The electric vehicle tax credit is gone, and Tesla's new, more affordable models don't quite close the gap. For nearly two decades, CEO Elon Musk has promised Tesla would make a more affordable electric vehicle, to, as he put it in 2006, "help expedite the move from a mine-and-burn hydrocarbon economy towards a solar electric economy." On Tuesday, Tesla announced a new Model Y and Model 3 Standard, versions of its popular compact SUV and sedan stripped of a few higher-end touches and features to bring the price down to $39,990 and $36,990, respectively. They're both about $5,000 cheaper than the Premium variants, which goes a ways--but not all the way--toward recouping the $7,500 tax credit canceled by the GOP-led Congress this past summer . The price point also puts Tesla's newest models firmly in the "more affordable" EV camp.

Nigerian Pidgin is an English-derived contact language and is traditionally an oral language, spoken by approximately 100 million people. No orthographic standard has yet been adopted, and thus the few available Pidgin datasets that exist are characterised by noise in the form of orthographic variations. This contributes to under-performance of models in critical NLP tasks. The current work is the first to describe various types of orthographic variations commonly found in Nigerian Pidgin texts, and model this orthographic variation. The variations identified in the dataset form the basis of a phonetic-theoretic framework for word editing, which is used to generate orthographic variations to augment training data. We test the effect of this data augmentation on two critical NLP tasks: machine translation and sentiment analysis. The proposed variation generation framework augments the training data with new orthographic variants which are relevant for the test set but did not occur in the training set originally. Our results demonstrate the positive effect of augmenting the training data with a combination of real texts from other corpora as well as synthesized orthographic variation, resulting in performance improvements of 2.1 points in sentiment analysis and 1.4 BLEU points in translation to English.

During the COVID-19 pandemic, a major driver of new surges has been the emergence of new variants. When a new variant emerges in one or more countries, other nations monitor its spread in preparation for its potential arrival. The impact of the variant and the timing of epidemic peaks in a country highly depend on when the variant arrives. The current methods for predicting the spread of new variants rely on statistical modeling, however, these methods work only when the new variant has already arrived in the region of interest and has a significant prevalence. The question arises: Can we predict when (and if) a variant that exists elsewhere will arrive in a given country and reach a certain prevalence? We propose a variant-dynamics-informed Graph Neural Network (GNN) approach. First, We derive the dynamics of variant prevalence across pairs of regions (countries) that applies to a large class of epidemic models. The dynamics suggest that ratios of variant proportions lead to simpler patterns. Therefore, we use ratios of variant proportions along with some parameters estimated from the dynamics as features in a GNN. We develop a benchmarking tool to evaluate variant emergence prediction over 87 countries and 36 variants. We leverage this tool to compare our GNN-based approach against our dynamics-only model and a number of machine learning models. Results show that the proposed dynamics-informed GNN method retrospectively outperforms all the baselines, including the currently pervasive framework of Physics-Informed Neural Networks (PINNs) that incorporates the dynamics in the loss function.

Researchers often use simulations when designing new algorithms, since testing ideas in the real world can be both costly and risky. But since it's impossible to capture every detail of a complex system in a simulation, they typically collect a small amount of real data that they replay while simulating the components they want to study. Known as trace-driven simulation (the small pieces of real data are called traces), this method sometimes results in biased outcomes. This means researchers might unknowingly choose an algorithm that is not the best one they evaluated, and which will perform worse on real data than the simulation predicted that it should. MIT researchers have developed a new method that eliminates this source of bias in trace-driven simulation.

Researchers from the University of Florida (UF) are making use of the funding from the school to assist in resolving the continuing COVID-19 pandemic. All around the world, the COVID-19 pandemic has become a never-ending challenge as new variants keep taking over as the previous ones die down. This is making the whole trend of the pandemic unpredictable and healthcare professionals and researchers want to dig deeper into the pandemic which has gripped the entire world over the past two years. The virus is always one step ahead, and whenever it feels like it's under control a new wave takes over which is more severe and contagious. The research team is all geared up to create an AI algorithm to track the virus and calculate new variants with precision. Publicly available global data will be leveraged to train the AI algorithm on the genetic structure of the virus.

In contrast, variants of concern are those that have also demonstrated such an increase in transmissibility, more severe disease presentation, or a reduction in the effectiveness of public health measures against them. To date, the five coronavirus variants to be classified as being "of concern" have been Alpha, Beta, Gamma, Delta and Omicron. An issue with this approach to classification is that identification of a new variant of concern inherently involves a prior period of monitoring the impact of the virus on the populace -- during which time it can be challenging to determine the appropriate public health response. Accordingly, methods to assess the risk posed by new variants in real-time have the potential to be of considerable benefit.

