Changelog located in NEWS.md
Carnegie Mellon University’s Delphi Research Group derives it’s “Combined” indicator from multiple data sources that include:
The higher the combined indicator value, the greater the signaling of higher COVID-19 prevalence in that metropolitan area (MSA). The value has no upper bound, but for reference, here are some of the combined indicator values from some of the hotspots around the country as of July 3rd, 2020.
Only metropolitan areas in Indiana with sufficient data have combined indicator values calculated, so not all areas are shown in the map. More details about the methodology behind this project can be found on their website.
The table shows the current COVID-19 cases per 100,000 population (Cases per 100K) and positivity rates (Positive Test Rate) for each MSA. The trend columns show historic values of each metric with the maximum value labelled.
The Cases per 100K value is the current, scaled weekly average of COVID-19 cases. By scaling (per 100K population) this value, we can make valid comparisons between MSAs. The trend column for this value is color coded based on the value of Cases per 100K column. Taken from the Harvard Global Health Institute, there are four possible colors to which are assigned four statuses: “Tipping Point”, “Accelerated Spread”, “Community Spread”, or “On Track for Containment”.
Positivity rates are the number of positive test results divided by the total number of tests administered for that week. While Cases per 100K is a rolling average, each Positive Test Rate value is calculated in weekly steps. For example, the week 28 value is calculated from 7/05/2020 to 7/11/2020 , and the week 29 value is calculated from 7/12/2020 to 7/18/2020. The diagonal-lined band in the line chart shows a 0% to 5% target range. Kentucky and Ohio do not have testing data that is publicly available, so I couldn’t calculate positivity rates for Evansville, Cincinnati, and Louisville.
The grouped line chart shows historic combined indicator values for each MSA.
The dumbbell chart shows the combined indicator value (gray) and the 95% confidence interval.
The map shows the metropolitan statistical areas that have calculated combined indicator values.
The table shows a scaled, weekly average of COVID-19 cases and a weekly positivity rate for MSAs that include states that have made their testing data available.
Both values can be used to gain a better understanding of the current situation in each MSA. For example, a moderate Cases per 100K value and a relatively higher Positive Test Rate could be interpreted as an area with substantial viral transmission but not enough testing.
The data date for Cases per 100K and Positive Test Rate displays by hovering over column names or over last value of trend column.
---
title: "Carnegie Mellon's COVIDcast for Indiana"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: scroll
social: menu
source_code: embed
theme: yeti
css: covidcast-style.css
favicon: images/ind-state-fav.png
logo: images/rsz_ind-state-logo.png
navbar:
- {icon: "fa-arrow-alt-circle-left", href: "https://ercbk.github.io/Indiana-COVID-19-Website/static.html", align: right, title: "Back to Tracker"}
---
```{r setup-data}
pacman::p_load(extrafont, dplyr, glue, leaflet, leaflet.extras, plotly, crosstalk, htmltools, reactable, dataui)
# clean combined indicator datasets for line, leaflet, and dumbbell charts
ci_clean_line <- readr::read_rds(glue("{rprojroot::find_rstudio_root_file()}/data/dash-ci-line.rds"))
ci_clean_leaf <- readr::read_rds(glue("{rprojroot::find_rstudio_root_file()}/data/dash-ci-leaf.rds"))
ci_clean_db <- readr::read_rds(glue("{rprojroot::find_rstudio_root_file()}/data/dash-ci-db.rds"))
# reactable/sparkline data
react_dat <- readr::read_rds(glue("{rprojroot::find_rstudio_root_file()}/data/dash-case-pos.rds"))
react_dates <- readr::read_rds(glue("{rprojroot::find_rstudio_root_file()}/data/dash-case-pos-dates.rds"))
```
```{r shared}
# {crosstalk} functions so charts can react to selections
ci_shared_line <- SharedData$new(ci_clean_line,
~name,
group = "covidcast")
ci_shared_leaf <- SharedData$new(ci_clean_leaf,
~name,
group = "covidcast")
ci_shared_db <- SharedData$new(ci_clean_db,
~name,
group = "covidcast")
react_shared <- SharedData$new(react_dat,
~msa,
group = "covidcast")
```
About {data-icon="fa-question-circle"}
=====================================
Changelog located in [NEWS.md](https://github.com/ercbk/Indiana-COVID-19-Tracker/blob/master/NEWS.md)
Description
Carnegie Mellon University's Delphi Research Group derives it's **"Combined" indicator** from multiple data sources that include:
- Doctor Visits - Percentage of doctor's visits that are due to COVID-like symptoms
- Symptoms (Facebook) - Random Facebook users are directed to a survey that asks for the number of household members with COVID-like symptoms.
- Symptoms in Community (Facebook) - Same as for "Symptoms" but asks about people they know outside of their household.
