Science
When A.I.’s Output Is a Threat to A.I. Itself
The internet is becoming awash in words and images generated by artificial intelligence.
Sam Altman, OpenAI’s chief executive, wrote in February that the company generated about 100 billion words per day — a million novels’ worth of text, every day, an unknown share of which finds its way onto the internet.
A.I.-generated text may show up as a restaurant review, a dating profile or a social media post. And it may show up as a news article, too: NewsGuard, a group that tracks online misinformation, recently identified over a thousand websites that churn out error-prone A.I.-generated news articles.
In reality, with no foolproof methods to detect this kind of content, much will simply remain undetected.
All this A.I.-generated information can make it harder for us to know what’s real. And it also poses a problem for A.I. companies. As they trawl the web for new data to train their next models on — an increasingly challenging task — they’re likely to ingest some of their own A.I.-generated content, creating an unintentional feedback loop in which what was once the output from one A.I. becomes the input for another.
In the long run, this cycle may pose a threat to A.I. itself. Research has shown that when generative A.I. is trained on a lot of its own output, it can get a lot worse.
Here’s a simple illustration of what happens when an A.I. system is trained on its own output, over and over again:
This is part of a data set of 60,000 handwritten digits.
When we trained an A.I. to mimic those digits, its output looked like this.
This new set was made by an A.I. trained on the previous A.I.-generated digits. What happens if this process continues? After 20 generations of training new A.I.s on their predecessors’ output, the digits blur and start to erode.
After 30 generations, they converge into a single shape.
While this is a simplified example, it illustrates a problem on the horizon.
Imagine a medical-advice chatbot that lists fewer diseases that match your symptoms, because it was trained on a narrower spectrum of medical knowledge generated by previous chatbots. Or an A.I. history tutor that ingests A.I.-generated propaganda and can no longer separate fact from fiction.
Just as a copy of a copy can drift away from the original, when generative A.I. is trained on its own content, its output can also drift away from reality, growing further apart from the original data that it was intended to imitate.
In a paper published last month in the journal Nature, a group of researchers in Britain and Canada showed how this process results in a narrower range of A.I. output over time — an early stage of what they called “model collapse.”
The eroding digits we just saw show this collapse. When untethered from human input, the A.I. output dropped in quality (the digits became blurry) and in diversity (they grew similar).
How an A.I. that draws digits “collapses” after being trained on its own output
If only some of the training data were A.I.-generated, the decline would be slower or more subtle. But it would still occur, researchers say, unless the synthetic data was complemented with a lot of new, real data.
Degenerative A.I.
In one example, the researchers trained a large language model on its own sentences over and over again, asking it to complete the same prompt after each round.
When they asked the A.I. to complete a sentence that started with “To cook a turkey for Thanksgiving, you…,” at first, it responded like this:
Even at the outset, the A.I. “hallucinates.” But when the researchers further trained it on its own sentences, it got a lot worse…
An example of text generated by an A.I. model. After two generations, it started simply printing long lists.
An example of text generated by an A.I. model after being trained on its own sentences for 2 generations.
And after four generations, it began to repeat phrases incoherently. An example of text generated by an A.I. model after being trained on its own sentences for 4 generations.
“The model becomes poisoned with its own projection of reality,” the researchers wrote of this phenomenon.
This problem isn’t just confined to text. Another team of researchers at Rice University studied what would happen when the kinds of A.I. that generate images are repeatedly trained on their own output — a problem that could already be occurring as A.I.-generated images flood the web.
They found that glitches and image artifacts started to build up in the A.I.’s output, eventually producing distorted images with wrinkled patterns and mangled fingers.
When A.I. image models are trained on their own output, they can produce distorted images, mangled fingers or strange patterns.
A.I.-generated images by Sina Alemohammad and others.
“You’re kind of drifting into parts of the space that are like a no-fly zone,” said Richard Baraniuk, a professor who led the research on A.I. image models.
The researchers found that the only way to stave off this problem was to ensure that the A.I. was also trained on a sufficient supply of new, real data.
