You prick your finger or swab your nose and dab a tiny sample of the fluid onto a semiconductor chip. Slot that chip into an inexpensive, handheld reader and, within a minute or so, its small screen displays a list of results—you are negative for the new coronavirus, positive for antibodies. The likelihood of false results is extremely low. You are cleared to enter your company’s building or fly on a plane.
Since the outbreak of the COVID-19 pandemic, national and local governments have come to realize that although sheltering in place helps to “flatten the curve” of new cases, economic and other considerations mean we can’t stay home indefinitely. But if we are to regain some sense of normalcy, early detection of the novel coronavirus, SARS-CoV-2, will be essential to containing it—at least during the period between the easing of restrictions on gatherings and commercial activity and the development of a vaccine.
At this moment, the best diagnostic tests for SARS-CoV-2, based on real-time RT-PCR (rRT-PCR) assays, are sensitive, but they require expensive equipment and trained technicians. What’s more, there’s a relatively long turnaround time for getting an answer (at least 24 hours prep time after receipt of a sample, plus 6 hours to complete the actual test procedure).
Now a multidisciplinary group of researchers at the University of Minnesota, with expertise in magnetics, microbiology and electrical engineering, says it has developed a test that is relatively cheap, easy to use, and quick to deliver results. The group, led by Prof. Jian-Ping Wang, chair of the Department of Electrical & Computer Engineering, and Associate Professor Maxim C. Cheeran, from the Department of Veterinary Population Medicine, says it looks to have a commercially viable version of its prototype system available soon enough to help speed up the pace of pre-vaccine testing. IEEE Spectrum interviewed Wang, an IEEE Fellow, about his group’s portable testing system.
IEEE Spectrum: How would you describe your virus detection system in a sentence or two?
Wang: It’s a portable platform based on magnetic particle spectroscopy (MPS) that allows for rapid, sensitive detection of SARS-CoV-2 at the point of care. Eventually, it will be used for routine diagnosis in households.
Spectrum: Give us a quick primer on the science underlying the MPS testing technique.
Wang: MPS is a versatile platform for different bioassays that uses artificially designed magnetic nanoparticles that act as magnetic tracers when their surfaces are functionalized with test reagents such as antibodies, aptamers or peptides. Imagine them as tiny probes capturing target analytes from biofluid samples. For COVID-19 antigen detection, we functionalized our nanoparticles with polyclonal antibodies to two of the four structural proteins that are components of the coronavirus (nucleocapsid and spike proteins). The antibodies allow the nanoparticles to bind to epitopes, or receptor sites, on these particular proteins. The more binding that occurs, the greater the presence of the virus.
Spectrum: So, how does the MPS system answer the question “Does this person have coronavirus?”
Wang: The MPS platform’s job is to monitor and assess the real-time specific binding of the nanoparticles with these proteins. The quantity or concentration of the target analyte—in this case, the aforementioned proteins indicating the presence of coronavirus—directly affects the responses of the nanoparticles to the system’s magnetic field. The magnitude of the difference in their behavior before and after the addition of the biofluid sample tells the tale. Because the biological tissues and fluids are nonmagnetic, there is negligible magnetic background noise from the biological samples. As a result, this volumetric-based immunoassay tool not only uncomplicated, but also accurate and effective with minimal sample preparation.
Spectrum: Take us through the steps of a testing cycle using this device.
Wang: No problem. But let’s back up a bit and note that our group has developed the MagiCoil Android app that processes information from the device’s microcontroller in real-time. And crucially, it also guides users on how to conduct a test from start to finish. The user interacts with the MPS handheld device using their smart phone, which communicates with the device via Bluetooth. Testing results are securely transmitted to cloud storage and could be readily shared between a patient and clinicians.
To run a test, the user begins by inserting a testing vial into the device and waiting as the system collects a baseline signal for 10 seconds. Then the user adds a biofluid sample into vial and waits again as the antigen and antibody bind for 10 minutes. The system will automatically read the ending signal for 10 seconds, then display the results.
Spectrum: How did you know this would work?
Wang: This epitope binding of the nanoparticles and target analytes form viral protein clusters, similar to what occurred when we applied this technique to the detection of the H1N1 nucleoprotein reported in our previous work.
Spectrum: What makes your team sure there won’t be a shortage of testing vials containing the functionalized nanoparticles (a situation comparable to the current shortage of reagents for COVID testing)?
Wang: For each test we use only a microgram of nanoparticles and a nanogram of reagents (antibodies, RNA fragments). We are already collaborating with nanoparticle companies, who supply us with high quality iron nitride nanoparticles for this application. Our group has also been seeking collaborations with biotechnology companies to secure sources of chemical reagents.
Spectrum: What challenges has your team faced and how did you overcome them?
