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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 challenge 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 provide real-time, long-term projections in the U.S. for use by federal agencies such as the Centers for Disease Control and Prevention, local health 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, having multiple teams address the same question allows us to better envision what could possibly happen in the future.

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.

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 come 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 project are the result of things researchers truly do not know. Retaining this scientific disagreement is essential; the success 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.

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 help 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 run 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 support 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

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

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Extreme heat silently accelerates aging on a molecular level − new research

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theconversation.com – Eunyoung Choi, Postdoctoral Associate in Gerontology, University of Southern California – 2025-03-04 13:44:00

Extreme heat increases the risk of a number of diseases, including kidney and heart conditions.
Spencer Platt/Getty Images

Eunyoung Choi, University of Southern California

What if extreme heat not only leaves you feeling exhausted but actually makes you age faster?

Scientists already know that extreme heat increases the risk of heat stroke, cardiovascular disease, kidney dysfunction and even death. I see these effects often in my work as a researcher studying how environmental stressors influence the aging process. But until now, little research has explored how heat affects biological aging: the gradual deterioration of cells and tissues that increases the risk of age-related diseases.

New research my team and I published in the journal Science Advances suggests that long-term exposure to extreme heat may speed up biological aging at the molecular level, raising concerns about the long-term health risks posed by a warming climate.

Person wearing a shirt reading 'EXCESSIVE HEAT ALERT' handing water bottle to older adult sitting outside
Extreme heat is a public health issue.
AP Photo/Lynne Sladky

Extreme heat’s hidden toll on the body

My colleagues and I examined blood samples from over 3,600 older adults across the United States. We measured their biological age using epigenetic clocks, which capture DNA modification patterns – methylation – that change with age.

DNA methylation refers to chemical modifications to DNA that act like switches to turn genes on and off. Environmental factors can influence these switches and change how genes function, affecting aging and disease risk over time. Measuring these changes through epigenetic clocks can strongly predict age-related disease risk and lifespan.

Research in animal models has shown that extreme heat can trigger what’s known as a maladaptive epigenetic memory, or lasting changes in DNA methylation patterns. Studies indicate that a single episode of extreme heat stress can cause long-term shifts in DNA methylation across different tissue types in mice. To test the effects of heat stress on people, we linked epigenetic clock data to climate records to assess whether people living in hotter environments exhibited faster biological aging.

Two people sitting with their backs against the corner of a blue building,
Certain populations are more vulnerable to extreme heat.
Angela Weiss/AFP via Getty Images

We found that older adults residing in areas with frequent very hot days showed significantly faster epigenetic aging compared with those living in cooler regions. For example, participants living in locations with at least 140 extreme heat days per year – classified as days when the heat index exceeded 90 degrees Fahrenheit (32.33 degrees Celcius) – experienced up to 14 months of additional biological aging compared with those in areas with fewer than 10 such days annually.

This link between biological age and extreme heat remained even after accounting for a wide range of individual and community factors such as physical activity levels and socioeconomic status. This means that even among people with similar lifestyles, those living in hotter environments may still be aging faster at the biological level.

Even more surprising was the magnitude of the effect – extreme heat has a comparable impact on speeding up aging as smoking and heavy alcohol consumption. This suggests that heat exposure may be silently accelerating aging, at a level on par with other major known environmental and lifestyle stressors.

Long-term public health consequences

While our study sheds light on the connection between heat and biological aging, many unanswered questions remain. It’s important to clarify that our findings don’t mean every additional year in extreme heat translates directly to 14 extra months of biological aging. Instead, our research reflects population-level differences between groups based on their local heat exposure. In other words, we took a snapshot of whole populations at a moment in time; it wasn’t designed to look at effects on individual people.

Our study also doesn’t fully capture all the ways people might protect themselves from extreme heat. Factors such as access to air conditioning, time spent outdoors and occupational exposure all play a role in shaping personal heat exposure and its effects. Some individuals may be more resilient, while others may face greater risks due to preexisting health conditions or socioeconomic barriers. This is an area where more research is needed.

What is clear, however, is that extreme heat is more than just an immediate health hazard – it may be silently accelerating the aging process, with long-term consequences for public health.

U.S. map showing extreme caution level or higher heat days, with the greatest number of total heat days in the South
Large swaths of the U.S. population are experiencing long stretches of extreme heat, as this map of cumulative heat days from 2010 to 2016 shows.
Eunyoung Choi, CC BY-ND

Older adults are especially vulnerable because aging reduces the body’s ability to regulate temperature effectively. Many older individuals also take medications such as beta-blockers and diuretics that can impair their heat tolerance, making it even harder for their bodies to cope with high temperatures. So even moderately hot days, such as those reaching 80 degrees Fahrenheit (26.67 degrees Celcius), can pose health risks for older adults.