While selfies are certainly not in short supply on the internet, there could be categories of images where A.I. output outnumbers genuine data, they said.
For example, A.I.-generated images in the style of van Gogh could outnumber actual photographs of van Gogh paintings in A.I.’s training data, and this may lead to errors and distortions down the road. (Early signs of this problem will be hard to detect because the leading A.I. models are closed to outside scrutiny, the researchers said.)
Why collapse happens
All of these problems arise because A.I.-generated data is often a poor substitute for the real thing.
This is sometimes easy to see, like when chatbots state absurd facts or when A.I.-generated hands have too many fingers.
But the differences that lead to model collapse aren’t necessarily obvious — and they can be difficult to detect.
When generative A.I. is “trained” on vast amounts of data, what’s really happening under the hood is that it is assembling a statistical distribution — a set of probabilities that predicts the next word in a sentence, or the pixels in a picture.
For example, when we trained an A.I. to imitate handwritten digits, its output could be arranged into a statistical distribution that looks like this:
Distribution of A.I.-generated data
Examples of
initial A.I. output:
The distribution shown here is simplified for clarity.
The peak of this bell-shaped curve represents the most probable A.I. output — in this case, the most typical A.I.-generated digits. The tail ends describe output that is less common.
Notice that when the model was trained on human data, it had a healthy spread of possible outputs, which you can see in the width of the curve above.
But after it was trained on its own output, this is what happened to the curve:
Distribution of A.I.-generated data when trained on its own output
It gets taller and narrower. As a result, the model becomes more and more likely to produce a smaller range of output, and the output can drift away from the original data.
Meanwhile, the tail ends of the curve — which contain the rare, unusual or surprising outcomes — fade away.
This is a telltale sign of model collapse: Rare data becomes even rarer.
If this process went unchecked, the curve would eventually become a spike:
Distribution of A.I.-generated data when trained on its own output
This was when all of the digits became identical, and the model completely collapsed.
Why it matters
This doesn’t mean generative A.I. will grind to a halt anytime soon.
The companies that make these tools are aware of these problems, and they will notice if their A.I. systems start to deteriorate in quality.
But it may slow things down. As existing sources of data dry up or become contaminated with A.I. “slop,” researchers say it makes it harder for newcomers to compete.
A.I.-generated words and images are already beginning to flood social media and the wider web. They’re even hiding in some of the data sets used to train A.I., the Rice researchers found.
“The web is becoming increasingly a dangerous place to look for your data,” said Sina Alemohammad, a graduate student at Rice who studied how A.I. contamination affects image models.
Big players will be affected, too. Computer scientists at N.Y.U. found that when there is a lot of A.I.-generated content in the training data, it takes more computing power to train A.I. — which translates into more energy and more money.
“Models won’t scale anymore as they should be scaling,” said Julia Kempe, the N.Y.U. professor who led this work.
The leading A.I. models already cost tens to hundreds of millions of dollars to train, and they consume staggering amounts of energy, so this can be a sizable problem.
‘A hidden danger’
Finally, there’s another threat posed by even the early stages of collapse: an erosion of diversity.
And it’s an outcome that could become more likely as companies try to avoid the glitches and “hallucinations” that often occur with A.I. data.
This is easiest to see when the data matches a form of diversity that we can visually recognize — people’s faces:
This set of A.I. faces was created by the same Rice researchers who produced the distorted faces above. This time, they tweaked the model to avoid visual glitches.
A grid of A.I.-generated faces showing variations in their poses, expressions, ages and races.
This is the output after they trained a new A.I. on the previous set of faces. At first glance, it may seem like the model changes worked: The glitches are gone.
After one generation of training on A.I. output, the A.I.-generated faces appear more similar.
After two generations …
After two generations of training on A.I. output, the A.I.-generated faces are less diverse than the original image.
After three generations …
After three generations of training on A.I. output, the A.I.-generated faces grow more similar.
After four generations, the faces all appeared to converge.
After four generations of training on A.I. output, the A.I.-generated faces appear almost identical.