Wang: Over the past decade, there was very little attention paid to filling the need in the market for a low noise, easy-to-use, portable bioassay kit for the detection of viruses—not until we began working on detecting Influenza A Virus subtype H1N1 in past year. We have steadily grown the team, accumulated experience in this research area, and optimized the MPS platform. In addition to full-time researchers, the MPS and MagiCoil team has benefited from the efforts of many talented graduate and undergraduate students from different areas. This work has resulted in the current version of the MPS device and the accompanying app.
I feel proud of my students for forging ahead on a project because they see it as an important bit of research and never giving up. Since the outbreak of COVID-19 pandemic, we saw how critical it was for us to speed up work on our project so we could contribute to the fight against the virus by making our MPS device available as soon as possible.
Spectrum: Your team’s aim has been to get this in doctors’ offices so anyone could walk in, get tested, and walk out knowing whether they’ve contracted the coronavirus. But in places like New York City, people have been urged to stay away from hospitals and clinics unless they are experiencing acute symptoms. How important is it to go beyond the clinical setting to household use so even asymptomatic people know their status?
Wang: From the outset, we wanted to make it inexpensive and easy to use so untrained people could conduct tests at home or out in the field in remote areas that are the antithesis of clinical situations. It will let large portions of the population afford to get regular updates on whether they have contracted the virus. And because it is capable of transmitting test results collected from distant locations to centrally located data analysis units, governments can have real-time epidemiological data at their fingertips. This would also significantly reduce the costs associated with tracking the spread of a disease and help health authorities more quickly evaluate and refine their disease control protocols.
Spectrum: How long before the system is commercially available?
Wang: We have just transformed the benchtop version of the system into a handheld version [which, at 212- by-84-by-72-millimeters is about the size of an old-school brick cellphone]. We have carried out preliminary tests such as characterizing the minimum amount of magnetic nanoparticles detectable and the system’s overall antigen sensitivity. And we’re still homing in on the optimum concentration of antibody to be functionalized on the nanoparticles so they’ll be most effective.
We anticipate that clinical trials will take an additional 3 to 5 months. At that point, we will work with local companies in Minnesota to mass produce MPS devices. The University of Minnesota Office for Technology Commercialization has been helping us lay the groundwork for founding a startup company in order to accelerate the process of commercializing this handheld device the instant we receive the necessary government approvals.
Spectrum: You mentioned antigen sensitivity. How much virus must there be in a sample for the MPS system to detect it?
Wang: We are currently evaluating what is the lowest concentration of virus our test can detect. But based on our experience with the H1N1 flu virus, it will be less than 150 virus particles.
Spectrum: I know it’s hard to say definitively, but give us a ballpark figure for the eventual price of the MPS testing system.
Wang: Based on our first prototype MSP device, we foresee the unit price starting at roughly US $100; the MagiCoil app, which is already completed and available for download from the Google Play store, is free. Testing vials containing the functionalized magnetic nanoparticles targeting the coronavirus will cost between $2 and $5 each.
Eventually, we plan to make a second-generation MPS device that’s as small as today’s smart phones. That step will require expertise in the areas of microfluid channel design, printed microcoils, high moment magnetic nanoparticles, automatic biofluid sample loading and filtering, optimized circuit layouts, etc. We are open to collaborations with other groups (including, but certainly not limited to IEEE members) to make a better, lighter weight, sensitive, fully automatic MPS device.
As we write these words, several billion people, the majority of the world’s population, are confined to their homes or subject to physical-distancing policies in an attempt to contain one of the worst pandemics of modern times. Economic activity has plummeted, countless people are out of work, and entire industries have ground to a halt.
Quite understandably, a couple of questions are on everyone’s mind: What is the exit strategy? How will we know when it’s safe to implement it?
Around the globe, epidemiologists, statisticians, biologists, and health officials are grappling with these questions. Though engineering perspectives are uncommon in epidemiological modeling, we believe that in this case public officials could greatly benefit from one. Of course, the COVID-19 pandemic isn’t an obvious or typical engineering problem. But in its basic behavior it is an unstable, open-loop system. Left alone, it grows exponentially, as we have all been told repeatedly. However, there’s good news, too: Like many such systems, it can be stabilized effectively and efficiently by applying the principles of control theory, most notably the use of feedback.
Inspired by the important work of epidemiologists and others on the front lines of this global crisis, we have explored how feedback can help stabilize and diminish the rate of propagation of this deadly virus that now literally plagues us. We’ve drawn on proven engineering principles to come up with an approach that would offer policymakers concrete guidance, one that takes into account both medical and socioeconomic considerations. We relied on feedback-based mechanisms to devise a system that would bring the outbreak under control and then adeptly manage the longer-term caseload.
It is during this longer-term phase, the inevitable relaxing of physical distancing that is required for a functioning society, that the strengths of a response grounded in control theory are most crucial. Using one of the widely available computer models of the disease, we tested our proposal and found that it could help officials manage the enormous complexity of trade-offs and unknowns that they will face, while saving perhaps hundreds of thousands of lives.
Our goal here is to share some of our key findings and to engage a community of control experts in this vital and fascinating problem. Together, we can contribute vitally to the international efforts to manage this outbreak.
