Additional round of edits from Roni

Author: krivard

content/flu/_index.md

@@ -7,18 +7,18 @@ Delphi systems developed before the COVID-19 pandemic typically target seasonal
 
 # About Flu Forecasting
 
-Since 2013, Delphi has supported the U.S. CDC’s Influenza Division in advancing and growing a scientific community around influenza forecasting. That year, we participated in the CDC’s inaugural “[Predict the Influenza Season Challenge](https://www.cdc.gov/flu/news/predict-flu-challenge.htm).” We’ve been perennial leaders in forecasting accuracy ever since, taking part in further [flu forecasting challenges](https://www.cdc.gov/flu/weekly/flusight/about-flu-forecasting.htm), and in 2019 being designated a [National Center of Excellence for Influenza Forecasting](https://delphi.cmu.edu/about/center-of-excellence/). It is a CDC-funded and CDC-designated center working on advancing influenza forecasting, and enabling and improving the usefulness of forecasts of both seasonal and pandemic influenza.
+Since 2013, Delphi has supported the U.S. CDC’s Influenza Division in advancing and growing a scientific community around influenza forecasting. That year, we participated in the CDC’s inaugural “[Predict the Influenza Season Challenge](https://www.cdc.gov/flu/news/predict-flu-challenge.htm).” We’ve been perennial leaders in forecasting accuracy ever since, taking part in further [flu forecasting challenges](https://www.cdc.gov/flu/weekly/flusight/about-flu-forecasting.htm). In 2019 we were designated a [National Center of Excellence for Influenza Forecasting](https://delphi.cmu.edu/about/center-of-excellence/), which is a CDC-funded and CDC-designated center working on advancing influenza forecasting and enabling and improving the usefulness of forecasts of both seasonal and pandemic influenza.
 
-## Flu Tools
+## Resources and Tools
 
 ### [Epidata](https://cmu-delphi.github.io/delphi-epidata/)
-The Epidata API, originally released in 2016, provides real-time access to epidemiological surveillance data. Epidata’s influenza and dengue signals served as the precursor for our COVIDCast system. In this system, we compile data from unique sources and extract flu-related signals, among others.
+The Epidata API, originally released in 2016, provides real-time access to epidemiological surveillance signals, as well as to historical versions of each signal (“what was known when”). In this system, we compile data from multiple sources and extract signals related to flu, dengue, and other pathogens. Epidata’s influenza and dengue signals served as the precursor for our COVIDCast system.
 
 ### [EpiVis](https://delphi.cmu.edu/epivis/)
-EpiVis is an interactive tool for visualizing epidemiological time-series data. Users may explore their own data or utilize existing time series from the numerous data sources provided by the EpiData API.
+EpiVis is an interactive tool for visualizing epidemiological time-series data. Users may explore their own data or utilize existing time series from the numerous data sources provided by the Epidata API.
 
 ### [ILI Nearby](https://delphi.cmu.edu/nowcast/)
-ILI Nearby is a flu nowcasting system which utilizes sensor fusion methodology to bring together multiple signals to nowcast (estimate in real-time) the prevalence of Influenza-Like-Illness (ILI) in various geographic areas of the US. **Note:** ILI Nearby has not been actively maintained since 2019 and should no longer be relied upon for accurate nowcasting.
+In continuous operation since 2016, ILI Nearby is a real-time flu tracking system which utilizes sensor fusion methodology to bring together multiple signals to nowcast (estimate in real-time) the prevalence of Influenza-Like-Illness ("%ILI") in each U.S. state and broader regions of the US. **Note:** Since early 2020, %ILI lost much of its meaning for flu surveillance because of its overlap with Covid-Like-Illness (CLI) and because of drastic changes in healthcare practices and healthcare-seeking behavior. Consequently, ILI Nearby has not been actively maintained since 2019 and should no longer be relied upon for accurate nowcasting of %ILI in the post-pandemic era.
 
 ## Research Articles
 - [Recalibrating Probabilistic Forecasts of Epidemics](https://arxiv.org/abs/2112.06305) by Rumack et al.
@@ -31,7 +31,7 @@ ILI Nearby is a flu nowcasting system which utilizes sensor fusion methodology t
 
 # About Other Pathogens
 
-Delphi has also worked on modeling for other pathogens, namely Dengue fever, Norovirus, and Chikungunya. More information about our data on those pathogens is available in our [Epidata API](https://cmu-delphi.github.io/delphi-epidata/api/README.html).
+Delphi has also worked on other pathogens, including Dengue fever, Norovirus, and Chikungunya. More information about our data on those pathogens is available in our [Epidata API](https://cmu-delphi.github.io/delphi-epidata/api/README.html).
 
 ## Research Articles
 - [An Open Challenge to Advance Probabilistic Forecasting for Dengue Epidemics](https://www.pnas.org/doi/full/10.1073/pnas.1909865116) by Johansson et al.