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.

Thanks to COVID-19, we’ve slowly built new routines centred on being at home. But as we start to enter various phases of reopening and increased contact, we may feel uncomfortable interacting in person again.

Treating each interaction as a type of micro-negotiation provides a helpful road map for navigating these potentially tricky situations.

What once were automatic interpersonal behaviours now require explicit agreement.

What do you do if someone enters the elevator with you without a mask?

If a friend rushes close to greet you?

If someone stands too closely in line?

What if you are (perhaps unintentionally) the offending party?

These situations are increasingly common and can escalate quickly into full-blown conflicts if not handled carefully. I draw on research on effective negotiations and conflict management to offer concrete suggestions and practical tips for how to ensure everyone walks away happy — and safe.

Overall, treating each interaction as a micro-negotiation first involves a change of mindset. Productive changes to your behaviour will then follow more easily.

It is important to note that many interactions won’t require all the recommendations below. But thinking about each in advance can help you be ready in the moment. A negotiation done well in this case may be one in which you don’t even realize you’ve successfully negotiated until after it’s over. Practice and preparation are key so that these tactics become second nature.

Prepare and have a plan beforehand

In negotiations, an important concept is what’s known as BATNA, which stands for the Best Alternative to a Negotiated Agreement. It is what you will do instead if you don’t reach agreement with your negotiation counterpart.

For daily interactions during the pandemic, this means you should have a clear idea in advance of what you will do if a situation gets too uncomfortable. Research shows that having a defined, desirable alternative in mind helps negotiators perform better; the psychological comfort of having an attractive backup plan helps you feel more powerful and removes unnecessary stress in the current moment.

Rather than storming away in a huff, or escalating a conflict unnecessarily, plan ahead and have explicit options in mind. For example, if mask-wearing doesn’t seem to be enforced in a particular place, know before you leave your home what you will do: you might get takeout from a different restaurant, order groceries for pickup or delivery or simply come back at a different time.

Having your alternative in mind will help you remain calm, knowing that you always have a perfectly acceptable alternative. In fact, research shows that simply feeling that you can handle a tense situation can help you avoid reacting unproductively.

Respect other perspectives, but be creative

Although it might seem inconceivable that someone may have a different comfort level in terms of interactions than you do, it’s bound to happen and doesn’t mean the other person is crazy. (In fact, they may be thinking you are the crazy one.)

A more productive approach is to try to understand the other person’s perspective, and how you can satisfy both of your underlying needs in a creative way. Separate the position (the behaviour, or the “what” that makes you feel uncomfortable) from the interest (the “why” of the behaviour).

For example, if you’re not comfortable attending the “small” get-together of friends that somehow grew much larger in number, that’s OK. Simply say so explicitly, but also suggest an alternative that could meet both your and the host’s interests (to connect with an old friend) in a different format (taking a physically distant walk together later in the week).

Remember that respecting the other person doesn’t mean you have to agree with their position.

But by being creative and focusing on deeper, underlying interests rather than more superficial positions, you can keep everyone happy.

Don’t take it personally, and use threats wisely

Despite our best intentions, it is likely that some interactions may lead to strong emotions, even anger.

However, rather than reacting angrily to a situation, which can backfire depending on how it is received, take a step back and reconsider the situation from an open-minded, problem-solving perspective.

Use the other person’s reactions and emotions as a trigger to help you find out what’s really going on at a deeper level, which research shows shows can help you reach a more mutually beneficial solution without having to simply give in to the other person’s demands.

If you feel that you need to resort to ultimatums, do so carefully and purposefully. Research suggests that WISE threats — those that you are willing to enact, that serve your underlying interests, that help the other person save face or maintain their dignity and that are exact rather than vague — are more likely to lead to effective conflict resolution.

Thinking about each interaction you have as a form of micro-negotiation will help you practise a few fairly minor behavioural and mindset changes so that you, and those around you, are more likely to have positive interactions and avoid unnecessary conflict.

It’s important to remember that we’re all navigating uncharted waters, and negotiating what used to be mundane but now feels uncomfortable may not come naturally. However, with conscious practice and an open mind, it’s possible to approach even the most challenging interactions from a productive problem-solving mindset.

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Laura Rees, Assistant Professor of Organizational Behaviour, Smith School of Business, Queen's University.

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

Provost and Vice-Principal (Academic) Mark Green announced on Wednesday the resumption of the search process for the next Dean of the Smith School of Business

The provost will chair the Principal’s Advisory Committee to advise on Smith’s future direction, and on the selection of the next dean.

“Much has changed since the previous committee last met in October, and thus I have decided to form a new search committee,” Provost Green says. “I think the community needs the opportunity to reflect on the appropriate composition of the committee for current circumstances, which include an enhanced focus on equity, diversity, inclusion, and Indigeneity within Smith and across campus.”

Provost Green thanked those who participated in the previous committee for all their hard work and dedication and invited members of the community to suggest individuals who might serve on the new Principal’s Advisory Committee.

Committee member suggestions can be sent to provost@queensu.ca by Tuesday, Aug. 18.

“Principal and Vice-Chancellor Patrick Deane and I would like to thank Interim Dean of the Smith School of Business Brenda Brouwer for her ongoing leadership as we continue the search for the next dean,” Provost Green adds.