The COVID-19 pandemic is unlike any other recent disease outbreak for several reasons. One is that its basic reproduction number, or R0 (“R naught”), is relatively high. R0 is an indication of how many people, on average, an infected person will infect during the course of her illness. R0 is not a fixed number, depending as it does on such factors as the density of a community, the general health of its populace, its medical infrastructure and resources, and countless details of the community’s response. But a commonly cited R0 figure for ordinary seasonal influenza is 1.3, whereas a figure calculated for the experience in Wuhan, China, where COVID-19 is understood to have originated, is 2.6 [PDF]. Figures for some outbreaks in Italy range from 2.76 to 3.25 [PDF].
The goal of infectious-disease intervention is reducing the R0 to below 1, because such a value means that new infections are in decline and will eventually reach zero. But with the COVID-19 outbreak, the level of urgency is extraordinarily high due to the disease’s relatively high fatality rate. Fatality rates, too, are quite variable and depend on such factors as age, physical fitness, present pathologies, region, and access to health care. But in general they are much higher for COVID-19 than for ordinary influenza. A surprisingly large percentage of people who contract the disease develop a form of viral pneumonia that sometimes proves fatal. Many of those patients require artificial ventilation, and if their number exceeds the capacity of intensive care units to accommodate them, some number of them, perhaps a majority, will die.
For that reason, enormous worldwide efforts have focused on “flattening the curve” of infections against time. A high, sharp curve indicating a surge of infections in a short time period, as occurred in China, Italy, Spain, and elsewhere, means that the number of serious cases will swamp the ability of hospitals to treat them and result in mass fatalities. So to reduce the peak demand on health care, the first priority must be to bring the caseload under control. Once that’s done, the emphasis shifts to managing a long-term return to normalcy while minimizing both death rates and economic impact.
The two basic approaches to controlling the spread of disease are mitigation, which focuses on slowing but not necessarily stopping the spread, and suppression, which aims to reverse epidemic growth. For mitigation, R0 is reduced but remains greater than 1, while for suppression, R0 is smaller than 1. Both obviously require changing R0. Officials accomplish that by introducing social measures such as restricted travel, home confinement, social distancing, and so on. These restrictions are referred to as nonpharmaceutical interventions, or NPIs. What we are proposing is a systematically designed strategy, based on feedback, to change R0 through modulation of NPIs. In effect, the strategy alternates between suppression and mitigation in order to maintain the spread at a desired level.
It may sound straightforward, but there are many challenges. Some of them arise from the fact that COVID-19 is a very peculiar disease. Despite enormous efforts to characterize the virus, biologists still do not understand why some people experience fairly mild symptoms while others spiral into a massive, uncontrolled immune response and death. And no one can explain why, among fatalities, men predominate. Other mysteries include the disease’s long incubation period—up to 14 days between infection and symptoms—and even the question of whether a person can get re-infected.
These perplexities have helped bog down efforts to deal with the pandemic. As a recent Imperial College research paper [PDF] notes: “There are very large uncertainties around the transmission of this virus, the likely effectiveness of different policies, and the extent to which the population spontaneously adopts risk reducing behaviours.” Consider the long incubation time and apparent spreading of the virus before symptoms are experienced. These undoubtedly contributed to the relatively high R0 values, because people who were infectious continued to interact with others and transmitted the virus without being aware that they were doing so.
This lag before the onset of symptoms corresponds to time delay in control-system theory. It is notorious for introducing oscillations into closed-loop systems, particularly when combined with substantial uncertainty in the model itself.
In addition to delays, there are very significant uncertainties. Testing, for example, has been spotty in some countries, and that inconsistency has obscured the number of actual cases. That, in turn, made it impossible for officials to know the true level of contagion. Even NPIs are not immutable. The extent to which the public is complying with policies is never 100 percent and may not even be knowable with a high degree of accuracy; people may follow directives less strictly over time. Also, health care capacity can go up because of an increase in available beds due to capacity additions, or down because of a decrease due to a natural disaster.
The point is, a pandemic is a dynamic, fast-moving situation, and inadequate local attempts to monitor and control it can be disastrous. In the Spanish flu pandemic of 1918, cities took widely varying approaches to the lockdown and release of their citizens, with wildly varying results. Some recovered straightforwardly, others had rebound spikes larger than the initial outbreak, and still others had multiple outbreaks after the initial lockdown.
A commonly cited proposal for relaxing social-distancing measures is an on-off approach, where some restrictions are lifted when the number of new cases requiring intensive care is below a threshold and are put back into place when it exceeds a certain number. The research paper [PDF] by the Imperial College COVID-19 Response Team showed how such a strategy is “robust to uncertainty in both the reproduction number, R0, and in the severity of the virus” and offers “greater robustness to uncertainty than fixed duration interventions and can be adapted for regional use.”