New variants have changed the face of the pandemic. What will the virus do next? ![Figure][1] CREDITS: (GRAPHIC) N. DESAI/ SCIENCE ; (DATA) NEXTSTRAIN; GISAID Edward Holmes does not like making predictions, but last year he hazarded a few. Again and again, people had asked Holmes, an expert on viral evolution at the University of Sydney, how he expected SARS-CoV-2 to change. In May 2020, 5 months into the pandemic, he started to include a slide with his best guesses in his talks. The virus would probably evolve to avoid at least some human immunity, he suggested. But it would likely make people less sick over time, he said, and there would be little change in its infectivity. In short, it sounded like evolution would not play a major role in the pandemic's near future. “A year on I've been proven pretty much wrong on all of it,” Holmes says. Well, not all: SARS-CoV-2 did evolve to better avoid human antibodies. But it has also become a bit more virulent and a lot more infectious, causing more people to fall ill. That has had an enormous influence on the course of the pandemic. The Delta strain circulating now—one of four “variants of concern” identified by the World Health Organization, along with four “variants of interest”—is so radically different from the virus that appeared in Wuhan, China, in late 2019 that many countries have been forced to change their pandemic planning. Governments are scrambling to accelerate vaccination programs while prolonging or even reintroducing mask wearing and other public health measures. As to the goal of reaching herd immunity—vaccinating so many people that the virus simply has nowhere to go—“With the emergence of Delta, I realized that it's just impossible to reach that,” says Müge Çevik, an infectious disease specialist at the University of St. Andrews. Yet the most tumultuous period in SARS-CoV-2's evolution may still be ahead of us, says Aris Katzourakis, an evolutionary biologist at the University of Oxford. There's now enough immunity in the human population to ratchet up an evolutionary competition, pressuring the virus to adapt further. At the same time, much of the world is still overwhelmed with infections, giving the virus plenty of chances to replicate and throw up new mutations. Predicting where those worrisome factors will lead is just as tricky as it was a year and a half ago, however. “We're much better at explaining the past than predicting the future,” says Andrew Read, an evolutionary biologist at Pennsylvania State University, University Park. Evolution, after all, is driven by random mutations, which are impossible to predict. “It's very, very tricky to know what's possible, until it happens,” Read says. “It's not physics. It doesn't happen on a billiard table.” Still, experience with other viruses gives evolutionary biologists some clues about where SARS-CoV-2 may be headed. The courses of past outbreaks show the coronavirus could well become even more infectious than Delta is now, Read says: “I think there's every expectation that this virus will continue to adapt to humans and will get better and better at us.” Far from making people less sick, it could also evolve to become even deadlier, as some previous viruses including the 1918 flu have. And although COVID-19 vaccines have held up well so far, history shows the virus could evolve further to elude their protective effect—although a recent study in another coronavirus suggests that could take many years, which would leave more time to adapt vaccines to the changing threat. Holmes himself uploaded one of the first SARS-CoV-2 genomes to the internet on 10 January 2020. Since then, more than 2 million genomes have been sequenced and published, painting an exquisitely detailed picture of a changing virus. “I don't think we've ever seen that level of precision in watching an evolutionary process,” Holmes says. Making sense of the endless stream of mutations is complicated. Each is just a tiny tweak in the instructions for how to make proteins. Which mutations end up spreading depends on how the viruses carrying those tweaked proteins fare in the real world. The vast majority of mutations give the virus no advantage at all, and identifying the ones that do is difficult. There are obvious candidates, such as mutations that change the part of the spike protein—which sits on the surface of the virus—that binds to human cells. But changes elsewhere in the genome may be just as crucial—yet are harder to interpret. Some genes' functions aren't even clear, let alone what a change in their sequence could mean. The impact of any one change on the virus' fitness also depends on other changes it has already accumulated. That means scientists need real-world data to see which variants appear to be taking off. Only then can they investigate, in cell cultures and animal experiments, what might explain that viral success. The most eye-popping change in SARS-CoV-2 so far has been its improved ability to spread between humans. At some point early in the pandemic, SARS-CoV-2 acquired a mutation called D614G that made it a bit more infectious. That version spread around the world; almost all current viruses are descended from it. Then in late 2020, scientists identified a new variant, now called Alpha, in patients in Kent, U.K., that was about 50% more transmissible. Delta, first