| Latest Data on Covid-19 in Connecticut | |||
| Data as of June 24, 2022 | |||
| total to date |
since yesterday |
since a week ago |
|
|---|---|---|---|
| Cases | 825,075 | +780 | +4,205 |
| Deaths | 11,034 | 0 | +19 |
| Currently in Hospital | 229 | -4 | -32 |
| Tests | 14,738,996 | +9,756 | +54,136 |
| Rate of positive tests | 7% | 6.7% | |
| Nursing home cases | 8,672 | +3 | |
| Nursing home deaths | 2,719 | +72 | |
| Source: Multiple tables from Connecticut Open Data | |||
Tracking Covid-19 in Connecticut
Daily Summary of Connecticut Data
COVID19
Summary of Connecticut Covid-19 based on Department of Public Health Data with some comparisons to national data. This has been updated each weekday during 2020 and most of 2021. There are also notes on sources of data for Covid-19 in Connecticut.
Under Repair
The Connecticut Department of Public Health made some changes in their datasets at the end of June 2022, and I have not yet caught up with those changes. As a result, I’m not updating the data regularly.
I started publishing this in as a blog post in June 2020. Each weekday I would update that post with the data from Connecticut state sources. The blog format never made sense for doing a frequent update of that type of data. I have moved the daily charts into Projects. The original post remains. I plan to redirect the old URL for that blog post so that it leads to this project page.
Latest Connecticut Data from the Department of Public Health
See most recent weekly report from the Department of Public Health for detail about cases, hospitalizations, and deaths among vaccinated compared with unvaccinated.
There is a Connecticut data tracker map that displays the rate of cases in the last two weeks by town. It is color-coded to indicate where cases are most prevalent.
Tracking the Virus in Connecticut
The first plot will show the average number of new cases reported each day along with a line that displays the rolling seven-day average. Typically there are day-of-the-week effects in the reporting so it’s best to focus on the seven-day average.
New Covid-19 Cases in Connecticut
The plot shows the history of new cases and also shows the actions required by some of Governor Lamont’s executive orders.
Covid-19 Deaths in Connecticut
Connecticut Covid-19 Patients in Hospital
A key issue is how fast epidemic is expanding or contracting. One indicator of that is to estimate the parameter Rt, the average number of people who become infected by an infectious person. When Rt is greater than 1 the number of daily new cases is increasing, If Rt is less than 1, it is decreasing. See the site covidestim · COVID-19 nowcasting for an estimate by state of the effective value for Rt. The site is described in this article at Vox.com. I don’t claim to have the expertise to evaluate the quality of the calculation of R at the covidestim site, but it seems to jive with other information about the pattern of the epidemic among states.
I have included Rt for Arizona as well as Connecticut to provide a contrast with a state that has lately shown signs of a growing outbreak. The goal in Connecticut and in the entire New York City area is to keep Rt well under 1. As of 02/27/2022 Rt is 0.54. On the other hand, in Arizona Rt is about 0.67.
Comparisons with Other States
Estimate of Rt by State (from covidestim · COVID-19 nowcasting)
The next display shows the trend of Rt in each state arranged according to geographic position in the US. Remember that Rt is a measure of the rate of increase. When Rt is greater than 1 the number of daily new cases is increasing, If Rt is less than 1, it is decreasing. The color of the R line shows whether the most recent estimate of R for a state is above 1 (purple line) or equal or below 1 (green line).
R is estimated from the state data and the gray area around the line indicates the statistical uncertainty of the estimate showing an 95% confidence interval. Note that this map shows only whether transmission seems to be growing or shrinking. How concerned one should be about the epidemic in a particular state also depends on the prevalence of cases.
Trends in New Cases by State
The next map shows the history of new cases in each state per 100K of population. The lines are color coded the same as in the previous map to indicate whether the most recent estimate of R for a state is above 1 (purple line) or equal or below 1 (green line).