This on-off approach is an example of the use of feedback, where the feedback variable is the number of cases in hospital intensive care units. A major drawback of this type of on-off control is that it can lead to oscillations—which, if this strategy is too aggressive, may overwhelm the capacity of the health care system to treat serious cases.
A major advantage of feedback here is that it lessens the impact of model uncertainty—meaning that if carefully designed, a strategy can be effective even if the models it is based on are not accurate. We do not yet have an accurate epidemiological model of COVID-19 and will likely not have one for at least several months, if at all. Furthermore, the physical distancing and confinement regimes that have been put into place are new, so we don’t really know yet exactly how effective they’ll be or even the extent to which people are complying with them.
In the absence of widespread immunity or vaccination, the only way to suppress the disease is total confinement—obviously not a viable long-term solution. A reasonable middle ground is to implement a feedback policy designed to keep R0 close to 1, with perhaps small oscillations on either side. In so doing we would maintain the critical caseload within the capacity of health care institutions while slowly and safely building immunity in our communities, and returning to normal social and economic conditions as quickly as is safely possible.
A key point is that the design of the policy be rigorous from a control-engineering viewpoint while remaining comprehensible to epidemiologists, policymakers, and others without deep knowledge of control theory. It should also be capable of generating restriction regimes that can be translated into practical public policies. If the tuning mechanism is too aggressive—for example, switching between full and zero social distancing—it will lead to severe oscillations and overwhelmed hospitals, and very likely frustration and social-distancing fatigue among the people who need to follow dramatically changing rules.
On the other hand, tuning that is too timid also courts fiasco. An example of such tuning might be a policy requiring a full month in which no new cases are recorded before officials relax restrictions. Such a hypercautious approach risks needlessly prolonging the pandemic’s economic devastation, creating a catastrophe of a different sort.
But a properly designed feedback-based policy that takes into account both dynamics and uncertainty can deliver a stable result while keeping the hospitalization rate within a desired approximate range. Furthermore, keeping the rate within such a range for a prolonged period allows a society to slowly and safely increase the percentage of people who have some sort of antibodies to the disease because they have either suffered it or they have been vaccinated—preferably the latter.
Eventually, as the percentage of the population who have suffered the disease and recovered from it becomes high enough, the number of susceptible people becomes small enough that the virus’s rate of spread is naturally lowered. This phenomenon is called herd immunity, and it is how pandemics have generally died out in the past. However, the question of whether or how such immunity to COVID-19 can be built up is still under investigation, which makes it particularly important to monitor and manage nonpharmaceutical interventions as a function of the actual spread of the virus and of hospitalization rates.
In designing our control system, we relied strongly on the fact that most nonpharmaceutical interventions are not binary, on-off quantities but instead can take on a range of values and can be implemented that way by policymakers. For example, stay-at-home directives can be applied disproportionately to specific groups of people who are particularly at risk because of age or a preexisting health condition. Then the number of people who are affected by the directive can be increased or decreased by simply changing the guidelines on who is “at risk.” We can similarly widen or narrow the definition of people who are designated “essential” and are therefore exempt from the directives. Public meetings, too, may be banned for n participants, where the value of n can be increased as things loosen up.
Restrictions on travel, too, can be quite variable. Full lockdown limits people to moving within the boundaries of their property. But as conditions improve, officials might grant access to businesses within a couple, and then perhaps a dozen, kilometers of their home, and so on. These are all obviously important levers.
To explore how such variability can save lives, we devised a series of scenarios, each indicative of a recovery strategy with a different level of feedback, and simulated the resulting policies against a commonly used infectious-disease computer model. We plotted the results in a series of graphs showing COVID-19 hospital cases as a function of time. Hospital occupancy may be a more reliable and tangible measure than total case count, which depends on extensive testing that many countries (such as the United States) do not have at the moment. Furthermore, hospital ICU bed occupancy or ventilator availability is arguably an important measure of the ability of the local health care system to treat those who are suffering from respiratory distress acute enough to require intensive care and perhaps assisted breathing.
The model we used was created by Jeffrey Kantor, professor of chemical engineering at the University of Notre Dame (Kantor’s model is available on GitHub). The model assumes we can suppress disease transmission to a very low level by choosing appropriate policy levers. Although worldwide experience with COVID-19 is still limited, at the time of this writing this assumption appears to be a realistic one.
To make the model more reflective of our current understanding of COVID-19, we added two types of uncertainty. We assumed different values of R0 to see how they affected outcomes. To consider how noncompliance with nonpharmaceutical interventions would affect results, we programmed for a range of effectiveness of these NPIs.
Our first, simplest simulation confirms what we all know by now, which is that not doing anything was not an option [Figure 1].
The large and lengthy peak well above the available bed capacity in the intensive care unit indicates a huge number of cases that will likely result in death. This is why, of course, most countries have put aggressive measures in place to flatten the curve.
