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Pooling multiple models during COVID-19 pandemic provided more reliable projections about an uncertain future

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Pooling multiple models during COVID-19 pandemic provided more reliable projections about an uncertain future

The sum is greater than the parts when researchers build an ensemble from multiple coordinated but independent models.
Matteo Chinazzi, CC BY-ND

Emily Howerton, Penn State; Cecile Viboud, National Institutes of Health, and Justin Lessler, University of North Carolina at Chapel Hill

How can anyone decide on the best course of action in a world full of unknowns?

There are few better examples of this than the COVID-19 pandemic, when officials fervently compared potential outcomes as they weighed options like whether to implement lockdowns or require masks in schools. The main tools they used to compare these futures were epidemic models.

But often, models included numerous unstated assumptions and considered only one scenario – for instance, that lockdowns would continue. Chosen scenarios were rarely consistent across models. All this variability made it difficult to compare models, because it’s unclear whether the differences between them were due to different starting assumptions or scientific disagreement.

In response, we came together with colleagues to found the U.S. COVID-19 Scenario Modeling Hub in December 2020. We real-time, long-term projections in the U.S. for use by federal agencies such as the Centers for Disease Control and Prevention, local authorities and the public. We work directly with public health officials to identify which possible futures, or scenarios, would be most helpful to consider as they set policy, and we convene multiple independent modeling teams to make projections of public health outcomes for each scenario. Crucially, multiple teams address the same question allows us to better envision what could possibly happen in the future.

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Since its inception, the Scenario Modeling Hub has generated 17 rounds of projections of COVID-19 cases, hospitalizations and deaths in the U.S. across varying stages of the pandemic. In a recent study published in the journal Nature Communications, we looked back at all these projections and evaluated how well they matched the reality that unfolded. This work provided insights about when and what kinds of model projections are most trustworthy – and most importantly supported our strategy of combining multiple models into one ensemble.

line graph that ends in multiple colored options on the right
Collecting projections from multiple independent models provides a fuller picture of possible futures − as in this graph of potential hospitalizations − and allows researchers to generate an ensemble.
COVID-19 Scenario Modeling Hub, CC BY-ND

Multiple models are better than just one

A founding principle of our Scenario Modeling Hub is that multiple models are more reliable than one.

From tomorrow’s temperature on your weather app to predictions of interest rates in the next few months, you likely use the combined results of multiple models all the time. Especially in times like the COVID-19 pandemic when uncertainty abounds, combining projections from multiple models into an ensemble provides a fuller picture of what could happen in the future. Ensembles have become ubiquitous in many fields, primarily because they work.

Our analysis of this approach with COVID-19 models resoundingly showed the strong performance of the Scenario Modeling Hub ensemble. Not only did the ensemble give us more accurate predictions of what could happen in the future overall, it was substantially more consistent than any individual model throughout the different stages of the pandemic. When one model failed, another performed well, and by taking into account results from all of these varying models, the ensemble emerged as more accurate and more reliable.

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Researchers have previously shown performance benefits of ensembles for short-term forecasts of influenza, dengue and SARS-CoV-2. But our recent study is one of the first times researchers have tested this effect for long-term projections of alternative scenarios.

A ‘hub’ makes multimodel projections possible

While scientists know combining multiple models into an ensemble improves predictions, it can be tricky to put an ensemble together. For example, in order for an ensemble to be meaningful, model outputs and key assumptions need to be standardized. If one model assumes a new COVID-19 variant will gain steam and another model does not, they will up with vastly different results. Likewise, a model that projects cases and one that projects hospitalizations would not provide comparable results.

people seated around an open conference table with whiteboards
Meeting frequently helps multiple modeling teams stay on the same page.
Matteo Chinazzi, CC BY-ND

Many of these challenges are overcome by convening as a “hub.” Our modeling teams meet weekly to make sure we’re all on the same page about the scenarios we model. This way, any differences in what individual models are the result of things researchers truly do not know. Retaining this scientific disagreement is essential; the of the Scenario Modeling Hub ensemble arises because each modeling team takes a different approach.

At our hub we work together to design our scenarios strategically and in close collaboration with public health officials. By projecting outcomes under specific scenarios, we can estimate the impact of particular interventions, like vaccination.

For example, a scenario with higher vaccine uptake can be compared with a scenario with current vaccination rates to understand how many lives could potentially be saved. Our projections have informed recommendations of COVID-19 vaccines for children and bivalent boosters for all age groups, both in 2022 and 2023.

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In other cases, we design scenarios to explore the effects of important unknowns, such as the impact of a new variant – known or hypothetical. These types of scenarios can individuals and institutions know what they might be up against in the future and plan accordingly.

