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The counter-narrative began almost instantly. After the U.S. count of Covid-19 cases began an inexorable rise in June, the White House sought to assure Americans that the increase was, basically, an illusion, created by an increase in testing for the novel coronavirus.

In a June 15 tweet, President Trump said testing “makes us look bad.” At his campaign rally in Tulsa five days later, he said he had asked his “people” to “slow the testing down, please.” At a White House press conference last week, he told reporters, “When you test, you create cases.”


And in an interview with Fox News that aired Sunday, Trump could not have been clearer: “Cases are up because we have the best testing in the world and we have the most testing.” Basically, the president was arguing that the U.S. had just as many new cases in June and July as it did in May but, with fewer tests being done in May, they weren’t being detected; with more testing now, they are.

A new STAT analysis of testing data for all 50 states and the District of Columbia, however, shows with simple-to-understand numbers why Trump’s claim is wrong. In only seven states was the rise in reported cases from mid-May to mid-July driven primarily by increased testing. In the other 26 states — among the 33 that saw cases increase during that period — the case count rose because there was actually more disease.

May had brought signs of hope that the U.S. had gotten its Covid-19 outbreak under control, with about 20,000 new cases reported per day after April highs closer to 30,000. But by late June, the daily count climbed to about 40,000, and now it’s at about 70,000. The STAT analysis shows that spread of the virus, far more than testing, explains that increase.


Epidemiologists and infectious disease experts have disputed the White House claims for weeks, citing rising hospitalization numbers and deaths. It’s hard to argue that extremely sick people, let alone dead people, had been obscured by low levels of testing but suddenly revealed by higher levels.

Without a doubt, many cases of Covid-19 in March, April, and May weren’t picked up. In late June, Centers for Disease Control and Prevention Director Robert Redfield told reporters that as many as 90% of cases had been missed; that is, although there were 2.3 million confirmed cases in the U.S. then, some 20 million people had probably been infected. But that reasoning applies today, too: Despite months of government claims to the contrary, not everyone who wants, or should have, a test is getting one.

Simple math belies the “it’s just because of more testing” claim — with some fascinating exceptions.

Using data from Covid Tracking, STAT looked at the number of people tested and the number who tested positive for the disease (cases) in every state and Washington, D.C. We did that for three dates: in mid-May, mid-June, and mid-July. (Due to reporting anomalies, the dates selected sometimes differed by a day or two between states.)

For each date, we calculated the number of cases found per 1,000 tests — a measure of the disease’s prevalence. For example, in Florida on May 13, that rate was 32. On June 13 it was 75. On July 13 it was 193. On May 13, Florida tested 15,159 people; on July 13, it tested 65,567. So indeed, the number of tests has increased.

But the number of cases per thousand, which is independent of the number of tests, has skyrocketed. On May 13, Florida recorded 479 cases; on July 13, it found 12,624. If the prevalence of Covid-19 were the same in July as in May, Florida would have found only 2,098 cases. In other words, 10,526 of the July 13 cases are not due to increased testing, but, instead, to the increased prevalence of disease.

Florida Gov. Ron DeSantis, however, echoes Trump’s explanation, telling a Saturday press briefing that his state’s soaring caseload is largely the result of more testing of people with no or minimal symptoms. “We’re now capturing a lot of those folks,” he said.

In fact, Florida has seen a sevenfold increase in cases in the past month, said Youyang Gu, who developed a well-respected, machine-learning-based model of Covid-19 whose projections have been quite accurate. “In the same time span, the number of tests only increased by a factor of two,” he said. “Obviously, if you double the testing but the number of cases increased sevenfold, then the virus is clearly spreading.”

Testing/cases graphic

The complete data for all 50 states can be found here.


Other states with soaring cases tell the same story as Florida.

In Arizona, the case-finding rate rose from 90 in May to 140 in June to 208 in July. Of its 2,537 cases on July 12, 1,441 were due to increased prevalence.

South Carolina has also experienced a steep rise in prevalence as its case count quintupled: Of the 2,280 cases on July 9, 1,869 were due to rising prevalence, not more testing. Texas and Georgia are similar: rising case counts well beyond increases in testing. In all, 26 states that did more testing in July than in May found more cases because Covid-19 was more prevalent. In 15 of them, the number of cases per 1,000 people tested had more than doubled.