So what do we do when the number of infections comes down? As this second simulation clearly shows [Figure 2], relaxing all restrictions when the number of infections has come down will only lead to a second surge in infections. Not only could this second surge overwhelm our hospitals, it could also lead to an even higher mortality rate than the first surge, as occurred repeatedly in several U.S. cities during the Spanish flu epidemic of 1918.
Now let’s consider the simple on-off approach to confinement, in which most of the usual restrictions on gatherings, travel, and social interaction are lifted entirely when the number of new ICU cases drops below a lower threshold, and then are put back into place when this number exceeds a higher threshold. In this case, the R0 swings sharply between two levels, a high above 2 and a low below 1, as shown in blue in the graph [Figure 3]. This approach leads to oscillations, and if it is applied too aggressively, the high points of these oscillations will exceed the health care system’s capacity to treat patients. Another likely problem with this approach has been labeled “social distance fatigue.” People become weary of the repeated changes to their routine—going back to work for a couple of weeks, then being told to stay at home for a few weeks, then given the all-clear to go back to work, and so on.
We can do better. For our third experiment, we developed a scenario in which we targeted 90 percent occupancy of hospital intensive care units. To achieve this, we designed a simple feedback-based policy using the principles of control systems theory.
When R0 is high, many restrictions are put into place. People are largely confined to their homes and services are limited to the bare minimum needed for society to function—utilities, police, sanitation, and food distribution, for example. Then, as conditions begin to improve, as revealed by our feedback measure of hospital-bed occupancy, other services are gradually phased in. Recovered people are allowed to move freely as they can no longer contract, or transmit, the virus. Perhaps people are allowed to visit restaurants within walking distance, some small businesses are allowed to reopen under certain conditions, or certain age groups are subject to less-stringent restrictions. Then geographical mobility might be loosened in other ways. The point is that restrictions are eased gradually, with each new gradation based carefully on feedback.
This strategy results in a stable response that maximizes the rate of recovery. Furthermore, the demand for hospital ICU beds never exceeds a threshold, thanks to a “set point” target below that threshold [Figure 4]. The health care capacity limit is never breached. In addition, note the general upward trend for the release of restrictions, as the number of recovered and immune people grows and nonpharmaceutical interventions are gradually phased out.
This simplified example shows how using feedback to modulate the restrictions imposed on a population to modify R0 leads to a policy that is robust. For example, early on in the outbreak, there will be a great deal of uncertainty about R0 because testing will still be spotty, and because an unknown number of people may have the disease without realizing it. That uncertainty will inevitably fuel a surge in initial cases. However, once the case count is stabilized by the initial restrictive regime, a policy based on feedback will prove very tolerant of variations in R0, as illustrated in Figure 5. As the graph shows, after a few months it doesn’t matter whether the R0 is 2 or 2.6 because the total case count stays well below the number of available hospital beds due to the use of feedback.
As an added benefit, using feedback makes the policy effective even in the face of likely degrees of noncompliance. In practice, what noncompliance means is that a given level of restrictions will result in an R0 that is slightly higher than expected, which in turn causes fluctuations in the number of people who are infected. Noncompliance might, for instance, result in the restrictions being 10 percent less effective than intended [Figure 6]. However, through feedback, the policy will automatically tighten to compensate.
In reality, various factors that the model treats as invariable, such as health care capacity, might be anything but. However, variations of this sort can typically be accommodated in a policy, for example by changing the threshold on ICU occupancy.
Clearly, tried-and-true principles of control theory, particularly feedback, can help officials plot more robust and optimal strategies as they attempt to deal with the devastating COVID-19 pandemic. But how to make officials aware of these powerful tools?
Imagine an online interactive tool offering detailed, specific guidance in plain language and aimed at public officials and others charged with mounting a response to the pandemic in their communities. The guidance would be based on strategies developed by a small group of control theorists, epidemiologists, and people with policy experience. The site could review the now-familiar initial response, in which nonessential workers are confined to their homes except for essential needs. Then the site could go on to give some guidance on how and when the tightest restrictions could be lifted.
The biggest challenge to the designers of this Web-based tool will be enabling nonspecialists to visualize how the various components of the epidemiological model interact with the various feedback policy options and model uncertainty. How exactly should the main feedback measure—likely some aspect of hospital or intensive care occupancy—be implemented? Which restrictions should be lifted in the first round of easing? How should they be eased in the first round? In the second round? While monitoring the feedback measure, how frequently should officials consider whether to implement another round of easing? Feedback will help officials determine when to time various phases of interventions. An interactive tool that could assess different policy approaches, illustrating what conditions must be in place to alleviate uncertainty and shrink the projected caseload, would be very valuable indeed.
Working with political officials, epidemiologists, and others, control engineers can systematically design policies that take these constraints and trade-offs into account. It comes down to this: In the many months of struggle ahead, such a collaboration could save countless lives.
Greg Stewart is vice president of data science for the agriculture technology startup Ecoation and an adjunct professor at the University of British Columbia, in Vancouver. A Fellow of the IEEE, he has led the research, development, and deployment of control and machine-learning technology in such applications as microalgae cultivation, large-scale data centers, automotive power-train control, and semiconductor fabrication. He is currently developing models and strategies for controlling the spread of pests and disease in agriculture.