Although the hub process requires substantial time and resources, our results showed that the effort has clear payoffs: The information we generate together is more reliable than the information we could generate alone.

woman filling out a form with a COVID vaccine sign in the foreground
What models suggest are likely futures can inform real-world decisions, such as when to a vaccine clinic.
Eric Lee for The Washington Post via Getty Images

Past reliability, confidence for future

Because Scenario Modeling Hub projections can inform real public health decisions, it is essential that we provide the best possible information. Holding ourselves accountable in retrospective evaluations not only allows us to identify places where the models and the scenarios can be improved, but also helps us build trust with the people who rely on our projections.

Our hub has expanded to produce scenario projections for influenza, and we are introducing projections of respiratory syncytial virus, or RSV. And encouragingly, other groups abroad, particularly in the EU, are replicating our setup.

Scientists around the world can take the hub-based approach that we’ve shown improves reliability during the COVID-19 pandemic and use it to a comprehensive public health response to important pathogen threats.The Conversation

Emily Howerton, Postdoctoral Scholar in Biology, Penn State; Cecile Viboud, Senior Research Scientist, National Institutes of Health, and Justin Lessler, Professor of Epidemiology, University of North Carolina at Chapel Hill

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Will your phone one day let you smell as well as see and hear what’s on the other end of a call?

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theconversation.com – Jian Liu, Assistant Professor of Electrical Engineering and Computer Science, of Tennessee – 2024-09-16 07:27:05

Phones that transmit odors seem like a great idea, but careful what you wish for!

Teo Mahatmana/iStock via Getty Images

Jian Liu, University of Tennessee

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Curious Kids is a for of all ages. If you have a question you’d like an expert to answer, send it to curiouskidsus@theconversation.com.


Is it possible to make a phone through which we can smell, like we can hear and see? – Muneeba K., age 10, Pakistan


Imagine this: You pick up your phone for a call with a friend. Not only can you see their face and hear their voice, but you can also smell the cookies they just baked. It sounds like something out of a science fiction , but could it actually happen?

I’m a computer scientist who studies how machines sense the world.

What phones do now

When you listen to music or to someone on your phone, you can hear the sound through the built-in speakers. These speakers convert digital signals into physical vibrations using a tiny component called a diaphragm. Your ears sense those vibrations as sound waves.

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Your phone also has a screen that displays images and . The screen uses tiny dots known as pixels that consist of three primary colors: red, green and blue. By mixing these colors in different ways, your phone can show you everything from beautiful beach scenes to cute puppies.

Smelling with phones

Now how about the sense of smell? Smells are created by tiny particles called molecules that float through the and reach your nose. Your nose then sends signals to your brain, which identifies the smell.

So, could your phone send these smell molecules to you? Scientists are working on it. Think about how your phone screen works. It doesn’t have every color in the world stored inside it. Instead, it uses just three colors to create millions of different hues and shades.

How your sense of smell works.

Now imagine something similar for smells. Scientists are developing digital scent technology that uses a small number of different cartridges, each containing a specific scent. Just like how pixels mix three colors to create images, these scent cartridges could mix to create different smells.

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Just like images on your phone are made of digital codes that represent combinations of pixels, smells produced by a future phone could be created using digital codes. Each smell could have a specific recipe made up of different amounts of the ingredients in the cartridges.

When you a digital scent code, your phone could mix tiny amounts of the different scents from the cartridges to create the desired smell. This mix would then be released through a small vent on the phone, allowing you to smell it. With just a few cartridges, your phone could potentially create a huge variety of smells, much like how red, green and blue pixels can create countless colors.

Researchers and companies are already working on digital odor makers like this.

The challenges to making smell phones

Creating a phone that can produce smells involves several challenges. One is designing a system that can produce thousands of different smells using only a few cartridges. Another is how to control how strong a scent should be and how long a phone should emit it. And phones will also need to sense odors near them and convert those to digital codes so your friends’ phones can send smells to you.

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The cartridges should also be easy to refill, and the chemicals in them be safe to breathe. These hurdles make it a tricky but exciting area of research.

An odiferous future

Even though we’re not there yet, scientists and engineers are working hard to make smell phones a reality. Maybe one day you’ll be able to not only see and hear your friend’s birthday party over the phone, but also smell the candles they blew out!


Hello, curious kids! Do you have a question you’d like an expert to answer? Ask an adult to send your question to CuriousKidsUS@theconversation.com. Please tell us your name, age and the city where you live.

And since curiosity has no age limit – adults, let us know what you’re wondering, too. We won’t be able to answer every question, but we will do our best.The Conversation

Jian Liu, Assistant Professor of Electrical Engineering and Computer Science, University of Tennessee

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a double shot of US history

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theconversation.com – Kyle G. Volk, Professor of History, University of Montana – 2024-09-16 07:28:46

a beer in Raceland, La.

