Coronavirus Risk Calculators: What You Need to Know
People in my family seem to develop every ailment in the world, including feline distemper and Dutch elm disease, so I naturally put fingers to keyboard when I discovered that COVID-19 risk calculators now exist.
"It's best to look at your risk band. This will give you a more useful insight into your personal risk."
But the results – based on my answers to questions -- are bewildering.
A British risk calculator developed by the Nexoid software company declared I have a 5 percent, or 1 in 20, chance of developing COVID-19 and less than 1 percent risk of dying if I get it. Um, great, I think? Meanwhile, 19 and Me, a risk calculator created by data scientists, says my risk of infection is 0.01 percent per week, or 1 in 10,000, and it gave me a risk score of 44 out of 100.
Confused? Join the club. But it's actually possible to interpret numbers like these and put them to use. Here are five tips about using coronavirus risk calculators:
1. Make Sure the Calculator Is Designed For You
Not every COVID-19 risk calculator is designed to be used by the general public. Cleveland Clinic's risk calculator, for example, is only a tool for medical professionals, not sick people or the "worried well," said Dr. Lara Jehi, Cleveland Clinic's chief research information officer.
Unfortunately, the risk calculator's web page fails to explicitly identify its target audience. But there are hints that it's not for lay people such as its references to "platelets" and "chlorides."
The 19 and Me or the Nexoid risk calculators, in contrast, are both designed for use by everyone, as is a risk calculator developed by Emory University.
2. Take a Look at the Calculator's Privacy Policy
COVID-19 risk calculators ask for a lot of personal information. The Nexoid calculator, for example, wanted to know my age, weight, drug and alcohol history, pre-existing conditions, blood type and more. It even asked me about the prescription drugs I take.
It's wise to check the privacy policy and be cautious about providing an email address or other personal information. Nexoid's policy says it provides the information it gathers to researchers but it doesn't release IP addresses, which can reveal your location in certain circumstances.
John-Arne Skolbekken, a professor and risk specialist at Norwegian University of Science and Technology, entered his own data in the Nexoid calculator after being contacted by LeapsMag for comment. He noted that the calculator, among other things, asks for information about use of recreational drugs that could be illegal in some places. "I have given away some of my personal data to a company that I can hope will not misuse them," he said. "Let's hope they are trustworthy."
The 19 and Me calculator, by contrast, doesn't gather any data from users, said Cindy Hu, data scientist at Mathematica, which created it. "As soon as the window is closed, that data is gone and not captured."
The Emory University risk calculator, meanwhile, has a long privacy policy that states "the information we collect during your assessment will not be correlated with contact information if you provide it." However, it says personal information can be shared with third parties.
3. Keep an Eye on Time Horizons
Let's say a risk calculator says you have a 1 percent risk of infection. That's fairly low if we're talking about this year as a whole, but it's quite worrisome if the risk percentage refers to today and jumps by 1 percent each day going forward. That's why it's helpful to know exactly what the numbers mean in terms of time.
Unfortunately, this information isn't always readily available. You may have to dig around for it or contact a risk calculator's developers for more information. The 19 and Me calculator's risk percentages refer to this current week based on your behavior this week, Hu said. The Nexoid calculator, by contrast, has an "infinite timeline" that assumes no vaccine is developed, said Jonathon Grantham, the company's managing director. But your results will vary over time since the calculator's developers adjust it to reflect new data.
When you use a risk calculator, focus on this question: "How does your risk compare to the risk of an 'average' person?"
4. Focus on the Big Picture
The Nexoid calculator gave me numbers of 5 percent (getting COVID-19) and 99.309 percent (surviving it). It even provided betting odds for gambling types: The odds are in favor of me not getting infected (19-to-1) and not dying if I get infected (144-to-1).
However, Grantham told me that these numbers "are not the whole story." Instead, he said, "it's best to look at your risk band. This will give you a more useful insight into your personal risk." Risk bands refer to a segmentation of people into five categories, from lowest to highest risk, according to how a person's result sits relative to the whole dataset.
The Nexoid calculator says I'm in the "lowest risk band" for getting COVID-19, and a "high risk band" for dying of it if I get it. That suggests I'd better stay in the lowest-risk category because my pre-existing risk factors could spell trouble for my survival if I get infected.
Michael J. Pencina, a professor and biostatistician at Duke University School of Medicine, agreed that focusing on your general risk level is better than focusing on numbers. When you use a risk calculator, he said, focus on this question: "How does your risk compare to the risk of an 'average' person?"
The 19 and Me calculator, meanwhile, put my risk at 44 out of 100. Hu said that a score of 50 represents the typical person's risk of developing serious consequences from another disease – the flu.
5. Remember to Take Action
Hu, who helped develop the 19 and Me risk calculator, said it's best to use it to "understand the relative impact of different behaviors." As she noted, the calculator is designed to allow users to plug in different answers about their behavior and immediately see how their risk levels change.
This information can help us figure out if we should change the way we approach the world by, say, washing our hands more or avoiding more personal encounters.
"Estimation of risk is only one part of prevention," Pencina said. "The other is risk factors and our ability to reduce them." In other words, odds, percentages and risk bands can be revealing, but it's what we do to change them that matters.
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.