This drop in diversity is “a hidden danger,” Mr. Alemohammad said. “You might just ignore it and then you don’t understand it until it’s too late.”
Just as with the digits, the changes are clearest when most of the data is A.I.-generated. With a more realistic mix of real and synthetic data, the decline would be more gradual.
But the problem is relevant to the real world, the researchers said, and will inevitably occur unless A.I. companies go out of their way to avoid their own output.
Related research shows that when A.I. language models are trained on their own words, their vocabulary shrinks and their sentences become less varied in their grammatical structure — a loss of “linguistic diversity.”
And studies have found that this process can amplify biases in the data and is more likely to erase data pertaining to minorities.
Ways out
Perhaps the biggest takeaway of this research is that high-quality, diverse data is valuable and hard for computers to emulate.
One solution, then, is for A.I. companies to pay for this data instead of scooping it up from the internet, ensuring both human origin and high quality.
OpenAI and Google have made deals with some publishers or websites to use their data to improve A.I. (The New York Times sued OpenAI and Microsoft last year, alleging copyright infringement. OpenAI and Microsoft say their use of the content is considered fair use under copyright law.)
Better ways to detect A.I. output would also help mitigate these problems.
Google and OpenAI are working on A.I. “watermarking” tools, which introduce hidden patterns that can be used to identify A.I.-generated images and text.
But watermarking text is challenging, researchers say, because these watermarks can’t always be reliably detected and can easily be subverted (they may not survive being translated into another language, for example).
A.I. slop is not the only reason that companies may need to be wary of synthetic data. Another problem is that there are only so many words on the internet.
Some experts estimate that the largest A.I. models have been trained on a few percent of the available pool of text on the internet. They project that these models may run out of public data to sustain their current pace of growth within a decade.
“These models are so enormous that the entire internet of images or conversations is somehow close to being not enough,” Professor Baraniuk said.
To meet their growing data needs, some companies are considering using today’s A.I. models to generate data to train tomorrow’s models. But researchers say this can lead to unintended consequences (such as the drop in quality or diversity that we saw above).
There are certain contexts where synthetic data can help A.I.s learn — for example, when output from a larger A.I. model is used to train a smaller one, or when the correct answer can be verified, like the solution to a math problem or the best strategies in games like chess or Go.
And new research suggests that when humans curate synthetic data (for example, by ranking A.I. answers and choosing the best one), it can alleviate some of the problems of collapse.
Companies are already spending a lot on curating data, Professor Kempe said, and she believes this will become even more important as they learn about the problems of synthetic data.
But for now, there’s no replacement for the real thing.
About the data
To produce the images of A.I.-generated digits, we followed a procedure outlined by researchers. We first trained a type of a neural network known as a variational autoencoder using a standard data set of 60,000 handwritten digits. We then trained a new neural network using only the A.I.-generated digits produced by the previous neural network, and repeated this process in a loop 30 times.
To create the statistical distributions of A.I. output, we used each generation’s neural network to create 10,000 drawings of digits. We then used the first neural network (the one that was trained on the original handwritten digits) to encode these drawings as a set of numbers, known as a “latent space” encoding. This allowed us to quantitatively compare the output of different generations of neural networks. For simplicity, we used the average value of this latent space encoding to generate the statistical distributions shown in the article.
Science
California’s summer COVID wave shows signs of waning. What are the numbers in your community?
There are some encouraging signs that California’s summer COVID wave might be leveling off.
That’s not to say the seasonal spike is in the rearview mirror just yet, however. Coronavirus levels in California’s wastewater remain “very high,” according to the U.S. Centers for Disease Control and Prevention, as they are in much of the country.
But while some COVID indicators are rising in the Golden State, others are starting to fall — a hint that the summer wave may soon start to decline.
Statewide, the rate at which coronavirus lab tests are coming back positive was 11.72% for the week that ended Sept. 6, the highest so far this season, and up from 10.8% the prior week. Still, viral levels in wastewater are significantly lower than during last summer’s peak.
