Added first Flash blog post

content/blog/2024-01-01-flash-intro.Rmd

@@ -0,0 +1,41 @@
+---
+title: Introducing the FlaSH Group
+author: Ananya Joshi, Nolan Gormley, Richa Gadgil, Tina Townes
+date: 2024-01-01
+tags:
+  - flash
+authors:
+  - ajoshi
+  - nolan
+  - richa
+  - tina
+heroImage: flash_long.png
+heroImageThumb: flash_logo.png
+summary: | 
+  Delphi's FlaSH group works to alert experts about data that suggests quality issues or changes in disease dynamics from the millions of data points they publish daily to improve the efficacy of Delphi's data.
+output:
+  blogdown::html_page:
+    toc: true
+---
+
+Delphi publishes millions of public-health-related data points per day, including the total number of daily influenza cases, hospitalizations, and deaths per county and state in the United States (US). This data helps public health practitioners, data professionals, and members of the public make important, informed decisions relating to health and well-being. 
+
+Yet, as data volumes continue to grow quickly (Delphi's data volume expanded 1000x in just 3 years), it is infeasible for data reviewers to inspect every one of these data points for subtle changes in  
+
+ * quality (like those resulting from data delays) or
+ * disease dynamics (like an outbreak).
+
+These issues, if undetected, can have critical downstream ramifications for data users (as shown by the example in Fig 1). 
+
+![Fig 1. Data quality changes in case counts, shown by the large spikes in March and July 2022, when cases were trending down, resulted in similar spikes for predicted counts (red) from multiple forecasts that were then sent to the US CDC. A weekly forecast per state, for cases, hospitalizations, and deaths, up to 4 weeks in the future means that modeling teams would have to review 600 forecasts per week and may not have been able to catch the upstream data issue.](/blog/2024-01-01-flash-intro/forecast.jpg)
+
+
+We care about finding data issues like these so that we can alert downstream data users accordingly. That is why our goal in the FlaSH team (Flagging Anomalies in Streams related to public Health) is to quickly identify data points that warrant human inspection and create tools to support data review. Towards this goal, our team of researchers, engineers, and data reviewers iterate on our deployed interdisciplinary approach. In this blog series, we will cover the different methods and perspectives of the FlaSH project.
+
+Members: Ananya Joshi, Nolan Gormley, Richa Gadgil, Tina Townes   \  
+
+Former Members: Luke Neurieter, Katie Mazaitis  \   
+
+Advisors: Peter Jhon, Roni Rosenfeld, Bryan Wilder
+
+

content/blog/2024-01-01-flash-intro.html

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+---
+title: Introducing the FlaSH Group
+author: Ananya Joshi, Nolan Gormley, Richa Gadgil, Tina Townes
+date: 2024-01-01
+tags:
+  - flash
+authors:
+  - ajoshi
+  - nolan
+  - richa
+  - tina
+heroImage: flash_long.png
+heroImageThumb: flash_logo.png
+summary: | 
+  Delphi's FlaSH group works to alert experts about data that suggests quality issues or changes in disease dynamics from the millions of data points they publish daily to improve the efficacy of Delphi's data.
+output:
+  blogdown::html_page:
+    toc: true
+---
+
+
+
+<p>Delphi publishes millions of public-health-related data points per day, including the total number of daily influenza cases, hospitalizations, and deaths per county and state in the United States (US). This data helps public health practitioners, data professionals, and members of the public make important, informed decisions relating to health and well-being.</p>
+<p>Yet, as data volumes continue to grow quickly (Delphi’s data volume expanded 1000x in just 3 years), it is infeasible for data reviewers to inspect every one of these data points for subtle changes in</p>
+<ul>
+<li>quality (like those resulting from data delays) or</li>
+<li>disease dynamics (like an outbreak).</li>
+</ul>
+<p>These issues, if undetected, can have critical downstream ramifications on data users (as shown by the example in Fig 1).</p>
+<div class="float">
+<img src="/blog/2024-01-01-flash-intro/forecast.jpg" alt="Fig 1. Data quality changes in case counts, shown by the large spikes in March and July 2022, when cases were trending down, resulted in similar spikes for predicted counts (red) from multiple forecasts that were then sent to the US CDC. A weekly forecast per state, for cases, hospitalizations, and deaths, up to 4 weeks in the future means that modeling teams would have to review 600 forecasts per week and may not have been able to catch the upstream data issue." />
+<div class="figcaption">Fig 1. Data quality changes in case counts, shown by the large spikes in March and July 2022, when cases were trending down, resulted in similar spikes for predicted counts (red) from multiple forecasts that were then sent to the US CDC. A weekly forecast per state, for cases, hospitalizations, and deaths, up to 4 weeks in the future means that modeling teams would have to review 600 forecasts per week and may not have been able to catch the upstream data issue.</div>
+</div>
+<p>We care about finding data issues like these so that we can alert downstream data users accordingly. That is why our goal in the FlaSH team (Flagging Anomalies in Streams related to public Health) is to quickly identify data points that warrant human inspection and create tools to support data review. Towards this goal, our team of researchers, engineers, and data reviewers iterate on our deployed interdisciplinary approach. In this blog series, we will cover the different methods and perspectives of the FlaSH project.</p>
+<p>Members: Ananya Joshi, Nolan Gormley, Richa Gadgil, Tina Townes  </p>
+<p>Former Members: Luke Neurieter, Katie Mazaitis  </p>
+<p>Advisors: Peter Jhon, Roni Rosenfeld, Bryan Wilder</p>

content/people/index.md

@@ -216,10 +216,12 @@ people:
   affiliation: Girls of Steel Robotics
   team:
   - contributors
-- firstName: Richa
+- key: richa
+  firstName: Richa
   lastName: Gadgil
   image: richa.png
   affiliation: CMU/MLD
+  description: is a masters student in the Machine Learning Department.
   team:
   - core
 - key: agarcia
@@ -871,10 +873,12 @@ people:
   - center-of-excellence
   description: is a Principal Investigator in the Delphi group, and a Professor in the Department of Statistics & Data Science and the Machine Learning Department at CMU. He is also an Amazon Scholar.
   leaderOrder: 1
-- firstName: Tina
+- key: tina
+  firstName: Tina
   lastName: Townes
   image: tina.png
   affiliation: CMU
+  description: is a member of the Delphi group specializing in data quality monitoring and response.
   team:
   - core
 - firstName: Will