What to Know about the Fast-Spreading Delta Variant
A highly contagious form of the coronavirus known as the Delta variant is spreading rapidly and becoming increasingly prevalent around the world. First identified in India in December, Delta has now been identified in 111 countries.
In the United States, the variant now accounts for 83% of sequenced COVID-19 cases, said Rochelle Walensky, director of the Centers for Disease Control and Prevention, at a July 20 Senate hearing. In May, Delta was responsible for just 3% of U.S. cases. The World Health Organization projects that Delta will become the dominant variant globally over the coming months.
So, how worried should you be about the Delta variant? We asked experts some common questions about Delta.
What is a variant?
To understand Delta, it's helpful to first understand what a variant is. When a virus infects a person, it gets into your cells and makes a copy of its genome so it can replicate and spread throughout your body.
In the process of making new copies of itself, the virus can make a mistake in its genetic code. Because viruses are replicating all the time, these mistakes — also called mutations — happen pretty often. A new variant emerges when a virus acquires one or more new mutations and starts spreading within a population.
There are thousands of SARS-CoV-2 variants, but most of them don't substantially change the way the virus behaves. The variants that scientists are most interested in are known as variants of concern. These are versions of the virus with mutations that allow the virus to spread more easily, evade vaccines, or cause more severe disease.
"The vast majority of the mutations that have accumulated in SARS-CoV-2 don't change the biology as far as we're concerned," said Jennifer Surtees, a biochemist at the University of Buffalo who's studying the coronavirus. "But there have been a handful of key mutations and combinations of mutations that have led to what we're now calling variants of concern."
One of those variants of concern is Delta, which is now driving many new COVID-19 infections.
Why is the Delta variant so concerning?
"The reason why the Delta variant is concerning is because it's causing an increase in transmission," said Alba Grifoni, an infectious disease researcher at the La Jolla Institute for Immunology. "The virus is spreading faster and people — particularly those who are not vaccinated yet — are more prone to exposure."
The Delta variant has a few key mutations that make it better at attaching to our cells and evading the neutralizing antibodies in our immune system. These mutations have changed the virus enough to make it more than twice as contagious as the original SARS-CoV-2 virus that emerged in Wuhan and about 50% more contagious than the Alpha variant, previously known as B.1.1.7, or the U.K. variant.
These mutations were previously seen in other variants on their own, but it's their combination that makes Delta so much more infectious.
Do vaccines work against the Delta variant?
The good news is, the COVID-19 vaccines made by AstraZeneca, Johnson & Johnson, Moderna, and Pfizer still work against the Delta variant. They remain more than 90% effective at preventing hospitalizations and death due to Delta. While they're slightly less protective against disease symptoms, they're still very effective at preventing severe illness caused by the Delta variant.
"They're not as good as they were against the prior strains, but they're holding up pretty well," said Eric Topol, a physician and director of the Scripps Translational Research Institute, during a July 19 briefing for journalists.
Because Delta is better at evading our immune systems, it's likely causing more breakthrough infections — COVID-19 cases in people who are vaccinated. However, breakthrough infections were expected before the Delta variant became widespread. No vaccine is 100% effective, so breakthrough infections can happen with other vaccines as well. Experts say the COVID-19 vaccines are still working as expected, even if breakthrough infections occur. The majority of these infections are asymptomatic or cause only mild symptoms.
Should vaccinated people worry about the Delta variant?
Vaccines train our immune systems to protect us against infection. They do this by spurring the production of antibodies, which stick around in our bodies to help fight off a particular pathogen in case we ever come into contact with it.
But even if the new Delta variant slips past our neutralizing antibodies, there's another component of our immune system that can help overtake the virus: T cells. Studies are showing that the COVID-19 vaccines also galvanize T cells, which help limit disease severity in people who have been vaccinated.
"While antibodies block the virus and prevent the virus from infecting cells, T cells are able to attack cells that have already been infected," Grifoni said. In other words, T cells can prevent the infection from spreading to more places in the body. A study published July 1 by Grifoni and her colleagues found that T cells were still able to recognize mutated forms of the virus — further evidence that our current vaccines are effective against Delta.
Can fully vaccinated people spread the Delta variant?
Previously, scientists believed it was unlikely for fully vaccinated individuals with asymptomatic infections to spread Covid-19. But the Delta variant causes the virus to make so many more copies of itself inside the body, and high viral loads have been found in the respiratory tracts of people who are fully vaccinated. This suggests that vaccinated people may be able to spread the Delta variant to some degree.
If you have COVID-19 symptoms, even if you're fully vaccinated, you should get tested and isolate from friends and family because you could spread the virus.
What risk does Delta pose to unvaccinated people?
The Delta variant is behind a surge in cases in communities with low vaccination rates, and unvaccinated Americans currently account for 97% of hospitalizations due to COVID-19, according to Walensky. The best thing you can do right now to prevent yourself from getting sick is to get vaccinated.
Gigi Gronvall, an immunologist and senior scholar at the Johns Hopkins Center for Health Security, said in this week's "Making Sense of Science" podcast that it's especially important to get all required doses of the vaccine in order to have the best protection against the Delta variant. "Even if it's been more than the allotted time that you were told to come back and get the second, there's no time like the present," she said.
With more than 3.6 billion COVID-19 doses administered globally, the vaccines have been shown to be incredibly safe. Serious adverse effects are rare, although scientists continue to monitor for them.
