Also consider our pandemic problems in the mind of an engineer

The last 20 months each dog was treated by an amateur epidemiologist and statistician. Meanwhile, a group of bona fide epidemiologists and statisticians believe that pandemic problems can be more effectively solved by adopting the mindset of an engineer: that is, focusing on problem-solving each and every strategy to make things work.

In a current essay, “Accounting for uncertainty during a pandemic, “Researchers consider their roles during a public health emergency and how they can be better prepared for the next crisis. The answer, they write, may lie in reimagining epidemiology with more. from an engineering perspective and not so much a “pure science” perspective.

Epidemiological research informs public health policy and the nature used mandate for prevention and protection. Yet the right balance between pure research outcomes and proactive solutions has proven to be frighteningly unrecognizable during a pandemic.

We have to make practical decisions, so what causes uncertainty?

Seth Guikema

“I’ve always imagined that in this kind of emergency, epidemiologists could be useful people,” Jon Zelner, a coauthor of the essay, said. “But our role is more complicated and much less well -defined than I expected at the beginning of the pandemic.” A model of infectious disease and social epidemiologist at the University of Michigan, Zelner has witnessed an “insane increase” in research papers, “many have little idea of ​​what it really means in terms of having positive effect. “

“There are a number of missed opportunities,” says Zelner – caused by the lack of connections between the ideas and tools proposed by epidemiologists and the world they aim to help.

Provide reassurance

Coauthor Andrew Gelman, a statistician and political scientist at Columbia University, sets the “more picture” in the introduction to the paper. He likened the outbreak of the pandemic to amateur epidemiologists to the way the war made every citizen an amateur geographer and tactician: People on the streets argued about infection death rates and crowd resistance in a way that perhaps they have debated combat strategies and alliances in the past. “

And with all the data and public speaking – Do we still need masks? How long will vaccine protection last?

In an attempt to figure out what happened and what went wrong, the researchers (who also include Ruth Etzioni at the University of Washington and Julien Riou at the University of Bern) did something of a reenactment. They examine the tools used to solve challenges such as estimating the rate of transfer from person to person and the number of cases circulating in a population at any given time. They examine everything from data collection (the quality of the data and its interpretation are the most common pandemic challenges) to model design to statistical analysis, as well as communication, decision making. , and trust. “Uncertainty is at every step,” they wrote.

However, Gelman said, the analysis still “doesn’t adequately express the confusion I’ve been through in the past few months.”

One tactic against all uncertainty is statistics. Gelman thinks of statistics as “mathematical engineering” – methods and tools that have as much resistance as have been discovered. Statistical sciences attempt to shed light on what is happening in the world, with a light weight of diversity and uncertainty. When new evidence arrives, it should create an iterative process that gradually refines prior knowledge and ensures reliability.

Good science is humble and able to refine itself even when there is uncertainty.

Marc Lipsitch

Susan Holmes, a Stanford statistician who was not involved in this research, also found engineering thinking skills. “An engineer is constantly updating their photo,” he says-reviewing when new data and tools are available. To solve a problem, an engineer offers a first-order arrival (blurry), then a second-order arrival (more focused), and so on.

However, there is Gelman previously warned that statistical science can be deployed as a machine for “cleaning up uncertainty” —unintentionally or not, crappy (uncertain) data is put together and made as convincing (specific). The statistic used against uncertainties “is always marketed as a kind of alchemy that shifts the uncertainties that it is certainty.”

We witnessed this during the pandemic. Drowned in chaos and unknown, epidemiologists and statisticians-amateur and expert alike-turned for something solid to try to stay afloat. But as Gelman points out, the desire for security during a pandemic is inappropriate and unrealistic. “Uncertainty has become part of the challenge of pandemic decisions,” he said. “Jumping between uncertainty and uncertainty causes a lot of problems.”

Leaving the desire for security can be liberating, he said. And this is, in part, where the engineering vision comes in.

A tinkering mindset

For Seth Guikema, co-director of the Center for Risk Analysis and Informed Decision Engineering at the University of Michigan (and a colleague of Zelner’s on other projects), a key aspect of the engineering approach is the immersion certainty, analyzing the chaos, and then taking a step, with the vision, “We have to make practical decisions, so what causes uncertainty?” Because if there’s a lot of uncertainty-and if that uncertainty changes what the best decisions are, or what the best decisions are-then that’s reason to be aware of, Guikema said. “But if it doesn’t really affect what my best decision is, that’s not very critical.”

For example, increasing the coverage of SARS-CoV-2 vaccination in the entire population is a scenario where even if there is uncertainty about exactly how many cases or deaths will be prevented, the fact is likely to decrease both, which there are few side effects, enough motivation to judge that a large vaccination program is a good idea.

An engineer constantly updates their photo.

Susan Holmes

Engineers, Holmes points out, are also very good at breaking down problems into critical pieces, applying carefully selected tools, and optimizing for solutions under constraints. With a team of engineers building a bridge, there is a cement specialist and a steel specialist, an air engineer and a structural engineer. “All the different specialists are working together,” he said.

For Zelner, the idea of ​​epidemiology as an engineering discipline was something he got from his father, a mechanical engineer who started his own company designing health care facilities. Drawing a childhood full of building and repairing things, his engineering thinking involved tinkering-refining a transfer model, for example, in response to a moving target.

“Often these problems require iterative solutions, where you make changes in response to what works or doesn’t work,” he says. “You keep updating what you’re doing as more data comes in and you see the successes and failures of your approach. To me, that’s very different – and more suited to the complex, relentless problems that pertain to public health – than the kind of static self -made image that many people have in academic science, where you have a lot of ideas, try them, and your result is stored in amber at all times. ”

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