cmu-delphi · melange396 · Aug 14, 2024 · May 2, 2024 · May 2, 2024 · May 2, 2024
@@ -1,6 +1,12 @@
 ---
 title: Research
 papers:
+  - title: "Outlier Ranking for Large-Scale Public Health Data"
+    image: ranking.png
+    authors: Joshi, Townes T., Gormley, Neureiter, Wilder, Rosenfeld
+    link: https://ojs.aaai.org/index.php/AAAI/article/view/30222
+    year: 2024
+    journal: Association for the Advancement of Artificial Intelligence
   - title: "Smooth Multi-Period Forecasting with Application to Prediction of COVID-19 Cases"
     image: smoothing-paper-teaser.jpg
     authors: Tuzhilina, Hastie, McDonald, Tay, Tibshirani

@@ -13,7 +13,7 @@ output:
     toc: true
 acknowledgements: Thank you to George Haff, Carlyn Van Dyke, and Ron Lunde for editing this blog post. 
 ---
-Insights from public health data can keep communities safe. However, identifying these insights in large volumes of modern public health data can be laborious^[Rosen, George. A history of public health. JHU Press, 2015.]. As a result, over the past few decades, public health agencies have built monitoring systems, like [ESSENCE](https://www.cdc.gov/nssp/new-users.html) (CDC), [EIOS](https://www.who.int/initiatives/eios) (WHO), and [DHIS2](https://dhis2.org/) (WHO), where users can set custom statistical alerts and then investigate these alerts using data visualizations^[Chen, Hsinchun, Daniel Zeng, and Ping Yan. Infectious disease informatics: syndromic surveillance for public health and biodefense. Vol. 21. New York: Springer, 2010.]. These alerting systems largely follow the following formula^[Murphy, Sean Patrick, and Howard Burkom. "Recombinant temporal aberration detection algorithms for enhanced biosurveillance." Journal of the American Medical Informatics Association 15.1 (2008): 77-86.] as shown in Fig 1.:
+Insights from public health data can keep communities safe. However, identifying these insights in large volumes of modern public health data can be laborious^[Rosen, George. A history of public health. JHU Press, 2015.]. As a result, over the past few decades, public health agencies have built monitoring systems, like [ESSENCE](https://www.cdc.gov/nssp/new-users.html) (CDC) and [DHIS2](https://dhis2.org/) (WHO), where users can set custom statistical alerts and then investigate these alerts using data visualizations^[Chen, Hsinchun, Daniel Zeng, and Ping Yan. Infectious disease informatics: syndromic surveillance for public health and biodefense. Vol. 21. New York: Springer, 2010.]. These alerting systems largely follow the following formula^[Murphy, Sean Patrick, and Howard Burkom. "Recombinant temporal aberration detection algorithms for enhanced biosurveillance." Journal of the American Medical Informatics Association 15.1 (2008): 77-86.] as shown in Fig 1.:
 
 
 <center>

@@ -21,7 +21,7 @@
 </ul>
 </div>
 
-<p>Insights from public health data can keep communities safe. However, identifying these insights in large volumes of modern public health data can be laborious<a href="#fn1" class="footnote-ref" id="fnref1"><sup>1</sup></a>. As a result, over the past few decades, public health agencies have built monitoring systems, like <a href="https://www.cdc.gov/nssp/new-users.html">ESSENCE</a> (CDC), <a href="https://www.who.int/initiatives/eios">EIOS</a> (WHO), and <a href="https://dhis2.org/">DHIS2</a> (WHO), where users can set custom statistical alerts and then investigate these alerts using data visualizations<a href="#fn2" class="footnote-ref" id="fnref2"><sup>2</sup></a>. These alerting systems largely follow the following formula<a href="#fn3" class="footnote-ref" id="fnref3"><sup>3</sup></a> as shown in Fig 1.:</p>
+<p>Insights from public health data can keep communities safe. However, identifying these insights in large volumes of modern public health data can be laborious<a href="#fn1" class="footnote-ref" id="fnref1"><sup>1</sup></a>. As a result, over the past few decades, public health agencies have built monitoring systems, like <a href="https://www.cdc.gov/nssp/new-users.html">ESSENCE</a> (CDC) and <a href="https://dhis2.org/">DHIS2</a> (WHO), where users can set custom statistical alerts and then investigate these alerts using data visualizations<a href="#fn2" class="footnote-ref" id="fnref2"><sup>2</sup></a>. These alerting systems largely follow the following formula<a href="#fn3" class="footnote-ref" id="fnref3"><sup>3</sup></a> as shown in Fig 1.:</p>
 <center>
 <div class="float">
 <img src="/blog/2024-01-30-flash-framework/image3.png" alt="Fig 1 Standard Approach for Alerting Systems" />