Seven states (Colorado, Indiana, Michigan, Missouri, North Carolina, Ohio, and Wisconsin) meet the three criteria needed to support Trump’s claim that we’re seeing more cases only, or mostly, because we’re doing more testing. The criteria are doing more tests in July than in May, finding more cases on a typical day in July than May, but seeing the number of cases per 1,000 tests decline or remain unchanged from May to July.

Take Missouri. It’s reporting more cases, but not because the virus is exploding there (despite those crowded holiday scenes at Lake of the Ozarks). Its case finding rate has been pretty stable or even declining, from 48 in mid-May to 44 in mid-July. By tripling its number of daily tests, Missouri is finding roughly triple the number of cases.

California comes close to meeting the three criteria, but doesn’t quite. Its number of daily tests more than quadrupled from May to July, from roughly 32,000 to 137,000. But the rate of cases being found has risen, though only about 10%, from 55 to 61 per 1,000 tests. So a big reason — but not the main reason, as in Missouri — more cases are being found is that more testing is being done. Washington is similar: more testing, more cases, but also slightly greater prevalence of disease in mid-July compared to mid-May; its worsening situation is real.

New York tells the opposite story: more testing found fewer cases. The state nearly doubled its daily tests from May 13 (33,794) to July 12 (62,418). But its cases fell from 2,176 to 557. If the case rate had not dropped (by 86 %), New York’s expanded testing would have found 3,995 cases on July 12.

In fact, 16 states plus the District of Columbia are like New York. They tested much more, but found fewer cases in July than May — in most, not only “fewer” in the sense of fewer cases per 1,000 but fewer in absolute terms. New Jersey reported 10,246 tests and 1,144 cases on May 14, and 20,846 tests with a mere 393 new cases on July 14. Again, the virus hasn’t disappeared, but the expansion of testing, far from “creating” cases, has brought good news: In these states, it’s much less prevalent than it was two months ago.

  • I may have missed it but I did not see any adjustment made related to whether or not the person tested was showing symptoms. That could seriously skew to data. Maybe the states showing lower apparent infections rates are starting to see population immunity effects. The early testing seemed to violate standards of randomness necessary to made valid judgements based on the results. Is any of the current testing actually random in the population or is it still biased by participant concerns? Without this basic consideration the current reported testing results have very little value.

    • Wrong, wrong, wrong! The statistical relationship (r square) between numbers tested and number of confirmed cases is .93! It means that there is a 93% direct relationship between testing and cases, leaving a 7% error in the data. Look it up.
      Using data from each state to compare or proving anything is, at best, misleading. Every state is suffering and managing the problem differently. At worst your information smacks of unabated political bias.

      At first, it was not random. Only those diagnosed with symptoms were tested. The rate of positive infections was about 25%. Now that anyone can get tested, the rate of positives has gone down to 8.5%.(more randomness now.) You are correct about randomness. What should have been done at the beginning is to establish totally random testing of the population. It was not done, not only in the US but nowhere else. Now we have inaccurate interpretations and outlandish projections.

  • Of those 7 that aren’t seeing an uptick in percent positive, 5 were added today to NYS’ “travel advisory”. To qualify for that list, the state must have “a positive test rate higher than 10 per 100,000 residents over a 7-day rolling average or [be] a state with a 10 percent or higher positivity rate over a 7-day rolling average”. So it would seem that they were observed having an uptick in one of those two metrics.
    What’s going on, statisticians?

  • The President is right. If other countries tested as much as the U.S. does, they would find more cases. We are testing people without any signs or symptoms and calling nearly any cause of death (including deaths from trauma) covid. I’m amazed how that no one is dying from stroke, heart attacks, motor vehicle collisions, or other trauma these days… they all die from covid 19. BTW they are testing the same people over and over again getting positive results and calling them new cases when they most certainly are not. They are cooking the books for political purposes. We the people are on to you…. Hmmmm……

