Multiple Authors
By: Amber Dance, Knowable Magazine

This story was originally published by Knowable Magazine.

As Covid-19 cases fill the hospitals, among the sickest and most likely to die are those whose bodies react in a signature, catastrophic way. Immune cells flood and attack the lungs they should be protecting. Blood vessels leak; the blood itself clots. Blood pressure plummets, and organs start to fail.

Such cases, doctors and scientists increasingly believe, are due to an immune system gone overboard—so that it harms instead of helps.

Normally, when the human body encounters a germ, the immune system attacks the invader and then stands down. But sometimes, that orderly army of cells wielding molecular weapons gets out of control, morphing from obedient soldiers into an unruly, torch- and pitchfork-bearing mob. Though there are tests and treatments that could help to identify and tamp down this insurrection, it’s too early to be sure of the best course of therapy for those who are suffering a storm due to Covid-19.

Jay Arthur—The KnowWare Man’s picture

By: Jay Arthur—The KnowWare Man

Story update 5/6/2020: The charts and some data have been updated to reflect the data available on the date this article was published.

During the Covid-19 stay-at-home order in Colorado, I've become increasingly frustrated by Covid-19 charts. Most of what I see are cumulative column charts, which don't give any real insight into what's going on. Are we really flattening the curve?

So I decided to use the state's Covid-19 statistics for Colorado and Denver county, and see what I could learn using control charts. Control charts have been around for almost 100 years. They use formulas to calculate control limits that encompass 99.7 percent of the data points. This makes it easy to monitor any process and detect process shifts and "out of control" conditions.


Source: https://covid19.colorado.gov/case-data Click image for larger view.

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.

Background

An analysis of the first 425 laboratory-identified cases of a novel coronavirus infected pneumonia (Covid-19) is presented by Qun Li, et.al.1. 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.

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