The latest COVID hospital admission rate was 3.9 hospitalizations for every 100,000 residents. That’s a slight decline from 4.14 the prior week. Overall, COVID hospitalizations remain low statewide, particularly compared with earlier surges.
The number of newly admitted COVID hospital patients has declined slightly in Los Angeles County and Santa Clara County, but ticked up slightly up in Orange County. In San Francisco, some doctors believe the summer COVID wave is cresting.
“There are a few more people in the hospitals, but I think it’s less than last summer,” said Dr. Peter Chin-Hong, a UC San Francisco infectious diseases expert. “I feel like we are at a plateau.”
Those who are being hospitalized tend to be older people who didn’t get immunized against COVID within the last year, Chin-Hong said, and some have a secondary infection known as superimposed bacterial pneumonia.
Los Angeles County
In L.A. County, there are hints that COVID activity is either peaking or starting to decline. Viral levels in local wastewater are still rising, but the test positivity rate is declining.
For the week that ended Sept. 6, 12.2% of wastewater samples tested for COVID in the county were positive, down from 15.9% the prior week.
“Many indicators of COVID-19 activity in L.A. County declined in this week’s data,” the L.A. County Department of Public Health told The Times on Friday. “While it’s too early to know if we have passed the summer peak of COVID-19 activity this season, this suggests community transmission is slowing.”
Orange County
In Orange County, “we appear to be in the middle of a wave right now,” said Dr. Christopher Zimmerman, deputy medical director of the county’s Communicable Disease Control Division.
The test positivity rate has plateaued in recent weeks — it was 15.3% for the week that ended Sept. 6, up from 12.9% the prior week, but down from 17.9% the week before that.
COVID is still prompting people to seek urgent medical care, however. Countywide, 2.9% of emergency room visits were for COVID-like illness for the week that ended Sept. 6, the highest level this year, and up from 2.6% for the week that ended Aug. 30.
San Diego County
For the week that ended Sept. 6, 14.1% of coronavirus lab tests in San Diego County were positive for infection. That’s down from 15.5% the prior week, and 16.1% for the week that ended Aug. 23.
Ventura County
COVID is also still sending people to the emergency room in Ventura County. Countywide, 1.73% of ER patients for the week that ended Sept. 12 were there to seek treatment for COVID, up from 1.46% the prior week.
San Francisco
In San Francisco, the test positivity rate was 7.5% for the week that ended Sept. 7, down from 8.4% for the week that ended Aug. 31.
“COVID-19 activity in San Francisco remains elevated, but not as high as the previous summer’s peaks,” the local Department of Public Health said.
Silicon Valley
In Santa Clara County, the coronavirus remains at a “high” level in the sewershed of San José and Palo Alto.
Roughly 1.3% of ER visits for the week that ended Sunday were attributed to COVID in Santa Clara County, down from the prior week’s figure of 2%.
Science
Early adopters of ‘zone zero’ fared better in L.A. County fires, insurance-backed investigation finds
As the Eaton and Palisades fires rapidly jumped between tightly packed houses, the proactive steps some residents took to retrofit their homes with fire-resistant building materials and to clear flammable brush became a significant indicator of a home’s fate.
Early adopters who cleared vegetation and flammable materials within the first five feet of their houses’ walls — in line with draft rules for the state’s hotly debated “zone zero” regulations — fared better than those who didn’t, an on-the-ground investigation from the Insurance Institute for Business and Home Safety published Wednesday found.
Over a week in January, while the fires were still burning, the insurance team inspected more than 250 damaged, destroyed and unscathed homes in Altadena and Pacific Palisades.
On properties where the majority of zone zero land was covered in vegetation and flammable materials, the fires destroyed 27% of homes; On properties with less than a quarter of zone zero covered, only 9% were destroyed.
The Insurance Institute for Business and Home Safety, an independent research nonprofit funded by the insurance industry, performed similar investigations for Colorado’s 2012 Waldo Canyon fire, Hawaii’s 2023 Lahaina fire and California’s Tubbs, Camp and Woolsey fires of 2017 and 2018.