seen in India and now conquering the world, is another 40% to 60% more transmissible than Alpha. Read says the pattern is no surprise. “The only way you could not get infectiousness rising would be if the virus popped into humans as perfect at infecting humans as it could be, and the chance of that happening is incredibly small,” he says. But Holmes was startled. “This virus has gone up three notches in effectively a year and that, I think, was the biggest surprise to me,” Holmes says. “I didn't quite appreciate how much further the virus could get.” Bette Korber at Los Alamos National Laboratory and her colleagues first suggested that D614G, the early mutation, was taking over because it made the virus better at spreading. She says skepticism about the virus' ability to evolve was common in the early days of the pandemic, with some researchers saying D614G's apparent advantage might be sheer luck. “There was extraordinary resistance in the scientific community to the idea this virus could evolve as the pandemic grew in seriousness in spring of 2020,” Korber says. ![Figure][1] CREDITS: (GRAPHIC) N. DESAI/ SCIENCE ; (DATA) NEXTSTRAIN; GISAID Researchers had never watched a completely novel virus spread so widely and evolve in humans, after all. “We're used to dealing with pathogens that have been in humanity for centuries, and their evolutionary course is set in the context of having been a human pathogen for many, many years,” says Jeremy Farrar, head of the Wellcome Trust. Katzourakis agrees. “This may have affected our priors and conditioned many to think in a particular way,” he says. Another, more practical problem is that real-world advantages for the virus don't always show up in cell culture or animal models. “There is no way anyone would have noticed anything special about Alpha from laboratory data alone,” says Christian Drosten, a virologist at the Charité University Hospital in Berlin. He and others are still figuring out what, at the molecular level, gives Alpha and Delta an edge. Alpha seems to bind more strongly to the human ACE2 receptor, the virus' target on the cell surface, partly because of a mutation in the spike protein called N501Y. It may also be better at countering interferons, molecules that are part of the body's viral immune defenses. Together those changes may lower the amount of virus needed to infect someone—the infectious dose. In Delta, one of the most important changes may be near the furin cleavage site on spike, where a human enzyme cuts the protein, a key step enabling the virus to invade human cells. A mutation called P681R in that region makes cleavage more efficient, which may allow the virus to enter more cells faster and lead to greater numbers of virus particles in an infected person. In July, Chinese researchers posted a preprint showing Delta could lead to virus levels in patient samples 1000 times higher than for previous variants. Evidence is accumulating that infected people not only spread the virus more efficiently, but also faster, allowing the variant to spread even more rapidly. The new variants of SARS-CoV-2 may also cause more severe disease. For example, a study in Scotland found that an infection with Delta was about twice as likely to lead to hospital admission than with Alpha. It wouldn't be the first time a newly emerging disease quickly became more serious. The 1918–19 influenza pandemic also appears to have caused more serious illness as time went on, says Lone Simonsen, an epidemiologist at Roskilde University who studies past pandemics. “Our data from Denmark suggests it was six times deadlier in the second wave.” A popular notion holds that viruses tend to evolve over time to become less dangerous, allowing the host to live longer and spread the virus more widely. But that idea is too simplistic, Holmes says. “The evolution of virulence has proven to be quicksand for evolutionary biologists,” he says. “It's not a simple thing.” Two of the best studied examples of viral evolution are myxoma virus and rabbit hemorrhagic disease virus, which were released in Australia in 1960 and 1996, respectively, to decimate populations of European rabbits that were destroying croplands and wreaking ecological havoc. Myxoma virus initially killed more than 99% of infected rabbits, but then less pathogenic strains evolved, likely because the virus was killing many animals before they had a chance to pass it on. (Rabbits also evolved to be less susceptible.) Rabbit hemorrhagic disease virus, by contrast, got more deadly over time, probably because the virus is spread by blow flies feeding on rabbit carcasses, and quicker death accelerated its spread. Other factors loosen the constraints on deadliness. For example, a virus variant that can outgrow other variants within a host can end up dominating even if it makes the host sicker and reduces the likelihood of transmission. And an assumption about human respiratory diseases may not always hold: that a milder virus—one that doesn't make you crawl into bed, say—might allow an infected person to spread the virus further. In SARS-CoV-2, most transmission happens early on, when the virus is replicating in the upper airways, whereas serious disease, if it develops, comes later, when the virus infects the lower airways. As a result, a variant that makes the host sicker might spread