Klaske van Heusden is a research associate at the University of British Columbia in Vancouver. An IEEE Senior Member, her research interests include modeling, prediction, and control, with applications in medical devices, mechatronics, and robotics. Lately she has been working on a robust and provably safe automated drug-delivery device for use in operating rooms.
Guy A. Dumont is a professor of electrical and computer engineering at the University of British Columbia in Vancouver and a principal investigator at BC Children’s Hospital Research Institute. An IEEE Fellow, he has 40 years of experience applying advanced control theory in the process industries, in particular pulp and paper and, for the last 20 years, in biomedical applications such as automated drug delivery for closed-loop control of anesthesia.
Global regulators have largely stepped out of diagnostics manufacturers’ way to enable them to quickly bring COVID-19 tests to the public. That has led to a deluge of testing options on the market, and in many cases, the reliability and accuracy of these tests is unclear.
That led us to wonder: Is anyone testing the tests?
We found one organization that’s on it. The Foundation for Innovative New Diagnostics, or FIND, headquartered in Geneva, Switzerland, is evaluating its way through a list of over 300 COVID-19 tests manufactured globally, and today published its first results.
FIND’s effort, which it is undertaking in collaboration with the World Health Organization (WHO), involves running thousands of coronavirus samples through the tests and comparing their performance against a gold standard. The organization will rank the tests based on sensitivity.
In late March, the FDA approved the use of Cepheid’s GeneXpert rapid molecular diagnostic machines to test for the new coronavirus. The automated modules—5000 of which are already installed in U.S. health facilities, while 18,000 are in operation in other countries—don’t require a lab facility or special training to operate. What’s more, they generate accurate results in about 45 minutes. The modules use disposable cartridges, pre-filled with the required chemicals that are channeled around test chambers using microfluidics.
While the cartridges had to be adapted to test for COVID-19, the microfluidic system itself dates back to the late 1990s, when Cepheid was cofounded in Silicon Valley by IEEE Fellow Kurt Petersen, a MEMS pioneer and winner of the 2019 IEEE Medal of Honor. (Cepheid went public in 2000 and Danaher acquired it in 2016.)
The system was just a prototype in September 2001, when letters containing anthrax spores began to arrive at the offices of U.S. senators and journalists. Cepheid won a set of competitions held by the U.S. Postal Service aimed at helping it prevent anthrax from getting into the mail system. To this day, Cepheid systems, attached to mail sorting machines, screen most mail in the U.S.
Since then, the company’s GeneXpert has been adapted to test for flu, strep, norovirus, chlamydia, tuberculosis, MRSA—and now, COVID-19.
Says Petersen: “The technology we developed is very powerful, because you can have all different kinds of cartridges for the instrument. Each cartridge can have a different design of its microfluidics and hold different chemicals, creating a PCR (polymerase chain reaction) specific to any DNA sequence you want, and so sensitive that you can detect just a few segments of that DNA in a milliliter of liquid.”
The GeneXpert test, like most COVID-19 tests to date, starts with a nasal sample taken with a swab. The person collecting the sample drops the swab into a liquid-filled specimen transfer tube. To start the test, liquid containing the sample is pipetted into a disposable test cartridge, and the cartridge is inserted into the test machine; this takes no special training. After this, the process is automatic.
Petersen explains how it works:
The test cartridge contains microfluidic channels; these are made out of plastic using high-precision injection molding. All the chemicals needed for the process are stored in chambers within the system. In the center of the cartridge, a rotary valve turns to open different pathways, while a tiny plunger—like a syringe–moves fluids in and out as needed.
So, the plunger pulls the sample into the center, the valve rotates, and the plunger pushes it into another region of the cartridge to do an operation on it.The system can do that multiple times, moving the sample to different regions with different chemicals, extracting RNA, mixing it with the reverse transcriptase that synthesizes complementary DNA that matches the RNA, and eventually pushing it into PCR reaction tube, where rapid heating and cooling speeds up the process of copying the DNA. Each new copy of the DNA gets a fluorescent molecule attached, which allows an optical system to determine whether or not the targeted gene sequence is in the sample.
Petersen, now an angel investor, says he’s gratified that a technology he worked to develop is being used to help address the pandemic. “The instrument hasn’t changed that much,” he says; “it’s pretty much what we designed 20 years ago.”
While Cepheid’s test for COVID-19 was the first approved in the U.S., Abbott has also received FDA approval for a five-minute test that runs on its ID Now analyzers. Roughly 18,000 of those are installed in U.S. healthcare facilities. According to a recent study on flu virus identification that compared the Cepheid and Abbott systems along with a similar technology from Roche, “the Cepheid test showed the best performance,” and was generally more sensitive. Such comparisons with respect to identifying COVID-19 are not yet available.