While a handful of recent studies have found homes with sparse vegetation in zone zero were more likely to survive fires, skeptics say it does not yet amount to a scientific consensus.
Travis Longcore, senior associate director and an adjunct professor at the UCLA Institute of the Environment and Sustainability, cautioned that the insurance nonprofit’s results are only exploratory: The team did not analyze whether other factors, such as the age of the homes, were influencing their zone zero analysis, and how the nonprofit characterizes zone zero for its report, he noted, does not exactly mirror California’s draft regulations.
Meanwhile, Michael Gollner, an associate professor of mechanical engineering at UC Berkeley who studies how wildfires destroy and damage homes, noted that the nonprofit’s sample does not perfectly represent the entire burn areas, since the group focused specifically on damaged properties and were constrained by the active firefight.
Nonetheless, the nonprofit’s findings help tie together growing evidence of zone zero’s effectiveness from tests in the lab — aimed at identifying the pathways fire can use to enter a home — with the real-world analyses of which measures protected homes in wildfires, Gollner said.
A recent study from Gollner looking at more than 47,000 structures in five major California fires (which did not include the Eaton and Palisades fires) found that of the properties that removed vegetation from zone zero, 37% survived, compared with 20% that did not.
Once a fire spills from the wildlands into an urban area, homes become the primary fuel. When a home catches fire, it increases the chance nearby homes burn, too. That is especially true when homes are tightly packed.
When looking at California Department of Forestry and Fire Protection data for the entirety of the two fires, the insurance team found that “hardened” homes in Altadena and the Palisades that had noncombustable roofs, fire-resistant siding, double-pane windows and closed eaves survived undamaged at least 66% of the time, if they were at least 20 feet away from other structures.
But when the distance was less than 10 feet, only 45% of the hardened homes escaped with no damage.
“The spacing between structures, it’s the most definitive way to differentiate what survives and what doesn’t,” said Roy Wright, president and chief executive of the Insurance Institute for Business and Home Safety. At the same time, said Wright, “it’s not feasible to change that.”
Looking at steps that residents are more likely to be able to take, the insurance nonprofit found that the best approach is for homeowners to apply however many home hardening and defensible space measures that they can. Each one can shave a few percentage points off the risk of a home burning, and combined, the effect can be significant.
As for zone zero, the insurance team found a number of examples of how vegetation and flammable materials near a home could aid the destruction of a property.
At one home, embers appeared to have ignited some hedges a few feet away from the structure. That heat was enough to shatter a single pane window, creating the perfect opportunity for embers to enter and burn the house from the inside out. It miraculously survived.
At others, embers from the blazes landed on trash and recycling bins close to the houses, sometimes burning holes through the plastic lids and igniting the material inside. In one instance, the fire in the bin spread to a nearby garage door, but the house was spared.
Wooden decks and fences were also common accomplices that helped embers ignite a structure.
California’s current zone zero draft regulations take some of those risks into account. They prohibit wooden fences within the first five feet of a home; the state’s zone zero committee is also considering whether to prohibit virtually all vegetation in the zone or to just limit it (regardless, well-maintained trees are allowed).
On the other hand, the draft regulations do not prohibit keeping trash bins in the zone, which the committee determined would be difficult to enforce. They also do not mandate homeowners replace wooden decks.
The controversy around the draft regulations center around the proposal to remove virtually all healthy vegetation, including shrubs and grasses, from the zone.
Critics argue that, given the financial burden zone zero would place on homeowners, the state should instead focus on measures with lower costs and a significant proven benefit.
“A focus on vegetation is misguided,” said David Lefkowith, president of the Mandeville Canyon Assn.
At its most recent zone zero meeting, the Board of Forestry and Fire Protection directed staff to further research the draft regulations’ affordability.
“As the Board and subcommittee consider which set of options best balance safety, urgency, and public feasibility, we are also shifting our focus to implementation and looking to state leaders to identify resources for delivering on this first-in-the-nation regulation,” Tony Andersen, executive officer of the board, said in a statement. “The need is urgent, but we also want to invest the time necessary to get this right.”