Russell Lee for Farm Security Administration/WPA

Kyle G. Volk, University of Montana

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Text saying: Uncommon Courses, from The Conversation

Uncommon Courses is an occasional from U.S. highlighting unconventional approaches to teaching.

Title of course:

“Intoxication Nation: Alcohol in American History”

What prompted the idea for the course?

I wanted to get excited about studying the past by learning about something that is very much a part of their own lives.

Alcohol – somewhat surprisingly to me at first – prominently in my own research on minority rights and U.S. democracy in the mid-19th century. As a result, I knew quite a bit about the temperance movement and conflicts over prohibition during that period. Designing this course allowed me to broaden my expertise.

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What does the course explore?

Prohibition is a must-do subject. Students expect it. But I cover several hundred years of history: from the 17th-century invention of rum – as a byproduct of sugar produced by enslaved people – to the rise of craft beer and craft spirits in the 21st century.

A faded poster with an illustration of a person about to smash a huge bottle of alcohol, and the message 'Close the saloons' at the top.

A temperance poster from the World War I era.

Office of Naval Records and Library via National Archives Catalog

Along the way, I’m thrilled when students get excited about details that allow them to a more complicated historical cocktail. For example, they learn why white women’s production of hard cider was crucial to the survival of colonial Virginia. The short answer: Potable was in short supply, alcoholic drinks were far healthier, and white men – and their indentured and enslaved workforce – were busy raising tobacco. It fell to women to turn fruit into salvation.

Why is this course relevant now?

Alcohol remains a big and almost inescapable part of American society. But of late, Americans have been drinking differently – and thinking about drinking differently.

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Examples abound. Alcohol producers, we learn, now face competition from legalized weed. Drinking l evels rose during the COVID-19 pandemic, yet interest is declining among Gen Zers. The “wine mom” culture that brought some mothers together now faces mounting criticism.

And, of course, there’s the never-ending debate about the health benefits and risks of alcohol. Of late, the risks seem to be dominating headlines.

What’s a critical lesson from the course?

Alcohol has been a highly controversial, central aspect of the American experience, shaping virtually all sectors of our society – political and constitutional, business and economic, social and cultural.

What materials does the course feature?

What will the course prepare students to do?

Like any history course, this one aims to develop student’s analytical, written, research and verbal skills. In lots of ways, the topic is just a tool to get students to grow their brains. But I also seek to grow students’ critical awareness of the place of alcohol in their own lives. The course has also informed students’ paths after graduation – some who wound up working in the alcohol industry or recovery .The Conversation

Kyle G. Volk, Professor of History, University of Montana

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Sunflowers make small moves to maximize their Sun exposure − physicists can model them to predict how they grow

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theconversation.com – Chantal Nguyen, Postdoctoral Associate at the BioFrontiers Institute, of Colorado Boulder – 2024-09-13 07:31:40

Sunflowers use tiny movements to follow the Sun’s path throughout the day.

AP Photo/Charlie Riedel

Chantal Nguyen, University of Colorado Boulder

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Most of us aren’t spending our days watching our houseplants grow. We see their signs of only occasionally – a new leaf unfurled, a stem leaning toward the window.

But in the summer of 1863, Charles Darwin lay ill in bed, with nothing to do but watch his plants so closely that he could detect their small movements to and fro. The tendrils from his cucumber plants swept in circles until they encountered a stick, which they proceeded to twine around.

“I am getting very much amused by my tendrils,” he wrote.

This amusement blossomed into a decadeslong fascination with the little-noticed world of plant movements. He compiled his detailed observations and experiments in a 1880 book called “The Power of Movement in Plants.”

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A zig-zagging line showing the movement of a leaf.

A diagram tracking the circumnutation of a leaf over three days.

Charles Darwin

In one study, he traced the motion of a carnation leaf every few hours over the course of three days, revealing an irregular looping, jagged path. The swoops of cucumber tendrils and the zags of carnation leaves are examples of inherent, ubiquitous plant movements called circumnutations – from the Latin circum, meaning circle, and nutare, meaning to nod.

Circumnutations vary in size, regularity and timescale across plant species. But their exact function remains unclear.

I’m a physicist interested in understanding collective behavior in living . Like Darwin, I’m captivated by circumnutations, since they may underlie more complex phenomena in groups of plants.

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Sunflower patterns

A 2017 study revealed a fascinating observation that got my colleagues and me wondering about the role circumnutations could play in plant growth patterns. In this study, researchers found that sunflowers grown in a dense row naturally formed a near-perfect zigzag pattern, with each plant leaning away from the row in alternating directions.

