Merge remote-tracking branch 'origin/dev' into sgratzl/webp_generation

Author: sgratzl

content/blog/2021-03-10_c2b.Rmd

@@ -15,31 +15,18 @@ heroImage: blog-lg-c2b.jpg # size: 1120x440 jpg format
 heroImageThumb: blog-thumb-c2b-1.jpg # size: 300x200 jpg format
 # related:
 summary: |
-  In our partnership with the Allegheny County Health Department,
-  we explored a variety of solutions to different pressing challenges
-  during the pandemic. Cases2Beds was one such tool that came as a
-  result of rising COVID cases in Allegheny County.
-
-  The goal of Cases2Beds is to project likely hospital bed
-  utilization from any given set of lab-confirmed cases. The intuition of
-  Cases2Beds is that some fraction ((hospitalization rate) of individuals who
-  test positive today will be hospitalized after a time delay
-  (from test date to admit date) and for a varying duration
-  (from admit date to discharge date). These values and distributions vary by
-  individual traits (primarily age group, and then race or gender).
-  Using the current relevant hospitalization rates, delay distributions,
-  and duration distributions, we show what bed usage might be for the inputted
-  positive cases. We are not aware of other generative approaches for
-  hospital utilization.
-
+  In partnership with the Allegheny County Health Department,
+  we explored solutions to pressing challenges
+  during the pandemic. 
+  
 acknowledgements: |
  Thank you to the Allegheny County Health Department (especially Antony Gnalian,
  Dr. LuAnn Brink, and Dr. Debra Bogen) for their invaluable feedback, efforts, and
  shared interest in actionable intelligence.
 
  Many members of the Delphi Group, including
  Sumit Agrawal, Katie Mazaitis,
- and Phil McGuinness met regularly with the Allegheny County
+ and Phil McGuinness, met regularly with the Allegheny County
  Health Department and investigated various solutions other than Cases2Beds.
  Andrew Chin and Benjamin Smith investigated Cases2Beds alternatives.
 
@@ -126,7 +113,7 @@ ggplot(data=df4, aes(x=Race, y=Rate)) +
 
 We used public data from Allegheny County about the number of people tested, test positivity rate, and hospitalization rate, broken down by the aforementioned demographic factors.
 
-We also acquired information for two critical parameters: offset and length of stay. Offset is the number of days between the day of testing (called specimen collection date) and the first day of hospitalization. For example, if the test date is around the hospital admit date, the offset would be 0 days (or sometimes -1 or +1 days). Otherwise, if the test date was 5 days before hospitalization, the offset would be 5 days. Notably, the offset can be negative, meaning you may have been tested some days or weeks after being admitted (presumably for an unrelated reason).
+We also acquired information for two critical parameters: offset and length of stay. Offset is the number of days between the day of testing (called specimen collection date) and the first day of hospitalization. For example, if the test date is around the hospital admit date, the offset would be 0 days (or sometimes -1 or +1 days). Otherwise, if the test date was 5 days before hospitalization, the offset would be 5 days. Notably, the offset can be negative, meaning a person may have been tested some days or weeks after being admitted (presumably for an unrelated reason).
 
 The length of stay is approximately how many days a person uses a bed in the hospital (± 1 day).
 

content/blog/2021-03-10_c2b.html

@@ -15,31 +15,18 @@
 heroImageThumb: blog-thumb-c2b-1.jpg # size: 300x200 jpg format
 # related:
 summary: |
-  In our partnership with the Allegheny County Health Department,
-  we explored avariety of solutions to different pressing challenges
-  during the pandemic. Cases2Beds was one such tool that came as a
-  result of rising COVID cases in Allegheny County.
-
-  The goal of Cases2Beds is to project likely hospital bed
-  utilization from any given set of lab-confirmed cases. The intuition of
-  Cases2Beds is that some fraction ((hospitalization rate) of individuals who
-  test positive today will be hospitalized after a time delay
-  (from test date to admit date) and for a varying duration
-  (from admit date to discharge date). These values and distributions vary by
-  individual traits (primarily age group, and then race or gender).
-  Using the current relevant hospitalization rates, delay distributions,
-  and duration distributions, we show what bed usage might be for the inputted
-  positive cases. We are not aware of other generative approaches for
-  hospital utilization.
-
+  In partnership with the Allegheny County Health Department,
+  we explored solutions to pressing challenges
+  during the pandemic. 
+  
 acknowledgements: |
  Thank you to the Allegheny County Health Department (especially Antony Gnalian,
  Dr. LuAnn Brink, and Dr. Debra Bogen) for their invaluable feedback, efforts, and
  shared interest in actionable intelligence.
 
  Many members of the Delphi Group, including
  Sumit Agrawal, Katie Mazaitis,
- and Phil McGuinness met regularly with the Allegheny County
+ and Phil McGuinness, met regularly with the Allegheny County
  Health Department and investigated various solutions other than Cases2Beds.
  Andrew Chin and Benjamin Smith investigated Cases2Beds alternatives.
 
@@ -53,7 +40,6 @@
     toc: true
 ---
 
-<script src="/rmarkdown-libs/header-attrs/header-attrs.js"></script>
 
 <div id="TOC">
 <ul>
@@ -127,7 +113,7 @@ <h2>A probabilistic approach</h2>
 <p><img src="/blog/2021-03-10_c2b_files/figure-html/rates-1.svg" width="864" class="wide-figure" />
 <strong>Fig. 2:</strong> Age Group Comparisons based on the Allegheny County COVID-19 Tableau <sup><a href="#ACHDDashboardLink">3</a></sup></p>
 <p>We used public data from Allegheny County about the number of people tested, test positivity rate, and hospitalization rate, broken down by the aforementioned demographic factors.</p>
-<p>We also acquired information for two critical parameters: offset and length of stay. Offset is the number of days between the day of testing (called specimen collection date) and the first day of hospitalization. For example, if the test date is around the hospital admit date, the offset would be 0 days (or sometimes -1 or +1 days). Otherwise, if the test date was 5 days before hospitalization, the offset would be 5 days. Notably, the offset can be negative, meaning you may have been tested some days or weeks after being admitted (presumably for an unrelated reason).</p>
+<p>We also acquired information for two critical parameters: offset and length of stay. Offset is the number of days between the day of testing (called specimen collection date) and the first day of hospitalization. For example, if the test date is around the hospital admit date, the offset would be 0 days (or sometimes -1 or +1 days). Otherwise, if the test date was 5 days before hospitalization, the offset would be 5 days. Notably, the offset can be negative, meaning a person may have been tested some days or weeks after being admitted (presumably for an unrelated reason).</p>
 <p>The length of stay is approximately how many days a person uses a bed in the hospital (± 1 day).</p>
 <p>Given the hospitalization rate, the offset distribution, and the length of stay distribution, we could simulate multiple futures for any given set of positive cases and their testing dates. This process ultimately shows the distribution of the number of beds needed each day.</p>
 <p>Estimating the future given a set of probabilities is a common problem and is possible with an approach called a Monte Carlo simulation.</p>