Home hardening and defensible space are just two of many strategies used to protect lives and property. The insurance team suspects that many of the close calls they studied in the field — homes that almost burned but didn’t — ultimately survived thanks to firefighters who stepped in. Wildfire experts also recommend programs to prevent ignitions in the first place and to manage wildlands to prevent intense spread of a fire that does ignite.
For Wright, the report is a reminder of the importance of community. The fate of any individual home is tied to that of those nearby — it takes a whole neighborhood hardening their homes and maintaining their lawns to reach herd immunity protection against fire’s contagious spread.
“When there is collective action, it changes the outcomes,” Wright said. “Wildfire is insidious. It doesn’t stop at the fence line.”
Science
Notorious ‘winter vomiting bug’ rising in California. A new norovirus strain could make it worse
The dreaded norovirus — the “vomiting bug” that often causes stomach flu symptoms — is climbing again in California, and doctors warn that a new subvariant could make even more people sick this season.
In L.A. County, concentrations of norovirus are already on the rise in wastewater, indicating increased circulation of the disease, the local Department of Public Health told the Los Angeles Times.
Norovirus levels are increasing across California, and the rise is especially notable in the San Francisco Bay Area and L.A., according to the California Department of Public Health.
And the rate at which norovirus tests are confirming infection is rising nationally and in the Western U.S. For the week that ended Nov. 22, the test positivity rate nationally was 11.69%, up from 8.66% two months earlier. In the West, it was even worse: 14.08%, up from 9.59%, according to the U.S. Centers for Disease Control and Prevention.
Norovirus is extraordinarily contagious, and is America’s leading cause of vomiting and diarrhea, according to the CDC. Outbreaks typically happen in the cooler months between November and April.
Clouding the picture is the recent emergence of a new norovirus strain — GII.17. Such a development can result in 50% more norovirus illness than typical, the CDC says.
“If your immune system isn’t used to something that comes around, a lot of people get infected,” said Dr. Peter Chin-Hong, an infectious diseases expert at UC San Francisco.
During the 2024-25 winter season, GII.17 overthrew the previous dominant norovirus strain, GII.4, that had been responsible for more than half of national norovirus outbreaks over the preceding decade. The ancestor of the GII.17 strain probably came from a subvariant that triggered an outbreak in Romania in 2021, according to CDC scientists.
GII.17 vaulted in prominence during last winter’s norovirus surge and was ultimately responsible for about 75% of outbreaks of the disease nationally.
The strain’s emergence coincided with a particularly bad year for norovirus, one that started unusually early in October 2024, peaked earlier than normal the following January and stretched into the summer, according to CDC scientists writing in the journal Emerging Infectious Diseases.
During the three prior seasons, when GII.4 was dominant, norovirus activity had been relatively stable, Chin-Hong said.
Norovirus can cause substantial disruptions — as many parents know all too well. An elementary school in Massachusetts was forced to cancel all classes on Thursday and Friday because of the “high volume of stomach illness cases,” which was suspected to be driven by norovirus.
More than 130 students at Roberts Elementary School in Medford, Mass., were absent Wednesday, and administrators said there probably wouldn’t be a “reasonable number of students and staff” to resume classes Friday. A company was hired to perform a deep clean of the school’s classrooms, doorknobs and kitchen equipment.
Some places in California, however, aren’t seeing major norovirus activity so far this season. Statewide, while norovirus levels in wastewater are increasing, they still remain low, the California Department of Public Health said.
There have been 32 lab-confirmed norovirus outbreaks reported to the California Department of Public Health so far this year. Last year, there were 69.
Officials caution the numbers don’t necessarily reflect how bad norovirus is in a particular year, as many outbreaks are not lab-confirmed, and an outbreak can affect either a small or large number of people.
Between Aug. 1 and Nov. 13, there were 153 norovirus outbreaks publicly reported nationally, according to the CDC. During the same period last year, there were 235.