just as fast as before. From the start of the pandemic, researchers have worried about a third type of viral change, perhaps the most unsettling of all: that SARS-CoV-2 might evolve to evade immunity triggered by natural infections or vaccines. Already, several variants have emerged sporting changes in the surface of the spike protein that make it less easily recognized by antibodies. But although news of these variants has caused widespread fear, their impact has so far been limited. Derek Smith, an evolutionary biologist at the University of Cambridge, has worked for decades on visualizing immune evasion in the influenza virus in so-called antigenic maps. The farther apart two variants are on Smith's maps, the less well antibodies against one virus protect against the other. In a recently published preprint, Smith's group, together with David Montefiori's group at Duke University, has applied the approach to mapping the most important variants of SARS-CoV-2 (see graphic, below). The new maps place the Alpha variant very close to the original Wuhan virus, which means antibodies against one still neutralize the other. The Delta variant, however, has drifted farther away, even though it doesn't completely evade immunity. “It's not an immune escape in the way people think of an escape in slightly cartoonish terms,” Katzourakis says. But Delta is slightly more likely to infect fully vaccinated people than previous variants. “It shows the possible beginning of a trajectory and that's what worries me,” Katzourakis says. ![Figure][1] CREDITS: (GRAPHIC) N. DESAI/ SCIENCE ; (DATA) DEREK SMITH/UNIVERSITY OF CAMBRIDGE; DAVID MONTEFIORI/DUKE UNIVERSITY Other variants have evolved more antigenic distance from the original virus than Delta. Beta, which first appeared in South Africa, has traveled the farthest on the map, although natural or vaccine-induced immunity still largely protects against it. And Beta's attempts to get away may come at a price, as Delta has outstripped it worldwide. “It's probably the case that when a virus changes to escape immunity, it loses other aspects of its fitness,” Smith says. The map shows that for now, the virus is not moving in any particular direction. If the original Wuhan virus is like a town on Smith's map, the virus has been taking local trains to explore the surrounding area, but it has not traveled to the next city—not yet. Although it's impossible to predict exactly how infectiousness, virulence, and immune evasion will develop in the coming months, some of the factors that will influence the virus' trajectory are clear. One is the immunity that is now rapidly building in the human population. On one hand, immunity reduces the likelihood of people getting infected, and may hamper viral replication even when they are. “That means there will be fewer mutations emerging if we vaccinate more people,” Çevik says. On the other hand, any immune escape variant now has a huge advantage over other variants. In fact, the world is probably at a tipping point, Holmes says: With more than 2 billion people having received at least one vaccine dose and hundreds of millions more having recovered from COVID-19, variants that evade immunity may now have a bigger leg up than those that are more infectious. Something similar appears to have happened when a new H1N1 influenza strain emerged in 2009 and caused a pandemic, says Katia Kölle, an evolutionary biologist at Emory University. A 2015 paper found that changes in the virus in the first 2 years appeared to make the virus more adept at human-to-human transmission, whereas changes after 2011 were mostly to avoid human immunity. It may already be getting harder for SARS-CoV-2 to make big gains in infectiousness. “There are some fundamental limits to exactly how good a virus can get at transmitting and at some point SARS-CoV-2 will hit that plateau,” says Jesse Bloom, an evolutionary biologist at the Fred Hutchinson Cancer Research Center. “I think it's very hard to say if this is already where we are, or is it still going to happen.” Evolutionary virologist Kristian Andersen of Scripps Research guesses the virus still has space to evolve greater transmissibility. “The known limit in the viral universe is measles, which is about three times more transmissible than what we have now with Delta,” he says. ![Figure][1] CREDITS: (GRAPHIC) N. DESAI/ SCIENCE ; (DATA) E. WALL ET AL., THE LANCET , 397:10292, 2331 (2021) The limits of immune escape are equally uncertain. Smith's antigenic maps show the space the virus has explored so far. But can it go much farther? If the variants on the map are like towns, then where are the country's natural boundaries—where does the ocean start? A crucial clue will be where the next few variants appear on the map, Smith says. Beta evolved in one direction away from the original virus and Delta in another. “It's too soon to say this now, but we might be heading for a world where there are two serotypes of this virus that would also both have to be considered in any vaccines,” Drosten says. Immune escape is so worrying because it could force humanity to update its vaccines continually, as happens for flu. Yet the vaccines against many other diseases—measles, polio, and yellow fever, for example—have remained effective for decades without updates, even in the rare cases where immune-evading