During the COVID-19 pandemic, words and phrases that have typically been limited to epidemiologists and public health professionals have entered the public sphere. Although we’ve rapidly accepted epidemiology-based news, the public hasn’t been given the chance to fully absorb what all these terms really mean.

As with all disease tests, a false positive result on a COVID-19 test can cause undue stress on individuals as they try to navigate their diagnosis, take days off work and isolate from family. One high-profile example was Ohio Governor Mike DeWine whose false positive result led him to cancel a meeting with President Donald Trump.

False negative test results are even more dangerous, as people may think it is safe and appropriate for them to engage in social activities. Of course, factors such as the type of test, whether the individual had symptoms before being tested and the timing of the test can also impact how well the test predicts whether someone is infected.

Sensitivity and specificity are two extremely important scientific concepts for understanding the results of COVID-19 tests.

In the epidemiological context, sensitivity is the proportion of true positives that are correctly identified. If 100 people have a disease, and the test identifies 90 of these people as having the disease, the sensitivity of the test is 90 per cent.

Specificity is the ability of a test to correctly identify those without the disease. If 100 people don’t have the disease, and the test correctly identifies 90 people as disease-free, the test has a specificity of 90 per cent.

This simple table helps outline how sensitivity and specificity are calculated when the prevalence — the percentage of the population that actually has the disease — is 25 per cent (totals in bold):

A test sensitivity of 80 per cent can seem great for a newly released test (like for the made-up case numbers I reported above).

Predictive value

But these numbers don’t convey the whole message. The usefulness of a test in a population is not determined by its sensitivity and specificity. When we use sensitivity and specificity, we are figuring out how well a test works when we already know which people do, and don’t, have the disease.

But the true value of a test in a real-world setting comes from its ability to correctly predict who is infected and who is not. This makes sense because in a real-world setting, we don’t know who truly has the disease — we rely on the test itself to tell us. We use the positive predictive value and negative predictive value of a test to summarize that test’s predictive ability.

To drive the point home, think about this: in a population in which no one has the disease, even a test that is terrible at detecting anyone with the disease will appear to work great. It will “correctly” identify most people as not having the disease. This has more to do with how many people have the disease in a population (prevalence) rather than how well the test works.

Using the same numbers as above, we can estimate the positive predictive value (PPV) and negative predictive value (NPV), but this time we focus on the row totals (in bold).

The PPV is calculated as the number of true positives divided by the total number of people identified as positive by the test.

The PPV is interpreted as the probability that someone that has tested positive actually has the disease. The NPV is the probability that someone that tested negative does not have the disease. Although sensitivity and specificity do not change as the proportion of diseased individuals changes in a population, the PPV and NPV are heavily dependent on the prevalence.

Let’s see what happens when we redraw our disease table when the population prevalence sits at one per cent instead of 25 per cent (much closer to the true prevalence of COVID-19 in Canada).

So, when the disease has low prevalence, the PPV of the test can be very low. This means that the probability that someone that tested positive actually has COVID-19 is low. Of course, depending on the sensitivity, specificity and the prevalence in the population, the reverse can be true as well: someone that tested negative might not truly be disease-free.

False positive and false negative tests in real life

What does this mean as mass testing begins for COVID-19? At the very least it means the public should have clear information about the implications of false positives. All individuals should be aware of the possibility of a false positive or false negative test, especially as we move to a heavier reliance on testing this fall to inform our actions and decisions. As we can see using some simple tables and math above, the PPV and NPV can be limiting even in the face of a “good” test with high sensitivity and specificity.

Without adequate understanding of the science behind testing and why false positives and false negatives happen, we might drive the public to further mistrust — and even question the usefulness — of public health and testing. Knowledge is power in this pandemic.

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Priyanka Gogna, PhD Candidate, Epidemiology, Queen's University.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

If we did not know it before, we know it now: pandemics present dire threats to our lives, similar to climate change and nuclear proliferation. Confronting these threats requires social and technical innovation and the willingness to view potential solutions in entirely new ways.