Months into the COVID-19 pandemic, the United States has finally moved from relying entirely on a single, flawed diagnostic test to having what may soon be an onslaught of testing options available from private entities. The U.S. Food and Drug Administration over the last three weeks has authorized the emergency use of more than 20 diagnostic tests for the novel coronavirus known as SARS-CoV-2.
Here’s something that sounds decidedly useful right now: A home test kit that anyone could use to see if they have the coronavirus. A kit that’s nearly as easy to use as a pregnancy test, and that would give results in half an hour.
It doesn’t exist yet, but serial biotech entrepreneur Jonathan Rothberg is working on it.
At Brigham and Women’s Hospital in Boston, a potential COVID-19 patient can now drive in to the ambulance bay, roll down their window, and ask staff to swab their nose and throat.
Those swabs will be sent to a state lab for a real-time PCR test, which amplifies any viral genetic material so it can be compared to the new coronavirus, SARS-CoV-2. But this standard test must be carried out in a certified laboratory with trained technicians, takes 3 to 4 days to deliver results, and produces some false negatives.
Publicly available skin cancer detection apps, such as SkinVision, use AI-based analysis to determine if a new or changing mole is a source of concern or nothing to worry about. Yet according to a new analysis of the scientific evidence behind those apps, there’s a lot to worry about.
The U.S. Department of Defense has put out a call to researchers to develop devices that detect pathogenic threats by performing up to 1,000 diagnostic tests in fewer than 15 minutes.
The devices ideally would determine the presence of a pathogen, and useful details about it, such as whether it’s a drug resistant variety, the severity of the infection, and any co-infections.
The Defense Advanced Research Projects Agency, or DARPA, which makes investments in breakthrough technologies on the military’s behalf, will oversee the initiative.
Scientists have endeavored to make this sort of diagnose-anything, “Star Trek”-inspired device previously, without a lot of success. But DARPA is betting that new technology that leverages gene-editing techniques can achieve this goal.
If the project budget is approved by the White House, DARPA will be able to award more than US $60 million to proposers. An information session will be held in Atlanta on 11 December. DARPA program manager Renee Wegrzyn spoke with IEEE Spectrum about her vision for the these powerful devices.
For the roughly 50 million people worldwide with epilepsy, the exchange of electrical signals between cells in their brain can sometimes go haywire and cause a seizure—often with little to no warning. Two researchers at the University of Louisiana at Lafayette have developed a new AI-powered model that can predict the occurrence of seizures up to one hour before onset with 99.6 percent accuracy.
“Due to unexpected seizure times, epilepsy has a strong psychological and social effect on patients,” explains Hisham Daoud, a researcher who co-developed the new model.
Detecting seizures ahead of time could greatly improve the quality of life for patients with epilepsy and provide them with enough time to take action, he says. Notably, seizures are controllable with medication in up to 70 percent of these patients.
During the Framingham Heart Study, a long-term research study initiated in 1948 that collected health data from thousands of people, researchers discovered that high cholesterol and elevated blood pressure increase one’s risk of heart disease. Thanks to that insight, at-risk individuals can reduce their chances of developing the condition by taking drugs to lower cholesterol and blood pressure.
Since then, HealthMap’s case counts have lined up closely to that of the feds at the U.S. Centers for Disease Control and Prevention (CDC). In its most recent update, which was based on data collected through 8 October, the agency reported 1,299 confirmed and probable cases of the lung illness; HealthMap counted 1,305 up to the same date.
The accuracy of HealthMap suggests that such web-based tools are a viable addition to traditional surveillance methods. “We see it not as a replacement [to traditional warning systems], but as a supplement,” says Yulin Hswen, a research fellow at Boston Children’s Hospital, Harvard Medical School. “It gives you a more comprehensive picture of everything that’s going on, and in real time,” she says.
A smartphone app that monitors personal photos can spot eye diseases more than a year before doctors do, according to a new report published today in the journal Science Advances.
Using machine learning, the app searches casual portraits for signs of leukocoria: the appearance of a white reflection in the pupil of the eye. Leukocoria, or “white eye,” looks similar to red eye—that creepy red reflection in the eye that often appears with flash photography. But a red reflection is actually a sign of a healthy eye. A white reflection can be a sign of a problem.
White eye can indicate retinoblastoma, a type of childhood cancer of the retina, or a handful of other eye disorders, including retinopathy of prematurity, cataracts, or Coats Disease. Catching these disorders early can save an eye, or a life.
“With retinoblastoma, every month counts,” says Bryan Shaw, an associate professor at Baylor University in Waco, Texas. “Tumors grow rapidly and when you start seeing the white eye, you have about six months to a year before the tumor starts to break up and metastasize down the optic nerve to the brain and kills you.”
Have you ever needed an IV and had to undergo multiple pricks before the nurse could find a vein? Technology to avoid that painful trial and error is in the works. Fujifilm’s ultrasound diagnostics arm SonoSite announced yesterday that it had partnered with a startup company to develop artificial intelligence that can interpret ultrasound images on a mobile phone.