As the U.S. population rapidly ages and climate change intensifies heat waves worldwide, I believe simply telling people to move to cooler regions isn’t realistic. Developing age-appropriate solutions that allow older adults to safely remain in their communities and protect the most vulnerable populations could help address the hidden yet significant effects of extreme heat.The Conversation

Eunyoung Choi, Postdoctoral Associate in Gerontology, University of Southern California

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

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Out-of-balance bacteria is linked to multiple sclerosis − the ratio can predict severity of disease

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theconversation.com – Ashutosh Mangalam, Associate Professor of Pathology, University of Iowa – 2025-03-03 14:03:00

Out-of-balance bacteria is linked to multiple sclerosis − the ratio can predict severity of disease

The myelin sheaths insulating neurons are damaged in multiple sclerosis.
Steve Gschmeissner/Science Photo Library/Brand X Pictures via Getty Images

Ashutosh Mangalam, University of Iowa

Multiple sclerosis is a disease that results when the immune system mistakenly attacks the brain and spinal cord. It affects nearly one million people in the U.S. and over 2.8 million worldwide. While genetics play a role in the risk of developing multiple sclerosis, environmental factors such as diet, infectious disease and gut health are major contributors.

The environment plays a key role in determining who develops multiple sclerosis, and this is evident from twin studies. Among identical twins who share 100% of their genes, one twin has a roughly 25% chance of developing MS if the other twin has the disease. For fraternal twins who share 50% of their genes, this rate drops to around 2%.

Scientists have long suspected that gut bacteria may influence a person’s risk of developing multiple sclerosis. But studies so far have had inconsistent findings.

To address these inconsistencies, my colleagues and I used what researchers call a bedside-to-bench-to-bedside approach: starting with samples from patients with multiple sclerosis, conducting lab experiments on these samples, then confirming our findings in patients.

In our newly published research, we found that the ratio of two bacteria in the gut can predict multiple sclerosis severity in patients, highlighting the importance of the microbiome and gut health in this disease.

Microscopy image of large clump of rod-like bacteria
Akkermansia is commonly found in the human gut microbiome.
Zhang et al/Microbial Biotechnology, CC BY-SA

Bedside to bench

First, we analyzed the chemical and bacterial gut composition of patients with multiple sclerosis, confirming that they had gut inflammation and different types of gut bacteria compared with people without multiple sclerosis.

Specifically, we showed that a group of bacteria called Blautia was more common in multiple sclerosis patients, while Prevotella, a bacterial species consistently linked to a healthy gut, was found in lower amounts.

In a separate experiment in mice, we observed that the balance between two gut bacteria, Bifidobacterium and Akkermansia, was critical in distinguishing mice with or without multiple sclerosis-like disease. Mice with multiple sclerosis-like symptoms had increased levels of Akkermansia and decreased levels of Bifidobacterium in their stool or gut lining.

Bench to bedside

To explore this further, we treated mice with antibiotics to remove all their gut bacteria. Then, we gave either Blautia, which was higher in multiple sclerosis patients; Prevotella, which was more common in healthy patients; or a control bacteria, Phocaeicola, which is found in patients with and without multiple sclerosis. We found that mice with Blautia developed more gut inflammation and worse multiple sclerosis-like symptoms.

Even before symptoms appeared, these mice had low levels of Bifidobacterium and high levels of Akkermansia. This suggested that an imbalance between these two bacteria might not just be a sign of disease, but could actually predict how severe it will be.

We then examined whether this same imbalance appeared in people. We measured the ratio of Bifidobacterium adolescentis and Akkermansia muciniphila in samples from multiple sclerosis patients in Iowa and participants in a study spanning the U.S., Latin America and Europe.

Our findings were consistent: Patients with multiple sclerosis had a lower ratio of Bifidobacterium to Akkermansia. This imbalance was not only linked to having multiple sclerosis but also with worse disability, making it a stronger predictor of disease severity than any single type of bacteria alone.

Microscopy image of clusters of rod bacteria
Bifidobacterium both produces and consumes mucin, a glycoprotein that protects the gut lining.
Paola Mattarelli and Monica Modesto/Katz Lab via Flickr, CC BY-NC

How ‘good’ bacteria can become harmful

One of the most interesting findings from our study was that normally beneficial bacteria can turn harmful in multiple sclerosis. Akkermansia is usually considered a helpful bacterium, but it became problematic in patients with multiple sclerosis.

A previous study in mice showed a similar pattern: Mice with severe disease had a lower Bifidobacterium-to-Akkermansia ratio. In that study, mice fed a diet rich in phytoestrogens – chemicals structurally similar to human estrogen that need to be broken down by bacteria for beneficial health effects – developed milder disease than those on a diet without phytoestrogens. Previously we have shown that people with multiple sclerosis lack gut bacteria that can metabolize phytoestrogen.

Although the precise mechanisms behind the link between the Bifidobacterium-to- Akkermansia ratio and multiple sclerosis is unknown, researchers have a theory. Both types of bacteria consume mucin, a substance that protects the gut lining. However, Bifidobacterium both eats and produces mucin, while Akkermansia only consumes it. When Bifidobacterium levels drop, such as during inflammation, Akkermansia overconsumes mucin and weakens the gut lining. This process can trigger more inflammation and potentially contribute to the progression of multiple sclerosis.