As Canada struggles with calibrating its response to COVID-19, the limits of our existing crisis strategies are plain to see.

Political leaders are stuck between controlling the spread of the pandemic and resuming commercial and economic activity. How quickly should restrictions on confinement and social distancing be relaxed? And for whom? Their responses rely largely on the extensive use of personal protective equipment (notably masks), deployment of immunity tests and test-and-tracing technologies.

There are two problems with this approach: first, they are based on after-the-fact views of COVID-19’s spread. And second, this approach treats the pandemic as a medical problem.

Managing the unknowns

The facts of this virus are becoming clear. While it is hard to know who is infected given that many may be asymptomatic, we do know that the vast majority of those who become infected will not experience severe symptoms. Data from France show that if everyone gets infected, only approximately one per cent of the population will experience symptoms severe enough to require admission to an intensive care unit.

Instead of using the blunt instrument approach of designing public health policy for an entire population, would it make more sense to predict who would fall into that highly vulnerable one per cent group and then devote the state’s resources to protecting them. That way, those who are less vulnerable can continue about their lives, while those who are more vulnerable would be better protected.

Different perspectives

Governments are not following this path. They see COVID-19 as primarily a medical problem when it is really an information problem. If it were to be seen as an information problem, then potential solutions are possible. These solutions use advanced information technologies that have proven successful in other contexts.

Consider personalized prediction. Machine-learning models fed with vast quantities of health data, for example, could be trained to make clinical risk predictions. Public health leaders could use these prediction models to identify those who are vulnerable and who would need to be quarantined and prioritized for access to scarce medical resources, such as personal protective equipment, dedicated health support, free delivery of groceries and other necessities.

Personalized prediction, based on machine learning and artificial intelligence, has transformed businesses over the last 20 years. Netflix evaluates consumers’ characteristics and past choices to make personalized recommendations about what they might watch next. Amazon uses the same approach to recommend future purchases based on past spending behaviour.

A similar approach could be taken to measure individuals’ clinical risk of suffering severe outcomes if infected during a pandemic such as COVID-19. What would this look like if rolled out on a country-wide scale?

Each person would receive an electronic message with their clinical risk score, which would be derived automatically from their medical records and reflect how vulnerable they are to a particular virus. Those with predicted scores above a certain threshold would be classified as “severe” or “high risk.” They would be temporarily isolated and supported. Those with scores below a threshold would be able to return to a more-or-less normal life.

Data-informed policies

A personalized approach to clinical risk during a pandemic outbreak has multiple benefits. It could protect medical systems from being overwhelmed and communities from the economic pain of indiscriminate lock-downs. It could help build herd immunity with lower mortality — and fast. It could also allow a more targeted and fairer allocation of resources, from test kits to hospital beds. Unlike medical tests that are scarce, expensive and slow to deploy, a data-driven digital personalization approach could be applied quickly and is relatively easy to scale.

An approach based on data science and machine learning could also enable safer de-confinement at a much faster rate than current best practices. In one study, my co-authors and I used COVID-19 data from France as of early May 2020 to understand the public health policies regarding the enacting and lifting of restrictions intended to control the spread of disease.

Our simulations show that isolation entry and exit policies could be substantially faster and safer using personalized prediction models. Our simulations indicated that the complete lifting of COVID-19 restrictions could be undertaken in six months, with only 30 per cent of the population being under strict isolation for longer than three months — all without overwhelming the medical system. In contrast, using conventional methods, simulations indicated that the complete exit would take 17 months, and 40 per cent of the population would be subject to strict isolation for more than one year.

This ideal scenario may seem like a moonshot, but a simple version could be designed and rolled out fairly quickly. Governments can focus on the data and models that can be deployed for COVID-19. For example, age, body mass index and hypertension and diabetes data for each person — all of which can be assessed at a community pharmacy for everyone within weeks and applied to an individual’s health card — can be used to train models. Even with just this information, public policy can be much more targeted.

