Multiple Authors
By: Donald J. Wheeler, Al Pfadt

Each day we receive data that seek to quantify the Covid-19 pandemic. These daily values tell us how things have changed from yesterday, and give us the current totals, but they are difficult to understand simply because they are only a small piece of the puzzle. And like pieces of a puzzle, data only begin to make sense when they are placed in context. And the best way to place data in context is with an appropriate graph.

When using epidemiological models to evaluate different scenarios it is common to see graphs that portray the number of new cases, or the demand for services, each day.1 Typically, these graphs look something like the curves in figure 1.

Figure 1: Epidemiological models produce curves of new cases under different scenarios in order to compare peak demands over time. (Click image for larger view.)

William A. Levinson’s picture

By: William A. Levinson

The phrase “flatten the curve” means to slow the transmission of the coronavirus (Covid-19) in order to spread the total number of cases out over a longer period of time. This will avoid overwhelming the healthcare system.1 The model is accurate as presented throughout the internet, but it also overlooks terrible dangers and enormous opportunities.

Eric Stoop’s picture

By: Eric Stoop

According to the National Safety Council, the rate of preventable workplace fatalities per 100,000 workers has flattened or risen slightly since 2009 after decades of steady improvement in occupational safety.

Companies conducting layered process audits (LPAs) can help get the United States get back on track reducing the workplace fatality rate by conducting daily checks to help identify safety nonconformances and fix them before they cause safety incidents.

With daily checks of high-risk processes, layered process audits lead to more conversations about safety, also demonstrating that leadership prioritizes safe work—both critical to creating a culture of safety.

Achieving this level of reliability, however, doesn’t happen overnight. Organizations must first make a key mindset shift, and take a strategic approach to uncovering and resolving instances where people don’t follow standards.

The quality-safety link

Quality and safety may occupy two different departments in the average manufacturing organization, but the reality is that safety is itself an aspect of quality.

Multiple Authors
By: Donald J. Wheeler, Al Pfadt, Kathryn J. Whyte

This article is an update to “Tracking Covid-19” that Al Pfadt, Kathryn Whyte, and I wrote last week. In that article we summarized what is known about Covid-19, what has already happened, and what is to be expected based on the analysis of the data and the epidemiological models.

Over the past week the curve of Covid-19 infections in the United States has slightly flattened. Here are updated graphs of the actual data and new projections for what we can expect in the next few weeks.

Figure 1 shows the number of confirmed cases of Covid-19 in the United States as of 7 a.m. each day. These are the values posted by the European CDC at noon London time, and so they are slightly smaller than some other values that are reported later each day.

Figure 1: Number of confirmed Covid-19 cases in the United States

Multiple Authors
By: Donald J. Wheeler, Al Pfadt, Kathryn J. Whyte

Based on the professional literature available, there are some inconvenient truths about Covid-19 that are not always considered in the chorus of confusion that exists today. Here we summarize what is known, what has already happened, and what is to be expected based on the analysis of the data and the epidemiological models.


An analysis of the first 425 laboratory-identified cases of a novel coronavirus infected pneumonia (Covid-19) is presented by Qun Li, The first cases were identified at Wuhan hospitals as a "pneumonia of unknown etiology" when the patients met the following criteria: fever in excess of 100.4°F, radiographic evidence of pneumonia, low or normal white-cell count or low lymphocyte count, and no symptomatic improvement after antimicrobial treatment for 3 to 5 days according to standard clinical guidelines. On Jan. 7, 2020, the outbreak was confirmed as a new coronavirus infection2.

David Pride’s picture

By: David Pride

‘That escalated quickly!” is a common trope used in popular culture to describe when a situation gets out of hand before you’ve even had a chance to think about it. We don’t often use this trope in medicine, but I can think of nothing better to describe what has been going on in the United States with the coronavirus outbreak.

I am a physician scientist who practices infectious disease medicine and runs a research laboratory that specializes in viruses. I spend much of my time directing a clinical microbiology laboratory for a large academic medical center. If you’ve ever had a doctor tell you that they are going to test you for a virus, it’s teams like mine that develop and run that test.

When I first heard about the coronavirus outbreak in China, I had no idea I would soon be on the front lines of dealing with this outbreak.

Sriram Chandrasekaran’s picture

By: Sriram Chandrasekaran

Imagine you’re a fossil hunter. You spend months in the heat of Arizona digging up bones only to find that what you’ve uncovered is from a previously discovered dinosaur.

That’s how the search for antibiotics has panned out recently. The relatively few antibiotic hunters out there keep finding the same types of antibiotics.

With the rapid rise in drug resistance in many pathogens, new antibiotics are desperately needed. It may be only a matter of time before a wound or scratch becomes life-threatening. Yet few new antibiotics have entered the market of late, and even these are just minor variants of old antibiotics.

Although the prospects look bleak, the recent revolution in artificial intelligence (AI) offers new hope. In a study published in February 2020 in the journal Cell, scientists from MIT and Harvard used a type of AI called deep learning to discover new antibiotics.

Multiple Authors
By: Sheng Lin-Gibson, Vijay Srinivasan

Biopharmaceuticals, also known as biological drugs or biologics, are manufactured from living organisms, or contain living organisms that have been genetically engineered to prevent or treat diseases. Biologics are chemically and structurally complex, and often highly heterogeneous; therefore, controlling and maintaining quality remains a challenge. The potential for new therapeutics to cure and treat previously untreatable diseases is enormous, but there is still a long way to go before they can be manufactured at the required scale, with predictive control of quality, and at a lower cost. NIST’s Vijay Srinivasan and Sheng Lin-Gibson discuss their recent paper on some of the challenges and solutions associated with manufacturing these life-saving drugs.

Peter Dizikes’s picture

By: Peter Dizikes

Given the complexities of healthcare, do basic statistics used to rank hospitals really work well? A study co-authored by MIT economists indicates that some fundamental metrics do, in fact, provide real insight about hospital quality.

“The results suggest a substantial improvement in health if you go to a hospital where the quality scores are higher,” says Joseph Doyle, an MIT economist and co-author of a new paper detailing the study’s results.

The study was designed to work around a difficult problem in evaluating hospital quality: Some high-performing hospitals may receive an above-average number of very sick patients. Accepting those difficult cases could, on the surface, worsen the aggregate outcomes of a given hospital’s patients and make such hospitals seem less effective than they are.

However, the scholars found a way to study equivalent pools of patients, thus allowing them to judge the hospitals in level terms. Overall, the study shows, when patient sickness levels are accounted for, hospitals that score well on quality measures have 30-day readmission rates that are 15 percent lower than a set of lesser-rated hospitals, and 30-day mortality rates that are 17 percent lower.

Anne Trafton’s picture

By: Anne Trafton

After a patient has a heart attack or stroke, doctors often use risk models to help guide their treatment. These models can calculate a patient’s risk of dying based on factors such as the patient’s age, symptoms, and other characteristics.

While these models are useful in most cases, they do not make accurate predictions for many patients, which can lead doctors to choose ineffective or unnecessarily risky treatments for some patients.

“Every risk model is evaluated on some dataset of patients, and even if it has high accuracy, it is never 100-percent accurate in practice,” says Collin Stultz, a professor of electrical engineering and computer science at MIT and a cardiologist at Massachusetts General Hospital. “There are going to be some patients for which the model will get the wrong answer, and that can be disastrous.”

Stultz and his colleagues from MIT, IBM Research, and the University of Massachusetts Medical School have now developed a method that allows them to determine whether a particular model’s results can be trusted for a given patient. This could help guide doctors to choose better treatments for those patients, the researchers say.

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