    • Exactly. My husband is an ER doc. I am an RN.
      There is plenty of false narrative being put out.
      New cases don’t mean ‘new sick’ people. New cases, amongst genuinely sick people, can mean people who tested positive after electing to go and be tested. There are thousands more tests being done on a daily basis now. Statistically the ‘positive’ numbers will soar as more and more tests are done, al eit most are not developing symptoms.
      If you die of a fatal gun shot wound; a myocardial infarction; a cerebral vascular accident; or a fatal motor vehicle accident; were tested as you were brought into the Emergency department, and the blood work comes back ‘positive’ after you expire You still died FROM OR OF the ‘emergency’ situation that you were brought to the ED with, not Covid 19. You died ‘WITH’ covid 19 and NOT ‘OF’ it. You will then join the statistical number of ‘cases’ along with the people who elect to be tested whether they have symptoms, or not, and also the cases 0f truly sick people, who are generally high risk compromised people. The media are playing a ruthless game with the general ‘lay’ public.

  • Yes, more cases when you test more. Also more cases when you throw multiple negative and a final positive results from the SAME person. Also, more cases when you force a positive test result on a said person that you never even tested. And more cases when you throw a negative result into the case pile. More cases when you aren’t actually specifying a COVID-19 test only a Coronavirus test (there are many viruses that fall under Coronavirus). Not saying the virus isnt here just that the numbers are epically Inflated and the fear mongering is unbelievable. The media and this article is the Virus. The Cure is worse than the disease. Wake up people!!

  • Asymptomatic people can still spread the disease to others, just because they are asymptomatic doesn’t mean that the people they infect will be. Testing for all cases gives us an idea of where it’s spreading which may help us reduce the number of cases altogether, and have less significant illness.

  • Nobody is discounting the tragedy of deaths, but you apparently confuse reported infection rates with cases of significant illness. Many people that contract the virus recover with only minor symptoms or none at all. The results of the large California county study that showed widespread Covid19 antibodies in asymptomatic people was consistent with that.

  • Thank you for your efforts. Interesting read and interesting conclusion … considering the variables. To comment on right or wrong, wouldn’t this require definitive knowledge on the accuracy of testing kits today vs past testing kits? This could be overcome by ensuring the data used per 1,000 test analysis came from identical testing methodologies. Is this true for this analysis? Or did the analysis lump in various testing methodologies and treat them all the same? If the latter, we fear “math” is now being used to write an opinion piece.

    It has been widely circulated, discussed and lamented over by the US public – why was testing originally so inadequate … and, so poor? So inadequate, in may ways. So poor, in its methodology. We are now well aware past testing occurred with faulty testing kits proven to be problematic. Not problematic with false positives, problematic with delivering conclusive results.

    From what we read now, it appears this challenge of poor testing has been addressed and many methodologies utilized today have the ability to deliver conclusive results. Thank goodness!

  • Prevalence rate is down in Ohio, yet we are now under a mandatory face covering rule? Good article and the quality of information we need. This information is what should be coming from our CDC and IHS.

  • How do we let 4 million people get sick? It is beyond our control.

    We are in a state of constant war with viruses, bacteria and parasites that infest or our environment. If not for the fabulous action of our immune system, we would rapidly succumb to these pathogens (as happened to people who immune systems were destroyed by HIV).

    Despite the incredible miracle of our immune system, hundreds of millions of people get sick every year due to infections and many die, despite the heroic efforts of modern medicine (when available).

    Unfortunately, the only way possible stop the spread of the virus once it gets established in the population is to achieve herd immunity naturally (by contagion) or by vaccination. But here’s the catch-22. For an infection to be recognized as a pandemic it must be established in the population. By the time it is recognized, it is to late to contain it. I think the best analogy is the adage, “closing the barn doors after the horses have left”. One can close the barn doors but it is ineffective at this point.

    Each year many people get sick due to influenza and some die (including those over 65 and young children). And this is with widespread vaccination and the availability of Tamiflu and other anti-virals. Children are at risk for Kawasaki syndrome. Here’s the truth: children have been getting Kawasaki syndrome for decades. COVID-19 is another suspected etiology. But here’s a bigger risk for children: acute flaccid myelitis due to coxsackie virus. Where is your outrage at this?

    Locking down the economy is not without unwanted side effects. The suicide rate goes up due to depression, despair and economic destitution. Domestic abuse increases.

