The purpose of this website is to allow easy access and analysis of Covid-19 data for the state of Texas, including subsets by major metropolitan areas, and by individual counties. The underlying data is updated every day at 1:40 PM.
The daily data (cases by county, state totals, and Total tests) is pulled off the website for the Texas State Department of Health https://dshs.texas.gov/news/updates.shtm#coronavirus.
There are a few peculiarities of this data which users should be aware of:
- The data reported is what they had received at 8 PM the previous day
- They update their website by noon, usually.
- They have a category "Pending County Assignment" that can be quite large. The county assigned to a case is the county of residence, not the reporting county. This has been discontinued.
For these reasons, my numbers will differ from news accounts, and tend to lag by about a day. County and metro area total will lag by several days as the state researches county of residence, but the state total should be fairly current.
On March 25, the following message appeared on the state website:
Why did the case counts reported by DSHS increase suddenly?
DSHS updated the method of reporting COVID-19 cases in Texas to provide the public with more timely information. The DSHS daily case count now includes all cases reported publicly by local health departments around the state. With the change, Texas is now reporting an additional 305 cases of COVID-19.
Because the state website only gave deaths by county for a single day, the death by county numbers are now scraped off the state emergency response website at https://opendata.arcgis.com/datasets/bc83058386d2434ca8cf90b26dc6b580_0.csv
To infill the missing death by date by county data, the New York Times has helpfully provided a nice datafile at https://github.com/nytimes/covid-19-data
For daily updates of the deaths by county, I use data from the Texas Division of Emergency Management website, which they get from the Health Department.
The Graph tab is designed to allow easy data analysis to understand the time progression of the epidemic.
Epidemics grow exponentially, or not at all. The basic equation governing epidemic growth is
Counter measures include "social distancing", which reduces E, and hygiene measures like hand washing, which reduces p.
A Logistic fit starts out exponential, but then as the epidemic peaks,
it flattens and asymptotically approaches the final epidemic size.
The simple logistic equation I will use for modeling is
-
$N_{d}=$ number of cases on day d -
$K =$ final epidemic size -
$N_{0} =$ initial number of cases -
$r =$ the early growth rate (about 0.24 in China) -
$d =$ day number
A logistic has its inflection point - where it starts to flatten - at halfway to the maximum value. So if it looks like the growth curve is starting to flatten, then we can expect the number of cases to double before the epidemic ends.
Data may be selected by Region (metro area or the whole state) or by individual county.
The metro areas follow the standard definition, and consist of counties:
- Houston: Harris, Fort Bend, Galveston, Waller, Montgomery, Liberty, Brazoria, Chambers, and Austin
- Dallas: Collin, Dallas, Denton, Ellis, Hood, Hunt, Johnson, Kaufman, Parker, Rockwall, Somervell, Tarrant and Wise
- San Antonio: Atascosa, Bandera, Bexar, Comal, Guadalupe, Kendall, Medina, and Wilson
- Austin: Bastrop, Caldwell, Hays, Travis, and Williamson
Under Plotting Options are several toggles that will add things to the plot, or adjust the appearance of the plot.
Suppose I want there to be a less than 1% change of interacting with someone who is infected. It is simple to calculate, it is just the number of infected people, divided by the total population in question, times 0.01. This can give surprisingly small numbers rather quickly, and highlights how crucial it is to not allow crowds of people to gather.
By default, I chop off the Y scale so that the actual data is easily seen. The toogle will expand (or sometimes reduce) the scale so that all plotted objects are visible.
Trying to get a handle on how many cases have been missed due to lack of testing, I assume the global average fit (see below) and force that to fit at the latest data point. I should probably allow the user to input an estimate of current missed cases and fit to that.
The actual fit is done to the logarithm of the number of cases (as can be seen in the displayed fit equation), because that makes the exponential a linear function. A key aspect of the epidemic will be to see a change in the slope of the fit, indicating that the growth rate is slowing. That will be easier to see on a Log plot.
There are a variety of options to try to fit the data and thus predict the future of the epidemic.
This option does a linear regression on
By default, I weight the fitting, to bias the fit towards the newer data,
assuming that because of the slow ramp-up of testing, the early numbers are
probably not very good. To weight them, I arbitrarily use a function
This option fits a logistic curve (see explanation above). The predicted maximum number of cases and the date of the inferred inflection point are also displayed.
The growth rate is amazingly consistent worldwide. The number I am using comes from dart throwing chimp, and it is easy to see on his growth graph that most countries have followed similar trends. His 0.3 growth value translates to my 0.13 slope since I use log base 10, and he uses natural logarithms.
Because the global value is only a slope, I derive the y intercept by tieing the curve to the most recent datapoint.
You can play with values on your own. One thing to keep an eye on as you play with the fit, is what happens to the doubling time.
The doubling time depends only on the slope of the fit, and tells you how many days it will take for the number of cases to double. Usually a pretty frightening number.
In yet another attempt to get a handle on the effect of undetected infectious people wandering around, you can create a new curve which is the original fit, but where the number of cases is multiplied by a constant. Did the lack of testing miss half the cases? Multiply by 2.
This tab will be devoted more to various analysis tabs. Initially there is a tab to look at the death rates, but more tabs are planned.
By default, fitting to cumulative deaths with an unweighted exponential function is done. The usual data selection and plot controls are provided.
A tool to estimate the actual number of cases from the number of deaths is provided.
CFR is the percent Case Mortality Rate, or the mortality rate among diagnosed cases. This will, of course, be a larger number than the IFR, or Infection Fatality Rate, which includes all infected individuals, including those who are asymptomatic. Practically speaking, with the poor status of testing and the rather strict criteria being applied before allowing testing, only people who are pretty sick are getting tested and diagnosed, so number of case estimates based on deaths will be potentially much lower than the actual infection rate. In any event, the literature seems to be averaging in the range of 0.7%-2.5%.
Days to Death is the average time from diagnosis to death. If only people who are already quite ill get tested, then the literature implies this number may be about 13 days. If many people who are not ill get tested, then this number will increase, up to perhaps 20-23 days?
Basically I take each death, work it back to the number of cases at the time that individual was diagnosed/tested, and multiply by the 1/CFR.
For reference, read this paper
The map is pretty simple. You can color counties by total reported cases, or by cases per 100,000 population. Note that the "Pending County Assignment" problem means that there can be a significant lag in a case being tied to a county.