The companies say the first target for their AI-enabled ultrasound will be finding veins for IV (intravenous) needle insertion. The technology would enable technicians to hold a simple ultrasound wand over the skin while software on a connected mobile device locates the vein for them.
For this project, Fujifilm SonoSite tapped the Allen Institute for Artificial Intelligence (AI2), which has an incubator for AI startup companies. “Not only do we have to come up with a very accurate model to analyze the ultrasound videos, but on top of that, we have to make sure the model is working effectively on the limited resources of an android tablet or phone,” says Vu Ha, technical director of the AI2 Incubator.
In an interview with IEEE Spectrum, Ha did not disclose the name of the startup that will be taking on the task, saying the fledgling company is still in “stealth mode.”
Ha says the AI2 startup will take on the project in two stages: First, it’ll train a model on ultrasound images without any resource constraints, with the purpose of making it as accurate as possible. Then, the startup will go through a sequence of experiments to simplify the model by reducing the number of hidden layers in the network, and by trimming and compressing the network until it is simple enough to operate on a mobile phone.
The trick will be to shrink the model without sacrificing too much accuracy, Ha says.
If successful, the device could help clinicians reduce the number of unsuccessful attempts at finding a vein, and enable less trained technicians to start IVs as well. Hospitals that do a large volume of IVs often have highly trained staff capable of eyeballing ultrasound videos and using those images to help them to find small blood vessels. But the number of these highly trained clinicians is very small, says Ha.
“My hope is that with this technology, a less trained person will be able to find veins more reliably” using ultrasound, he says. That could broaden the availability of portable ultrasound to rural and resource-poor areas.
But the adoption of these devices has been relatively slow. As Eric Topol, director of the Scripps Research Translational Institute, told Spectrum recently, the smartphone ultrasound is a “brilliant engineering advance” that’s “hardly used at all” in the health care system. Complex challenges such as reimbursement, training, and the old habits of clinicians often hinder the uptake of new gadgets, despite engineers’ best efforts.
Machine learning algorithms that combine clinical and molecular data are the “wave of the future,” experts say
A man walks into a doctor’s office for a CT scan of his gallbladder. The gallbladder is fine but the doctor notices a saclike pocket of fluid on the man’s pancreas. It’s a cyst that may lead to cancer, the doctor tells him, so I’ll need to cut it out to be safe.
It’ll take three months to recover from the surgery, the doctor adds—plus, there’s a 50 percent chance of surgical complications, and a 5 percent chance the man will die on the table.
An estimated 800,000 patients in the United States are incidentally diagnosed with pancreatic cysts each year, and doctors have no good way of telling which cysts harbor a deadly form of cancer and which are benign. This ambiguity results in thousands of unnecessary surgeries: One study found that up to 78 percent of cysts for which a patient was referred to surgery ended up being not cancerous.
Now there’s a machine learning algorithm that could help. Described today in the journal Science Translational Medicine, surgeons and computer scientists at Johns Hopkins University have built a test called CompCyst (for comprehensive cyst analysis) that is significantly better than today’s standard-of-care—a.k.a. doctor observations and medical imaging—at predicting whether patients should be sent home, monitored, or undergo surgery.
This AI system detects unique gasping sounds that occur when the heart stops beating
When a person’s heart malfunctions and suddenly stops beating, death can occur within minutes—unless someone intervenes. A bystander administering CPR right away can triple a person’s chances of surviving a cardiac arrest.
Last July, we described a smart watch designed to detect cardiac arrest and summon help. Now, a team at the University of Washington has developed a totally contactless AI system that listens to detect the telltale sound of agonal breathing—a unique guttural gasping sound made by 50 percent of cardiac arrest patients.
The smart speaker system, described today in the journal npj Digital Medicine, detected agonal breathing events 97 percent of the time with almost no false alarms in a proof-of-concept study.
The team imagines using the tool—which can run on Amazon’s Alexa or Google Home, among other devices—to passively monitor bedrooms for the sound of agonal breathing and, if detected, set off an alarm.
The first study of a new treatment in humans demonstrates a noninvasive, harmless cancer killer
Tumor cells that spread cancer via the bloodstream face a new foe: a laser beam, shined from outside the skin, that finds and kills these metastatic little demons on the spot.
In a study published today in Science Translational Medicine, researchers revealed that their system accurately detected these cells in 27 out of 28 people with cancer, with a sensitivity that is about 1,000 times better than current technology. That’s an achievement in itself, but the research team was also able to kill a high percentage of the cancer-spreading cells, in real time, as they raced through the veins of the participants.
If developed further, the tool could give doctors a harmless, noninvasive, and thorough way to hunt and destroy such cells before those cells can form new tumors in the body. “This technology has the potential to significantly inhibit metastasis progression,” says Vladimir Zharov, director of the nanomedicine center at the University of Arkansas for Medical Sciences, who led the research.
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