We don’t know how many people in the US have really recovered from COVID-19.
The HHS released a facility-level data set on COVID-19 hospitalizations in December. We’ve taken that data and created an interactive map, allowing the public to see how their local hospitals are faring against this virus.
Holiday reporting has garbled most metrics. Going by current COVID-19 hospitalizations, outbreaks in the Midwest are still easing, but every other region is in trouble.
States provide COVID-19 data in a variety of sources and formats. To ensure our data is as accurate and consistent as possible, we spend a lot of time looking at these sources to make sure that we’re capturing the most data possible for each state, while maintaining high standards of data quality and integrity. Today, we’re publicly releasing a detailed set of notes on the sources of all our data points.
COVID-19 hospitalizations continue to rise across the country, but the method in which they are reported can take several different forms. We dive into the meaning of some of the most common metrics and comment on why some may be more interpretable than others.
The Department of Health and Human Services released a new public dataset on December 7 that includes data down to the facility level on where COVID-19 patients are hospitalized and how healthcare systems are coping. We explored how to use this dataset and what patterns it can reveal.
Despite a rocky beginning, the current hospitalization and new admissions metrics from the HHS Protect public dataset have stabilized—and they’re now largely harmonious with state-reported hospitalization metrics if we account for differences in data definitions and reporting lag time.
As North Dakota’s Deaths Metrics Diverge, We’re Switching to a Less Backlogged Measure of Fatalities
North Dakota's growing backlog of death certificates means its count of individuals who died due to COVID is no longer an accurate measure of deaths in the state. As a result, we're switching our metric to deaths among cases, matching the state’s most prominent method of reporting.
The upcoming holiday means that many COVID-19 metrics are going to drop—and then rise—in ways that may trip up unsuspecting observers. Here’s what to watch out for.
More people are now in the hospital with COVID-19 than ever before, and the per-capita hospitalization rates in the Midwest have now surpassed those of the South in the summer’s Sunbelt surge. Hospitals across the country are warning of staff and PPE shortages, and case rates continue to spike in every US region.
Our new data collection tracks the spread of COVID-19 in 65 cities and counties across the United States, and it lets us see how fatality rates vary widely across geographies.
As COVID-19 cases and hospitalizations once again rise across the United States so, inevitably, will deaths. But there is reason to hope that we will not see the devastating fatality rates of the initial spring surge.
As COVID-19 cases rise across the United States, claims are circulating that case increases are (mostly or entirely) due to expanded testing, and do not indicate a spike in infections. The data does not support this conclusion.
Several sites tracking the progression of the virus hit a grim milestone today: more than 200,000 deaths since the pandemic began. Our figures haven’t yet reached that level. Here’s why.
Though cases are rising in parts of the Midwest, hospitalizations in the West and South continued trending downward. The Labor Day holiday impacted data reporting lag times both this week and last, obscuring what had been positive trends in September.
A long holiday weekend makes ambiguous testing data even harder to understand, but hospitalizations are dropping, which is good.
Key data points in our COVID-19 tracking are finally beginning to trend positively. In the South, tests rose while cases fell, a pattern not seen there since early spring. Hospitalizations fell for the third week straight, but deaths remained above 1,000 a day on average.
Hospitalization Data Reported by the HHS vs. the States: Jumps, Drops, and Other Unexplained Phenomena
In mid-July the federal government began requiring hospitals to report COVID-19 data to the HHS rather than to the CDC. We compared current hospitalization data reported by the federal government and state health departments since the switch, and found contradictions that suggest the federal data continue to be unreliable, while the state datasets face their own challenges.
States across the US use two primary methods for announcing COVID-19 deaths: date of death (reported) and date of death (actual). To analyze how the pandemic is trending across the country, understanding the relationship between these two data points is crucial. Here's what we've learned from investigating both methods in three of our largest hotspots: Arizona, Florida, and Texas.
Data for current COVID-19 hospitalizations in the United States—one of our most valuable metrics for understanding the pandemic and its effects—has become highly erratic in recent weeks. Here's what we've learned from watching the data closely, and from our initial analysis of the hospitalization data being published by the federal government.
The South continues to be the epicenter of surges in both cases and hospitalizations. In Arizona, Florida, South Carolina, and Texas, COVID-19 deaths have begun to climb following jumps in new cases. And for the first time since April, deaths are rising nationally.
We're up to 24 states publishing both confirmed and probable COVID-19 deaths, and we're adding those data points into our API. But states are also using two different ways of deciding which deaths to count as COVID-19 deaths.
Hospitalization data can help us understand the severity of COVID-19 outbreaks in the United States, and even see a little bit of what's to come. Until very recently, we didn't have a national summary figure—now we can finally piece together a national statistic from states that provide it, and estimate the rest.
COVID-19 death data lags behind testing data in ways we mostly understand. What we only partly understand is how an infection rate that seems to be skewing younger will affect the death toll in surging regional outbreaks.