The tallest graphs of new cases per 100K of population are New York and Arizona.. From the point of view of someone living in Connecticut, the striking thing is how thoroughly Connecticut (and New York, New Jersey, Rhode Island, and Massachusetts) have recovered from the peak. New cases are low and the low value of the estimate of R confirms that they are likely to decrease even more. The great contrast is with the increase of cases in some of the southern states.
Connecticut Covid-19 Deaths and Cases by Age
Deaths increase with age, as has been well known since the beginning of the epidemic. For cases, the number of cases per population stays relatively flat from 20 to 79 and rises very rapidly for the oldest category. I haven’t examined this data closely, but I assume this pattern is related to the high rates of mortality in nursing homes. For the age range from 60 to 79 there probably are a lot of seniors who are going to a great deal of trouble avoiding places and situations where there is risk of catching the virus. But individuals in nursing homes don’t have that much control over their own lives. I would guess that the older population outside of nursing homes has a lower rate of catching the virus and the population inside nursing homes has a higher rate. The two trends may average out to a rate that is the same as middle aged adults. This is just speculation. I haven’t dug into the data to say anything firm about this
Nursing Homes
I discussed nursing homes and prisons in the post last June. I have not been updating that data since the summer.
Variations by County in Connecticut
Next I’ll look at variations within Connecticut. Below are two maps showing the eight counties in Connecticut1. The first map in Figure @ref(fig:county-maps) shows the cumulative total of cases since the start of the epidemic. The second map shows only new cases reporting during the last two weeks. On both maps, cases are adjusted to show cases per 100K of population. So the first map relates more to the total impact of the disease over the full course of the epidemic to date, and the second map is an indication of the recent prevalence of the virus.
Connecticut is part of the epidemic’s surge in the New York area and the magnitude of the epidemic has been greater in the counties closer to New York City. Back in March cases first appeared in Fairfield County and then spread to Litchfield, New Haven, and Hartford. More recently Hartford has been the area that we have to watch.
By County and Type of Town
There are 169 towns in Connecticut, which makes it hard to examine variations by town. To help examine variations within counties, I’ll use a typology of towns called The Five Connecticuts that divides towns into five categories based on census variables: Urban Core, Urban Periphery, Suburban, Rural, and a fifth category for Wealthy Suburbs used for some towns in Fairfield County. Adjusting for population, the number of cases has been greater in the counties closer to New York City. In the figure below I’ll fold the wealthy towns into the Suburban category. I have excluded Montville and Somers because the prisons in those towns complicates interpretation of the town statistics.
Counties are laid out from top to bottom and the counties closer to New York are toward the top. The columns show the four categories. Urban towns have been hit harder than suburban and rural towns. While density may have some effect on case rates, the report Towards Health Equity in Connecticut: The Role of Social Inequality and the Impact of COVID-19 by DataHaven documents how the epidemic interacts with existing social inequality in Connecticut.
Details by Town
The links below take you to a separate HTML page for each Connecticut county. That page contains a table with one row for each town in the county.
For each town it shows the category, the population, the total number of cases to date, the number of recent cases per 100K of population (total new cases in the last two weeks), total deaths, and the percentage of total deaths in that town attributed to nursing home residents.
Next there are two sparklines that show the trend in daily new cases and daily deaths (based on 14 day moving average). Cases and deaths are shown as a ratio to population in the town.
The purpose of these tables is to make it easier to quickly scan for trends in local towns. Here is sample (based on my home of Guilford) showing what is available for each town.
For each county there is also a map showing the towns in that county and the relative number of total cases in the town and a second map that shows the percentage of individuals who are below the poverty line.