National data infrastructure

What would need to happen to implement this new model on a province- or country-wide basis? For one thing, a deep data pool. Training a machine learning model for a pandemic such as COVID-19 would require data on thousands of people who tested positive and were hospitalized for the virus. It would also require medical data for everyone else in the population, akin to the information dossiers that big tech firms such as Facebook or Netflix have on consumers.

This is why government commitment to building a robust health data infrastructure is so important. Unfortunately, in Canada as elsewhere, the state of electronic health records varies widely. Depending on the jurisdiction, records may be incomplete or difficult to access, and information may not be standardized. A commitment to address these shortcomings is paramount. Privacy protections and cybersecurity provisions would need to be developed and well communicated.

As COVID-19 shows, the upside of applying advanced analytical tools used successfully elsewhere vastly outweighs the downside of staying the course. The question is not whether countries can apply artificial intelligence at a health-system scale. It is already being used at scale for commercial purposes that hardly involve life-or-death issues. The question for policy makers is: Can we afford not to go down this path?

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Anton Ovchinnikov, Distinguished Professor of Management Analytics at Smith School of Business, Queen's University.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The winning pitches of the ninth Queen’s Innovation Centre Summer Initiative (QICSI) have been announced with seven teams receiving funding.

This year’s edition moved online and hosted its biggest-ever cohort with more than 170 participants from around the globe and 42 teams taking part in QICSI and the newly-launched SpreadInnovation (COVID-19 innovation) challenge.

By introducing the SpreadInnovation Challenge stream, the Dunin-Deshpande Queen’s Innovation Centre (DDQIC) stepped up to provide opportunities for students and community members who had lost opportunities due to the pandemic. Specifically geared toward COVID-19, participants were tasked with building a solution for one of the pressing challenges facing our healthcare systems, livelihoods, economies, and communities. Teams were provided more than 100 days of free online training, mentorship and support from the QICSI program.

The Final Pitch Competition

After 16 weeks of hard work, 11 teams from the QICSI and the SpreadInnovation Challenge QICSI competed alongside two regional ventures in the Dunin-Deshpande Summer Pitch Competition.

The virtual format of the final pitch competition allowed DDQIC to invite a judging panel of esteemed Queen’s University alumni hailing from the Boston, San Francisco, Los Angeles, and Kingston innovation ecosystems. The judging panel was comprised of Gururaj "Desh" Deshpande (Sc’79), founder and chairman of Sparta Group LLC and founder of the Deshpande Foundation; Lauren Long (ArtSci’11), senior software engineer at Google; Anton Toutov (ArtSci’11), founder and chief science officer at Fuzionaire; and Brian Dodo (Sc;’16), founder and principal designer at BmDodo Strategic Design.

Bino Books, founded by Andena Xhiku (Comm’21), Danielle Baxter (ArtSci’19), Sydney Terry (ArtSci’20), Jessica Dassanayake (Comp’20), won the Grand Prize of $20,000, and received $10,000 in additional funding.

RESULTS

• Bino Books | $30,000 winners
• Waive the Wait | $15,000 winners
• Afino | $15,000 winners, $2,000 Wisdom of the Market Award winners
• PolitraQ | $10,000 winners
• ISOVID | $5,000 winners
• My Care Buddy | $5,000 winners
• The Youth Transit Project | $5,000 winners

Learn more about all the finalists and their projects.

As part of ongoing efforts to limit cases of COVID-19 in the Kingston region, Queen’s University, and KFL&A Public Health are asking that all students take a number of precautions before coming to Kingston. These steps – which cover testing, limiting contact, what to do upon arrival, and more – will help keep both students and the greater Kingston community safe.