UCLA hasn’t reported an increase in the number of norovirus tests ordered, nor has it seen a significant increase in test positivity rates. Chin-Hong said he likewise hasn’t seen a big increase at UC San Francisco.
“Things are relatively still stable clinically in California, but I think it’s just some amount of time before it comes here,” Chin-Hong said.
In a typical year, norovirus causes 2.27 million outpatient clinic visits, mostly young children; 465,000 emergency department visits, 109,000 hospitalizations, and 900 deaths, mostly among seniors age 65 and older.
People with severe ongoing vomiting, profound diarrhea and dehydration may need to seek medical attention to get hydration intravenously.
“Children who are dehydrated may cry with few or no tears and be unusually sleepy or fussy,” the CDC says. Sports drinks can help with mild dehydration, but what may be more helpful are oral rehydration fluids that can be bought over the counter.
Children under the age of 5 and adults 85 and older are most likely to need to visit an emergency room or clinic because of norovirus, and should not hesitate to seek care, experts say.
“Everyone’s at risk, but the people who you worry about, the ones that we see in the hospital, are the very young and very old,” Chin-Hong said.
Those at highest risk are babies, because it doesn’t take much to cause potentially serious problems. Newborns are at risk for necrotizing enterocolitis, a life-threatening inflammation of the intestine that virtually only affects new babies, according to the National Library of Medicine.
Whereas healthy people generally clear the virus in one to three days, immune-compromised individuals can continue to have diarrhea for a long time “because their body’s immune system can’t neutralize the virus as effectively,” Chin-Hong said.
The main way people get norovirus is by accidentally drinking water or eating food contaminated with fecal matter, or touching a contaminated surface and then placing their fingers in their mouths.
People usually develop symptoms 12 to 48 hours after they’re exposed to the virus.
Hand sanitizer does not work well against norovirus — meaning that proper handwashing is vital, experts say.
People should lather their hands with soap and scrub for at least 20 seconds, including the back of their hands, between their fingers and under their nails, before rinsing and drying, the CDC says.
One helpful way to keep track of time is to hum the “Happy Birthday” song from beginning to end twice, the CDC says. Chin-Hong says his favorite is the chorus of Kelly Clarkson’s “Since U Been Gone.”
If you’re living with someone with norovirus, “you really have to clean surfaces and stuff if they’re touching it,” Chin-Hong said. Contamination is shockingly easy. Even just breathing out little saliva droplets on food that is later consumed by someone else can spread infection.
Throw out food that might be contaminated with norovirus, the CDC says. Noroviruses are relatively resistant to heat and can survive temperatures as high as 145 degrees.
Norovirus is so contagious that even just 10 viral particles are enough to cause infection. By contrast, it takes ingesting thousands of salmonella particles to get sick from that bacterium.
People are most contagious when they are sick with norovirus — but they can still be infectious even after they feel better, the CDC says.
The CDC advises staying home for 48 hours after infection. Some studies have even shown that “you can still spread norovirus for two weeks or more after you feel better,” according to the CDC.
The CDC also recommends washing laundry in hot water.
Besides schools, other places where norovirus can spread quickly are cruise ships, day-care centers and prisons, Chin-Hong said.
The most recent norovirus outbreak on a cruise ship reported by the CDC is on the ship AIDAdiva, which set sail on Nov. 10 from Germany. Out of 2,007 passengers on board, 4.8% have reported being ill. The outbreak was first reported on Nov. 30 following stops that month at the Isle of Portland, England; Halifax, Canada; Boston; New York City; Charleston, S.C.; and Miami.
According to CruiseMapper, the ship was set to make stops in Puerto Vallarta on Saturday, San Diego on Tuesday, Los Angeles on Wednesday, Santa Barbara on Thursday and San Francisco between Dec. 19-21.
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Cleveland, OH7 days agoMan shot, killed at downtown Cleveland nightclub: EMS
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World6 days ago
Chiefs’ offensive line woes deepen as Wanya Morris exits with knee injury against Texans