The town sparklines at the right show the trends over time. The vertical scale is different for new cases and for deaths. For new cases, the maximum height shown on the scale is 100 while for deaths, the maximum value shown is 28.6. For both new cases and deaths, the sparkline shows the rolling seven day average of new cases or deaths.
| town | category | total population | total cases | recent cases per 100K | total deaths | % deaths from nursing homes | town cases | town deaths |
|---|---|---|---|---|---|---|---|---|
| Guilford | Suburban | 22,285 | 4,363 | 417.3 | 45 | 82% |
Links to Summary Page for Each Connecticut County
Below are links to a separate web page by for each of the eight counties in Connecticut. Click on the county name for detail about individual towns within that county.
| Litchfield County | Hartford County | Tolland County | Windham County |
| Fairfield County | New Haven County | Middlesex County | New London County |
Links to Sources
State of Connecticut Department of Public Health
- Daily Covid-19 Update in the form of a PDF. A list of the reports is available via Connecticut Open Data.
- Connecticut Covid-19 Data Tracker
- Links to all Covid-19 items available via Connecticut Open Data
The Connecticut Open Data site is a tremendous resource and I expect to use it for other projects in the future. The RSocrata package made it very easy to download this data. It’s run by Connecticut’s Chief Data Officer under the auspices of the Office of Policy and Management. They have produced a high-quality data portal.
Mapping and Covid-19 at the Yale Medical School I have had an opportunity to observe some of these efforts for me it has been a great source of information and inspiration.
DataHaven provides some Connecticut-focused information on Covid-19. They also published a report on how Covid-19 relates to existing inequalities in Connecticut.
CTData Collaborative also provides data on Covid-19 and its impact in Connecticut.
Connecticut COVID-19 Wastewater Project Report on test results on New Haven area sewage.
Yale SARS-CoV-2 Genomic Surveillance Initiative Genomic sequencing of some cases in Connecticut.
COVID Tracking Project The data for Figure @ref(fig:p-states-map) is from COVID Tracking Project and is used under a Creative Commons CC BY-NC-4.0 license.
For Rt I am using covidestim · COVID-19 nowcasting. I previously used rt.live, but they stopped doing estimates and referred me to Covid-19 nowcasting.
Covid ActNow I only learned about this project today, but it seems quite interesting.
CovidTrackCT uses genetic sequence mapping to trace what strains have they have seen in Connecticut. This shows connection to New York and continued local transmission within Connectiut.
Methodology and Notes
Code Used to Produce This Post
The R code used to download and process the data from Connecticut and elsewhere is in the file daily_ct_stats. This post was created using RMarkdown so the code to create most of the figures is in the .Rmd file for this post.
The separate HTML files for each Connecticut county were created using the rmarkdown::render function. The RMarkdown document that actually formatted the town details (including sparklines and county maps) is here.
Some noteworthy packages used here:
geofacet used to create the map of US states in Figures @ref(fig:p-rt-map) and @ref(fig:p-states-map).
tidycensus used to create the Connecticut maps and retrieve Census data (most of which I didn’t use).
formattable – Used to create the town-by-town detail tables primarily because this was the only tool I found that would readily add a sparkline to a table.
Some Notes on the Data Available From the Department of Public Health
Sometimes there is a lag before cases and deaths end up in the daily reports. As a result, there tends to be a “day of the week” effect. For that reason, observers of the Covid-19 statistics generally focus on a 7-day rolling average of the daily counts. Some of the unusually large peaks and valleys in these charts are due to reporting process. The DPH has also begun producing reports based on the date a sample was taken for a test and by the date of death. That’s a more accurate way to look at the change over time, but it means the data for the most recent days are hard to interpret because some data may be “in process” and not yet reported. In these charts I have used “date of report” rather than “date of sample taken” or “date of death” because that gives me all of the recent data that is available and because that is what most other data projects (such as the COVID Tracking Project or the New York Times) have been using.
The DPH reports always include a comment that “all data are preliminary and subject to change.” But when they make and adjustment to correct errors, as far as I can tell they do not go back and adjust the earlier reports. That can lead to a misleading report of recent changes. For example, in one note below they removed 70 cases because of errors on a day in which there were 81 new cases. In the data series that I download, that shows up as having been 11 new cases that day, not 81, because 70 were removed. Earlier days are not corrected.