diff --git a/content/blog/2024-05-02-flash-expert.Rmd b/content/blog/2024-05-02-flash-expert.Rmd
@@ -0,0 +1,32 @@
+---
+title: "A User's Perspective on the Updated FlaSH System"
+author: Tina Townes
+date: 2024-05-02
+tags:
+  - flash
+authors:
+  - tina
+heroImage: blog-thumb-flash-expert.png
+heroImageThumb: blog-lg-flash-expert.png
+summary: | 
+  A reflection of the recent changes to the FlaSH user experience by our quality assurance expert, Tina Townes. 
+output:
+  blogdown::html_page:
+    toc: true
+---
+
+<center>
+![**Fig 1a.** Revised FlaSH Dashoard](/blog/2024-05-02-flash-expert/new_dash.png)</center>
+
+
+In its initial stages, the FlaSH dashboard (Fig 1b) only enabled me to assess potential anomalies by viewing graphs, line-by-line, as generated by the FlaSH program. There wasn’t an efficient way to filter various incoming anomalies when I needed to examine specific geographic areas or signals. Nor was there an easy way to see a daily overview map of the aggregated average FlaSH scores for nationwide anomalies. Without the current dashboard, I was spending a good amount of time scrolling, manually sorting, documenting, and searching for specific anomaly reports I wanted to examine rather than focusing solely on identifying, marking, and analyzing anomalies.
+
+<center>![**Fig 1b.** Prior FlaSH Dashoard](/blog/2024-05-02-flash-expert/old_dash.png)</center>
+
+Now, in its current iteration (Fig 1a), the FlaSH dashboard lets me easily filter daily anomaly results by various variables including geos and signal types, and also view a national map offering a quick glimpse of locations of high FlaSH scores. Furthermore, the updated FlasH dashboard now enables me to take detailed notes on particularly interesting anomalies, trends and other issues of importance, and maintain these notes in an organized, searchable fashion within the platform. 
+
+These new dashboard features allow me to devote more of my time and efforts to assessing anomalies of interest and focus on geographies with high concentrations of problematic data or noteworthy trends.
+
+Finally, now with the dashboard's repositioned filtering menu, the page layout becomes an even more familiar environment. The menu echoes the user-friendly layouts of popular retail and informational sites, making navigation much smore intuitive and smoother, thus allowing me to work through various options more quickly. 
+
+* Fig 1a. From a paper by Joshi, Gormley, Gadgil, Townes, Rosenfeld and Wilder
diff --git a/content/blog/2024-05-02-flash-expert.html b/content/blog/2024-05-02-flash-expert.html
@@ -0,0 +1,38 @@
+---
+title: "A User's Perspective on the Updated FlaSH System"
+author: Tina Townes
+date: 2024-05-02
+tags:
+  - flash
+authors:
+  - tina
+heroImage: blog-thumb-flash-expert.png
+heroImageThumb: blog-lg-flash-expert.png
+summary: | 
+  A reflection of the recent changes to the FlaSH user experience by our quality assurance expert, Tina Townes. 
+output:
+  blogdown::html_page:
+    toc: true
+---
+
+
+
+<center>
+<div class="float">
+<img src="/blog/2024-05-02-flash-expert/new_dash.png" alt="Fig 1a. Revised FlaSH Dashoard" />
+<div class="figcaption"><strong>Fig 1a.</strong> Revised FlaSH Dashoard</div>
+</div>
+</center>
+<p>In its initial stages, the FlaSH dashboard (Fig 1b) only enabled me to assess potential anomalies by viewing graphs, line-by-line, as generated by the FlaSH program. There wasn’t an efficient way to filter various incoming anomalies when I needed to examine specific geographic areas or signals. Nor was there an easy way to see a daily overview map of the aggregated average FlaSH scores for nationwide anomalies. Without the current dashboard, I was spending a good amount of time scrolling, manually sorting, documenting, and searching for specific anomaly reports I wanted to examine rather than focusing solely on identifying, marking, and analyzing anomalies.</p>
+<center>
+<div class="float">
+<img src="/blog/2024-05-02-flash-expert/old_dash.png" alt="Fig 1b. Prior FlaSH Dashoard" />
+<div class="figcaption"><strong>Fig 1b.</strong> Prior FlaSH Dashoard</div>
+</div>
+</center>
+<p>Now, in its current iteration (Fig 1a), the FlaSH dashboard lets me easily filter daily anomaly results by various variables including geos and signal types, and also view a national map offering a quick glimpse of locations of high FlaSH scores. Furthermore, the updated FlasH dashboard now enables me to take detailed notes on particularly interesting anomalies, trends and other issues of importance, and maintain these notes in an organized, searchable fashion within the platform.</p>
+<p>These new dashboard features allow me to devote more of my time and efforts to assessing anomalies of interest and focus on geographies with high concentrations of problematic data or noteworthy trends.</p>
+<p>Finally, now with the dashboard’s repositioned filtering menu, the page layout becomes an even more familiar environment. The menu echoes the user-friendly layouts of popular retail and informational sites, making navigation much smore intuitive and smoother, thus allowing me to work through various options more quickly.</p>
+<ul>
+<li>Fig 1a. From a paper by Joshi, Gormley, Gadgil, Townes, Rosenfeld and Wilder</li>
+</ul>
diff --git a/content/blog/images/blog-lg-flash-expert.png b/content/blog/images/blog-lg-flash-expert.png
diff --git a/content/blog/images/blog-thumb-flash-expert.png b/content/blog/images/blog-thumb-flash-expert.png
diff --git a/static/blog/2024-05-02-flash-expert/new_dash.png b/static/blog/2024-05-02-flash-expert/new_dash.png
diff --git a/static/blog/2024-05-02-flash-expert/old_dash.png b/static/blog/2024-05-02-flash-expert/old_dash.png