    We were lucky that when the first lock down was imposed the warehouses were full of food. But as is always the case, if a stock is not replaced, it is eventually exhausted. This was recognized by the Government and food suppliers were deemed essential.

    But here’s the catch: every job is essential to the worker. Every job is essential to the economic functioning of society. Without a functioning economy the result is scarcity. Putting food suppliers back to work opens up the walls of containment to allow the virus to spread.

    Not everyone is at risk. We know the at risk populations and care can be taken to help and insulate them. But the best containment plans always produce leaks due to the human element. If you remember, the CDC released live smallpox due to human error.

    Widespread testing shows us that the virus is not as virulent as initially feared. This is not to say it is not virulent; people will get sick and some will die. It is more on the order of a bad flu season (as was seen in the H3/N2 pandemic of 1968. In that season, a population adjusted death number would be about 233,000).

    It is not a question about selfishness. It is a question about risk and reward; prudence and recklessness; cost and benefit.

    • This is disingenuous to characterize this as merely “a bad flu season (as was seen in the H3/N2 pandemic of 1968. In that season, a population adjusted death number would be about 233,000)”. We are 6 months into a pandemic that is projected to kill between 250,000 at the lowest (unadjusted real people) and frequently modeled as high as 1.25 million. The final number, when the pandemic is over later this year or early next year will be a function of what we as a society did to mitigate and prevent its spread to those who died, not just what we did or did not do in the early days but what we do going forward. This attempt to discuss the crisis in dismissive terms, minimizing the risks and impact has been the driving force for the comparatively terrible response to date by the Federal and some State governments.
      Other large economies have addressed the problem early and decisively with clear and consistent messaging so that their populations have been able to restrict its spread and minimize deaths. In doing so they are better positioned to address their economic issues and have a higher likelihood of restarting their economies successfully. If you look at the historical precedence of the Spanish flu the cities in the US that shut down hard and stayed shut down until it passed were able to recover and grow significantly more successfully than those that prematurely reopened and ended up shutting down twice or three times. For many people who get C-19 and do not die it is not a case of ‘as you were’, we have only begun to scratch the surface of the secondary severe clinical issues from blood clots to renal failure and loss of cognition; does that sound like a ‘bad flu season’ outcome?
      We have to do what is required to stop its spread at whatever the cost to the economy, ultimately there is no economy if everyone is ill, frightened of getting ill or giving it to their parents and grandparents; just ask anyone in the hospitality business in Texas, Arizona or California. Major advanced economies such as Germany, Denmark, Holland, France, Spain, Italy, Taiwan, South Korea, Norway, New Zealand have taken this hard nosed approach and they are now in a situation where they are planning for a future. We have got better at treating it as we have lots of cases to experiment on, that is something to be happy about rather than be used as yet another reason why not to take it seriously.
      Finally, as it seems to underpin most of these laissez faire apologist arguments for not disrupting someone’s investment plans, the silver bullet that is the much vaunted vaccine may yet be a very big disappointment. We have according to Milliken Institute 197 vaccines and 271 treatments under various stages of clinical trials globally. Recent studies show limited persistence of the antibodies though. One study showed 10 percent of nearly 1,500 COVID-positive patients registered undetectable antibody levels within weeks of first showing symptoms. In another in 74 patients found they typically lost their antibodies two to three months after recovering from the infection, especially among those who tested positive but were asymptomatic. In contrast, infections caused by coronavirus cousins such as SARS and MERS result in antibodies that remain in the body for nearly a year. At best people may need multiple vaccinations which poses an increased risk for severe adverse reactions such as ADE. So think of this as less a one shot vaccine and the world goes back to normal and more like an annual flu vaccine with a flu that has a 5-10% chance of killing you.

    • I think you both made some good points.
      Just wanted to clarify what Jim Buttons threw in about “a 5%-10% chance of killing you”. It may be that 9-10% of detected cases die, but serological surveys seem to indicate that over 90% of cases are not detected, reducing the death rate, out of the total infected persons, to under 1%. My datasets seem to indicate that it’s probably around 0.6-0.7%.
      Perhaps he meant that figure for the most vulnerable population groups,
      or he meant a calculation of the lifetime risk. For the real mathematics fans on this site, maybe try a calculus series with some constant to indicate the drop in risk with increasing experiences of the immune system.

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