*In Connecticut during the early months of this pandemic, it became increasingly clear that it would be necessary to track probable COVID-19 cases and deaths, in addition to laboratory-confirmed (RT- PCR) cases and deaths. This was needed to better measure the burden and impact of this disease in our communities and is now part of the national surveillance case definition for COVID-19. Today for the first time, DPH is reporting cases and deaths as “confirmed” or “probable.” Previous reports reported these as a combined number. The only change today is that they are being separated to conform with CDC reporting guidance. Probable cases of COVID-19 involve persons who have not had confirmatory laboratory testing (RT-PCR) performed for COVID-19, but whose symptoms indicate they are very likely to have a COVID-19 infection. In Connecticut, most of the probable COVID-19 cases involve persons whose death certificates list COVID-19 disease or SARS-CoV-2 as a cause of death or a significant condition contributing to death.
The staff at the Department of Public Health have removed 356 cases and 808 tests in the past 24 hours, which were identified as duplicates in the system, affecting both test and overall case numbers. Since yesterday, there have been 341 new positive cases, and 5,215 new tests were reported.
Please note that 81 new cases were reported in the past 24-hours; 70 previously reported cases were removed from the total counts due to correction of data errors.
In the data portal, the number of cases reported for Montville was 381 on June 17. As of June 18 it reports 293 cases, a reduction of 88 cases. That’s more than the 70 cases removed. Perhaps the case counts for Montville were affected by inmates being moved within the Connecticut prison system.
1175 new test results were reported since the last report and 2770 previously reported PCR tests were removed due to correction of data errors.
*Please note 83 new cases were reported to DPH since yesterday. In addition, 74 previously reported cases were removed due to updated laboratory findings of false positive results.
July 24, 2020: Governor’s press release
*NOTE: Today’s update includes a large set of data provided by an out-of-state lab on tests that were conducted on Connecticut residents between May 23 and July 20 and not reported to the State of Connecticut until today. This data set provided by the out-of-state lab includes approximately 12,000 tests, 440 of which were positive. The remaining 104 positive cases in today’s report are newly reported cases in the day-to-day update, giving a 0.79% positivity rate for the day.
See also Hartford Courant, July 24, 2020 I don’t see any explanation of this in the Department of Public Health PDF, but it was clear that an increase of 544 cases in one day would be remarkable.
On Friday, the state also announced a backlog of unreported test results for Connecticut residents dating back to mid-May. Among these approximately 12,000 tests, 440 were positive. On Thursday, the state reported an additional 13,000 tests. With the 544 new cases, the state has now recorded 48,776 coronavirus cases.
*In addition to the 79 recently diagnosed cases and 12,367 tests, 384 cases and 750 tests performed between April and June were newly reported to the Department of Public Health in connection with a transition to electronic reporting by an out-of-state regional laboratory. For surveillance purposes, that data has been added to the total case and total test counts.
Note as of August 18, 2020 DPH daily update:
*Forty new cases were reported to CT DPH since yesterday; in addition, DPH removed 52 previously reported cases because of newly identified data errors.
As of Monday, there were additional 1,066 positive cases from Friday. That included over 270 positive cases out of 23,130 tests conducted between September 26 and October 8 that are newly reported as part of catch-up reporting.
Note as of September 30, 2021:
Today’s total deaths reflects an increase of 146. This total includes 53 deaths newly reported in the last week and a reconciliation of 93 deaths that occurred among Connecticut residents who died out of state. These out of state deaths span the entire course of the pandemic with the majority occurring in 2020.
Footnotes
The data displayed on these maps excludes the towns of Somers, Brooklyn, and Montville because they are small rural towns with prisons, and the cases reported in the prisons may dominate the total in the town and even affect the reporting for the county overall. Covid-19 in prisons is a significant issue, but it’s helpful to try to evaluate it separately from data from the non-prison community.↩︎