This pattern the plants to avoid shade from their neighbors and maximize their exposure to sunlight. These sunflowers flourished.

Researchers then planted some plants at the same density but constrained them so that they could grow only upright without leaning. These constrained plants produced less oil than the plants that could lean and get the maximum amount of sun.

While farmers can’t grow their sunflowers quite this close together due to the potential for disease spread, in the future they may be able to use these patterns to up with new planting strategies.

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Self-organization and randomness

This spontaneous pattern formation is a neat example of self-organization in nature. Self-organization refers to when initially disordered systems, such as a jungle of plants or a swarm of bees, achieve order without anything controlling them. Order emerges from the interactions between individual members of the system and their interactions with the .

Somewhat counterintuitively, noise – also called randomness – facilitates self-organization. Consider a colony of ants.

Ants secrete pheromones behind them as they crawl toward a food source. Other ants find this food source by the pheromone trails, and they further reinforce the trail they took by secreting their own pheromones in turn. Over time, the ants converge on the best path to the food, and a single trail prevails.

But if a shorter path were to become possible, the ants would not necessarily find this path just by following the existing trail.

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If a few ants were to randomly deviate from the trail, though, they might stumble onto the shorter path and create a new trail. So this randomness injects a spontaneous change into the ants’ system that allows them to explore alternative scenarios.

Eventually, more ants would follow the new trail, and soon the shorter path would prevail. This randomness helps the ants adapt to changes in the environment, as a few ants spontaneously seek out more direct ways to their food source.

A group of honeybees spread out standing on honeycomb.

Beehives are an example of self-organization in nature.

Martin Ruegner/Stone via Getty Images

In biology, self-organized systems can be found at a range of scales, from the patterns of proteins inside cells to the socially complex colonies of honeybees that collectively build nests and forage for nectar.

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Randomness in sunflower self-organization

So, could random, irregular circumnutations underpin the sunflowers’ self-organization?

My colleagues and I set out to explore this question by following the growth of young sunflowers we planted in the lab. Using cameras that imaged the plants every five minutes, we tracked the movement of the plants to see their circumnutatory paths.

We saw some loops and spirals, and lots of jagged movements. These ultimately appeared largely random, much like Darwin’s carnation. But when we placed the plants together in rows, they began to move away from one another, forming the same zigzag configurations that we’d seen in the previous study.

Five plants and a diagram showing loops and jagged lines that represent small movements made by the plants.

Tracking the circumnutations made by young sunflower plants.

Chantal Nguyen

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We analyzed the plants’ circumnutations and found that at any given time, the direction of the plant’s motion appeared completely independent of how it was moving about half an hour earlier. If you measured a plant’s motion once every 30 minutes, it would appear to be moving in a completely random way.

We also measured how much the plant’s leaves grew over the course of two weeks. By putting all of these results together, we sketched a picture of how a plant moved and grew on its own. This information allowed us to computationally model a sunflower and simulate how it behaves over the course of its growth.

A sunflower model

We modeled each plant simply as a circular crown on a stem, with the crown expanding according to the growth rate we measured experimentally. The simulated plant moved in a completely random way, taking a “step” every half hour.

We created the model sunflowers with circumnutations of lower or higher intensity by tweaking the step sizes. At one end of the spectrum, sunflowers were much more likely to take tiny steps than big ones, leading to slow, minimal movement on average. At the other end were sunflowers that are equally as likely to take large steps as small steps, resulting in highly irregular movement. The real sunflowers we observed in our experiment were somewhere in the middle.

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Plants require light to grow and have evolved the ability to detect shade and alter the direction of their growth in response.

We wanted our model sunflowers to do the same thing. So, we made it so that two plants that get too close to each other’s shade begin to lean away in opposite directions.

Finally, we wanted to see whether we could replicate the zigzag pattern we’d observed with the real sunflowers in our model.

First, we set the model sunflowers to make small circumnutations. Their shade avoidance responses pushed them away from each other, but that wasn’t enough to produce the zigzag – the model plants stayed stuck in a line. In physics, we would call this a “frustrated” system.

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Then, we set the plants to make large circumnutations. The plants started moving in random patterns that often brought the plants closer together rather than farther apart. Again, no zigzag pattern like we’d seen in the field.

But when we set the model plants to make moderately large movements, similar to our experimental measurements, the plants could self-organize into a zigzag pattern that gave each sunflower optimal exposure to light.

So, we showed that these random, irregular movements helped the plants explore their surroundings to find desirable arrangements that benefited their growth.

Plants are much more dynamic than people give them credit for. By taking the time to follow them, scientists and farmers can unlock their secrets and use plants’ movement to their advantage.The Conversation

Chantal Nguyen, Postdoctoral Associate at the BioFrontiers Institute, University of Colorado Boulder

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