/incidence-violin.{pdf,png}`, posteriors for all incidence viruses
 * `fig/prevalence-violin.{pdf,png}`, posteriors for all prevalence viruses
 * `fig/by_location_incidence-violin.{pdf,png}`, posteriors separated by location for the most common incidence viruses
+* `fig/by_location_prevalence-violin.{pdf,png}`, posteriors separated by location for a subset of prevalence viruses
 
 ### Development
 
-#### Making changes
-
-Create a branch named yourname-purpose and push your changes to it.  Then
-create a pull request.  Use the "request review" feature to ask for a review:
-all PRs need to be reviewed by someone else, and for now include Jeff and Simon
-on all PRs unless they're OOO.
-
-Once your change has been approved by your reviewers and passes
-[presubmit](#presubmit), you can merge it.  Don't merge someone else's PR
-without confirming with them: they may have other changes they've realized they
-needed to make, or a tricky branch structure that needs to be resolved in a
-particular order.
-
-Handle incoming reviews at least twice a day (morning and afternoon) -- slow
-reviews add a lot of friction.  As a PR author you can avoid this friction by
-creating another branch that diverges from the code you have under review; ask
-Jeff to show you how if you're interested.  Configure [notification
-routing](https://github.com/settings/notifications/custom_routing) on github so
-that work-related notifications go to your work account.
-
 #### Testing
 
-Run `./test.py`
-
-#### Presubmit
-
-Before creating a PR or submitting code, run `./check.sh`.  It will run tests
-than check your types and formatting.  This also runs automatically on GitHub
-when you make PRs, but it's much faster to catch problems locally first.
+Before creating a PR or submitting code, run `./check.sh`.  It will run tests that check your formatting.  This also runs automatically on GitHub when making pull requests, but it's much faster to catch problems locally first.
 
-If `./check.sh` complains about formatting or import sorting, you can fix this
-automatically with `./check.sh --fix`.
+If `./check.sh` complains about formatting or import sorting, you can fix this automatically with `./check.sh --fix`.
 
 #### Installing pystan
 
@@ -81,4 +55,4 @@ However, on some non-Linux systems (including M2 Macbooks), one of `pystan`'s de
 To get around this problem, you can [install httpstan from source](https://httpstan.readthedocs.io/en/latest/installation.html#installation-from-source).
 Once it is built and installed, you can then install the requirements file as above.
 (Note that you can clone the `httpstan` repo anywhere on your computer.
-I recommend doing it outside of the `p2ra` repo directory to that git doesn't try to track it.)
+We recommend doing this outside of the `p2ra` repo directory so that git doesn't try to track it.)