Being vaccinated also helps prevent the emergence of new and potentially more dangerous variants. Viruses need to infect people in order to replicate, and variants emerge because the virus continues to infect more people. More infections create more opportunities for the virus to acquire new mutations.
Surtees and others worry about a scenario in which a new variant emerges that's even more transmissible or resistant to vaccines. "This is our window of opportunity to try to get as many people vaccinated as possible and get people protected so that so that the virus doesn't evolve to be even better at infecting people," she said.
Does Delta cause more severe disease?
While hospitalizations and deaths from COVID-19 are increasing again, it's not yet clear whether Delta causes more severe illness than previous strains.
How can we protect unvaccinated children from the Delta variant?
With children 12 and under not yet eligible for the COVID-19 vaccine, kids are especially vulnerable to the Delta variant. One way to protect unvaccinated children is for parents and other close family members to get vaccinated.
It's also a good idea to keep masks handy when going out in public places. Due to risk Delta poses, the American Academy of Pediatrics issued new guidelines July 19 recommending that all staff and students over age 2 wear face masks in school this fall, even if they have been vaccinated.
Parents should also avoid taking their unvaccinated children to crowded, indoor locations and make sure their kids are practicing good hand-washing hygiene. For children younger than 2, limit visits with friends and family members who are unvaccinated or whose vaccination status is unknown and keep up social distancing practices while in public.
While there's no evidence yet that Delta increases disease severity in children, parents should be mindful that in some rare cases, kids can get a severe form of the disease.
"We're seeing more children getting sick and we're seeing some of them get very sick," Surtees said. "Those children can then pass on the virus to other individuals, including people who are immunocompromised or unvaccinated."
Researchers probe extreme gene therapy for severe alcoholism
Story by Freethink
A single shot — a gene therapy injected into the brain — dramatically reduced alcohol consumption in monkeys that previously drank heavily. If the therapy is safe and effective in people, it might one day be a permanent treatment for alcoholism for people with no other options.
The challenge: Alcohol use disorder (AUD) means a person has trouble controlling their alcohol consumption, even when it is negatively affecting their life, job, or health.
In the U.S., more than 10 percent of people over the age of 12 are estimated to have AUD, and while medications, counseling, or sheer willpower can help some stop drinking, staying sober can be a huge struggle — an estimated 40-60 percent of people relapse at least once.
A team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
According to the CDC, more than 140,000 Americans are dying each year from alcohol-related causes, and the rate of deaths has been rising for years, especially during the pandemic.
The idea: For occasional drinkers, alcohol causes the brain to release more dopamine, a chemical that makes you feel good. Chronic alcohol use, however, causes the brain to produce, and process, less dopamine, and this persistent dopamine deficit has been linked to alcohol relapse.
There is currently no way to reverse the changes in the brain brought about by AUD, but a team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
To find out, they tested it in heavy-drinking monkeys — and the animals’ alcohol consumption dropped by 90% over the course of a year.
How it works: The treatment centers on the protein GDNF (“glial cell line-derived neurotrophic factor”), which supports the survival of certain neurons, including ones linked to dopamine.
For the new study, a harmless virus was used to deliver the gene that codes for GDNF into the brains of four monkeys that, when they had the option, drank heavily — the amount of ethanol-infused water they consumed would be equivalent to a person having nine drinks per day.
“We targeted the cell bodies that produce dopamine with this gene to increase dopamine synthesis, thereby replenishing or restoring what chronic drinking has taken away,” said co-lead researcher Kathleen Grant.
To serve as controls, another four heavy-drinking monkeys underwent the same procedure, but with a saline solution delivered instead of the gene therapy.
The results: All of the monkeys had their access to alcohol removed for two months following the surgery. When it was then reintroduced for four weeks, the heavy drinkers consumed 50 percent less compared to the control group.
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
The researchers then took the alcohol away for another four weeks, before giving it back for four. They repeated this cycle for a year, and by the end of it, the treated monkeys’ consumption had fallen by more than 90 percent compared to the controls.
“Drinking went down to almost zero,” said Grant. “For months on end, these animals would choose to drink water and just avoid drinking alcohol altogether. They decreased their drinking to the point that it was so low we didn’t record a blood-alcohol level.”
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
Looking ahead: Dopamine is involved in a lot more than addiction, so more research is needed to not only see if the results translate to people but whether the gene therapy leads to any unwanted changes to mood or behavior.
Because the therapy requires invasive brain surgery and is likely irreversible, it’s unlikely to ever become a common treatment for alcoholism — but it could one day be the only thing standing between people with severe AUD and death.
“[The treatment] would be most appropriate for people who have already shown that all our normal therapeutic approaches do not work for them,” said Grant. “They are likely to create severe harm or kill themselves or others due to their drinking.”
This article originally appeared on Freethink, home of the brightest minds and biggest ideas of all time.
Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the book The Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
AI's accountability
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Google, for example, recently updated its privacy policy to say that anything on the public internet will be used as training data. “Obviously, technology companies have to respond to their economic interests, so their decisions are not necessarily going to be the best for society and for the environment,” Oliver says. “And that’s why we need strong research institutions and civil society institutions to push for actions.” ELLIS also advocates for data centers to be built in locations where the energy can be produced sustainably.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.