“Though most students will take part in remote learning this fall, those returning to the city have an important role, as members of the community, in helping prevent the local spread of COVID-19,” says Mark Green, Provost and Vice-Principal (Academic), who shared the guidelines with new and returning students via email this week. “We know that this year brings with it some exceptional challenges, but we also know that our students are resilient and understand that coming to Queen’s carries with it the benefits and responsibilities of both learning and living in Kingston. Together, we can overcome these challenges.”

With the exception of students in on-campus Health Sciences programs – who will receive additional instructions specific to their departments and students arriving from international locations who have specific requirements ­– all students are being asked to undertake the following important steps.

Encouraging testing before coming to Kingston

Students from areas with community outbreaks or active community transmission are being encouraged to get tested for COVID-19, even without symptoms, at their local testing site before travelling to Kingston given that there is the potential for asymptomatic transmission. They are being asked to allow for enough time to receive their test results prior to departure.

Students who have already been residing in the KFL&A, Hastings and Prince Edward counties, and Leeds, Grenville, and Lanark area for 14 days are not being encouraged to get tested unless they are symptomatic.

Following any testing, while awaiting results, students should follow all public health guidelines, including the use of face coverings, handwashing, physical distancing, and limiting contact with those outside their immediate social circle, prior to arrival.

Should a student receive a positive test result, it is expected that they delay their travel to Kingston until they test negative for COVID-19. In this situation, students with on-campus programs should contact their central faculty or school office for further information. Late arrival to residence can also be arranged.

The first two weeks in Kingston

Under the direction of KFL&A Public Health, the university strongly recommends that all students upon arrival limit contact with anyone outside their household for a period of 14 days. Students who have already been residing in Kingston or the surrounding regions for 14 days, and who display no symptoms consistent with COVID-19, are not being asked to observe these requirements, but are encouraged to avoid contact with others outside their households as much as possible if their housemates are travelling from outside the region.

All students arriving in Kingston are encouraged to bring supplies and groceries needed for 14 days to help limit contact with others. Those living in residences will have access to a Queen’s dining hall. For essential shopping, ­students are encouraged to use online ordering, or to wear a mask, wash hands, and physically distance should they attend a store in person. The provost’s letter to students provides more details.

Students staying in Queen’s residences can find residence-specific health and safety protocols and information online, and those living off-campus can find more on the Queen’s University COVID-19 Information website.

International students

International students arriving in Canada must follow the federal rules set out by the emergency orders under the Quarantine Act and quarantine for 14 days without contact with others. Additionally, the Ontario government is requiring that institutions ensure that both international and domestic students who are in quarantine as a result of having entered Canada within two weeks prior to the start of their studies be tested for the COVID-19 virus at least once during their quarantine period. This requirement is over and above the normal protocols for individuals who show symptoms. It is recommended that this testing take place within five to seven days after the arrival period to address the virus incubation period.

Further information is available on the Queen's University International Centre (QUIC) website.

On-campus testing centre

The university is currently working with Kingston Health Sciences Centre and local public health authorities to establish a COVID-19 testing centre on campus. More information will be available on the COVID-19 Information website as it becomes available. For now, testing is available at the city’s local COVID-19 assessment centre. Details on hours and location are listed on KFL&A’s Public Health website.

Travelling outside Kingston

Many students may be accustomed to traveling to and from Kingston throughout the semester. Given the unprecedented nature of the COVID-19 pandemic, students are being encouraged to limit travel outside of the KFL&A region as much as possible throughout the fall term to assist in local efforts to limit the spread of COVID-19. If students choose to, or must, travel outside the KFL&A region during the fall semester, they are strongly encouraged to limit contacts for 14 days upon return. This recommendation is not meant to discourage students from seeing their families and friends as needed to support their mental health and wellness.

A strong Queen’s community

“We express our thanks to students in advance for their cooperation in helping to keep themselves, their friends and the community safe,” says Provost Green. “Be kind, check on your friends, and look out for one another. We know we can count on you to do your part to support the Queen’s and Kingston community.”

The detailed recommendations were communicated to all students through a letter from the Provost, which is posted on the COVID-19 Information website.