Our finding that the Bifidobacterium-to-Akkermansia ratio may be a key marker for multiple sclerosis severity could help improve diagnosis and treatment. It also highlights how losing beneficial gut bacteria can allow other gut bacteria to become harmful, though it is unclear whether changing levels of certain microbes can affect multiple sclerosis.

While more research can help clarify the link between the gut microbiome and multiple sclerosis, these findings offer a promising new direction for understanding and treating this disease.The Conversation

Ashutosh Mangalam, Associate Professor of Pathology, University of Iowa

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

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How are clouds’ shapes made? A scientist explains the different cloud types and how they help forecast weather

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theconversation.com – Ross Lazear, Instructor in Atmospheric and Environmental Sciences, University at Albany, State University of New York – 2025-03-03 07:18:00

Lenticular clouds, like this one over a mountain in Chile, can look like flying saucers.
Bilderbuch/Design Pics Editorial/Universal Images Group via Getty Images

Ross Lazear, University at Albany, State University of New York

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


How are clouds’ shapes made? – Amanda, age 5, Chile


I’m a meteorologist, and I’ve been fascinated by weather since I was 8 years old. I grew up in Minnesota, where the weather changes from wind-whipping blizzards in winter to severe thunderstorms – sometimes with tornadoes – in the summer. So, it’s not all that surprising that I’ve spent most of my life looking at clouds.

All clouds form as a result of saturation – that’s when the air contains so much water vapor that it begins producing liquid or ice.

Once you understand how certain clouds develop their shapes, you can learn to forecast the weather.

A view showing typical cloud heights shows tall cumulonimbus clouds, low level cumulus and high-level cirrus.
Cloud types show their general heights.
Australian Bureau of Meteorology

Cotton ball cumulus clouds

Clouds that look like cartoon cotton balls or cauliflower are made up of tiny liquid water droplets and are called cumulus clouds.

Often, these are fair-weather clouds that form when the Sun warms the ground and the warm air rises. You’ll often see them on humid summer days.

A horse or donkey next to river bank with puffy clouds in the sky.
Cumulus clouds over Lander, Wyo.
Ross Lazear, CC BY-ND

However, if the air is particularly warm and humid, and the atmosphere above is much colder, cumulus clouds can rapidly grow vertically into cumulonimbus. When the edges of these clouds look especially crisp, it’s a sign that heavy rain or snow may be imminent.

Wispy cirrus are ice clouds

When cumulonimbus clouds grow high enough into the atmosphere, the temperature becomes cold enough for ice clouds, or cirrus, to form.

Clouds made up entirely of ice are usually more transparent. In some cases, you can see the Sun or Moon through them.

Streaks of high white clouds look like paintbrush strokes
Cirrus clouds over the roof of Bard College in Annandale-on-Hudson, N.Y.
Ross Lazear, CC BY-ND

Cirrus clouds that forms atop a thunderstorm spread outward and can form anvil clouds. These clouds flatten on top as they reach the stratosphere, where the atmosphere begins to warm with height.

However, most cirrus clouds aren’t associated with storms at all. There are many ice clouds associated with tranquil weather that are simply regions of the atmosphere with more moisture but not precipitation.

Fog and stratus clouds

Clouds are a result of saturation, but saturated air can also exist at ground level. When this occurs, we call it fog.

In temperatures below freezing, fog can actually deposit ice onto objects at or near the ground, called rime ice.

YouTube video
Reading clouds, with the National Oceanic and Atmospheric Administration.

When clouds form thick layers, we add the word “stratus,” or “layer,” to the name. Stratus can occur just above the ground, or a bit higher up – we call it altostratus then. It can occur even higher and become cirrostratus, or a layer or ice clouds.

If there’s enough moisture and lift, stratus clouds can create rain or snow. These are nimbostratus.

How mountains can create their own clouds

There are a number of other unique and beautiful cloud types that can form as air rises over mountain slopes and other topography.

Lenticular clouds, for example, can look like flying saucers hovering just above, or near, mountaintops. Lenticular clouds can actually form far from mountains, as wind over a mountain range creates an effect like ripples in a pond.

A cloud appears to stream off the side of a tall mountain peak.
A banner cloud appears to stream out from the Matterhorn, in the Alps on the border between Italy and Switzerland.
Zacharie Grossen via Wikimedia, CC BY

Rarer are banner clouds, which form from horizontally spinning air on one side of a mountain.

Wind plays a big role

You might have looked up at the sky and noticed one layer of clouds moving in a different direction from another. Clouds move along with the wind, so what you’re seeing is the wind changing direction with height.

Cirrus clouds at the level of the jet stream – often about 6 miles (10 kilometers), above the ground – can sometimes move at over 200 miles per hour (320 kilometers per hour). But because they are so high up, it’s often hard to tell how fast they are moving.


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

Ross Lazear, Instructor in Atmospheric and Environmental Sciences, University at Albany, State University of New York

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

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