- Search Trends (Google) - Google searchs for COVID-related topics relative to an area's population
The higher the combined indicator value, the greater the signaling of higher COVID-19 prevalence in that [metropolitan area (MSA)](https://www.census.gov/programs-surveys/metro-micro/about.html). The value has no upper bound, but for reference, here are some of the combined indicator values from some of the hotspots around the country as of July 3rd, 2020.
- San Antonio, Texas: 2.69
- Phoenix, Arizona: 1.92
- Naples, Florida: 2.76
Only metropolitan areas in Indiana with sufficient data have combined indicator values calculated, so not all areas are shown in the map. More details about the methodology behind this project can be found on their [website](https://covidcast.cmu.edu/index.html?sensor=doctor-visits-smoothed_adj_cli&level=county®ion=42003&date=20200701&signalType=value).
The table shows the current **COVID-19 cases per 100,000 population** (Cases per 100K) and **positivity rates** (Positive Test Rate) for each MSA. The trend columns show historic values of each metric with the maximum value labelled.
The Cases per 100K value is the current, scaled weekly average of COVID-19 cases. By scaling (per 100K population) this value, we can make valid comparisons between MSAs. The trend column for this value is color coded based on the value of Cases per 100K column. Taken from the [Harvard Global Health Institute](https://globalepidemics.org/key-metrics-for-covid-suppression/), there are four possible colors to which are assigned four statuses: "Tipping Point", "Accelerated Spread", "Community Spread", or "On Track for Containment".
Positivity rates are the number of positive test results divided by the total number of tests administered for that week. While Cases per 100K is a *rolling average*, each Positive Test Rate value is calculated in weekly steps. For example, the week 28 value is calculated from 7/05/2020 to 7/11/2020 , and the week 29 value is calculated from 7/12/2020 to 7/18/2020. The diagonal-lined band in the line chart shows a 0% to 5% target range. Kentucky and Ohio do not have testing data that is publicly available, so I couldn't calculate positivity rates for Evansville, Cincinnati, and Louisville.
Charts
Line
The grouped line chart shows historic combined indicator values for each MSA.
- Clicking a line highlights the line and deemphasizes the others. Multiple lines can be highlighted. Highlighting a line also has the same effect on that MSA's error bar in the dumbbell chart.
- Clicking on the brush color in the top-left allows you to change the default color (red) to either blue, green, or purple.
- Drawing a window around a section of the chart zooms into that time window
- Pan or Zoom-out (toolbar) can be useful for looking at edge values
- Home button (toolbar) or double-clicking in the plot area resets the chart
- Compare-data-on-hover button (toolbar) shows every label for each MSA as you move your cursor across the days.
Dumbbell
The dumbbell chart shows the combined indicator value (gray) and the 95% confidence interval.
- Clicking on a point, highlights that MSA's value and deemphasizes the others. It also has the same effect for the MSA's values in the line chart. Multiple MSA values may be selected.
- Double-clicking in the plot area resets the chart.
Map
The map shows the metropolitan statistical areas that have calculated combined indicator values.
- Hovering over an area highlights the borders of that area.
- Clicking on an area shows its estimated combined indicator value.
- Reset button (upper-left) resets the map view. It's a bit buggy, so you might need to refresh the webpage instead.
Table
The table shows a scaled, weekly average of COVID-19 cases and a weekly positivity rate for MSAs that include states that have made their testing data available.
Both values can be used to gain a better understanding of the current situation in each MSA. For example, a moderate Cases per 100K value and a relatively higher Positive Test Rate could be interpreted as an area with substantial viral transmission but not enough testing.
The data date for Cases per 100K and Positive Test Rate displays by hovering over column names or over last value of trend column.
Cases per 100K
- Tipping Point (25+ cases)
- Accelerated Spread (10 - 24 cases)
- Community Spread (1 - 9 cases)
- On Track for Containment (less than 1)
Positive Test Rate
- 20%
- 15%
- 10%
- 5%
- 1%
Dashboard {data-icon="glyphicon-stats"}
=====================================
Row {data-height=360}
-------------------------------------
### Historic Values
```{r}
# styling for axes labels and ticks
x_style_line <- list(
title = "Combined indicator over time",
titlefont = list(
color = "black",
face = "bold",
size = 18
),
tickfont = list(
face = "bold",
size = 14
)
)
y_style_line <- list(
title = "",
tickfont = list(
color = "black",
face = "bold",
size = 16
)
)
# tried to get hoverlabel to have background color conditional on value (bgcolor) but failed. Text color would depend on darkness of bgcolor.
# group_by was screwing up something, but I forget (maybe crosstalk). Split does same/similar thing
ci_shared_line %>%
# group_by(name) %>%
# "~" are for variables in df
plot_ly(x = ~time_value, y = ~value,
color = I("black"), split = ~name,
text = ~name,
# part removes "trace" from text
hovertemplate = paste(
"%{x}
",
"%{text}
",
"Combined: %{y}",
" part removes "trace" from text
hovertemplate = paste(
"%{text}
",
"Combined: %{x}",
"