variants appeared. “There was big alarm around 2000 that maybe we'd need to replace the hepatitis B vaccines,” because an escape variant had popped up, Read says. But the variant has not spread around the world: It is able to infect close contacts of an infected person, but then peters out. The virus apparently faces a trade-off between transmissibility and immune escape. Such trade-offs likely exist for SARS-CoV-2 as well. Some clues about SARS-CoV-2's future path may come from coronaviruses with a much longer history in humans: those that cause common colds. Some are known to reinfect people, but until recently it was unclear whether that's because immunity in recovered people wanes, or because the virus changes its surface to evade immunity. In a study published in April in PLOS Pathogens , Bloom and other researchers compared the ability of human sera taken at different times in the past decades to block virus isolated at the same time or later. They showed that the samples could neutralize strains of a coronavirus named 229E isolated around the same time, but weren't always effective against virus from 10 years or more later. The virus had evidently evolved to evade human immunity, but it had taken 10 years or more. “Immune escape conjures this catastrophic failure of immunity when it is really immune erosion,” Bloom says. “Right now it seems like SARS-CoV-2, at least in terms of antibody escape, is actually behaving a lot like coronavirus 229E.” Others are probing SARS-CoV-2 itself. In a preprint published this month, researchers tinkered with the virus to learn how much it has to change to evade the antibodies generated in vaccine recipients and recovered patients. They found that it took 20 changes to the spike protein to escape current antibody responses almost completely. That means the bar for complete escape is high, says one of the authors, virologist Paul Bieniasz of Rockefeller University. “But it's very difficult to look into a crystal ball and say whether that is going to be easy for the virus to acquire or not,” he says. “It seems plausible that true immune escape is hard,” concludes William Hanage of the Harvard T.H. Chan School of Public Health. “However, the counterargument is that natural selection is a hell of a problem solver and the virus is only beginning to experience real pressure to evade immunity.” And the virus has tricks up its sleeve. Coronaviruses are good at recombining, for instance, which could allow new variants to emerge suddenly by combining the genomes—and the properties—of two different variants. In pigs, recombination of a coronavirus named porcine epidemic diarrhea virus with attenuated vaccine strains of another coronavirus has led to more virulent variants of PEDV. “Given the biology of these viruses, recombination may well factor into the continuing evolution of SARS-CoV-2,” Korber says. Given all that uncertainty, it's worrisome that humanity hasn't done a great job of limiting the spread of SARS-CoV-2, says Eugene Koonin, a researcher at the U.S. National Center for Biotechnology Information. Some dangerous variants may only be possible if the virus hits on a very rare, winning combination of mutations, he says. It might have to replicate an astronomical number of times to get there. “But with all these millions of infected people, it may very well find that combination.” Indeed, Katzourakis adds, the past 20 months are a warning to never underestimate viral evolution. “Many still see Alpha and Delta as being as bad as things are ever going to get,” he says. “It would be wise to consider them as steps on a possible trajectory that may challenge our public health response further.” [1]: pending:yes

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has the capacity to generate variants with major genomic changes. The UK variant B.1.1.7 (also known as VOC 202012/01) has many mutations that alter virus attachment and entry into human cells. Using a variety of statistical and dynamic modeling approaches, Davies et al. characterized the spread of the B.1.1.7 variant in the United Kingdom. The authors found that the variant is 43 to 90% more transmissible than the predecessor lineage but saw no clear evidence for a change in disease severity, although enhanced transmission will lead to higher incidence and more hospital admissions. Large resurgences of the virus are likely to occur after the easing of control measures, and it may be necessary to greatly accelerate vaccine roll-out to control the epidemic. Science , this issue p. [eabg3055][1] ### INTRODUCTION Several novel variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, emerged in late 2020. One of these, Variant of Concern (VOC) 202012/01 (lineage B.1.1.7), was first detected in southeast England in September 2020 and spread to become the dominant lineage in the United Kingdom in just a few months. B.1.1.7 has since spread to at least 114 countries worldwide. ### RATIONALE The rapid spread of VOC 202012/01 suggests that it transmits more efficiently from person to person than preexisting variants of SARS-CoV-2. This could lead to global surges in COVID-19 hospitalizations and deaths, so there is an urgent need to estimate how much more quickly VOC 202012/01 spreads, whether it is associated with greater or lesser severity of disease, and what control measures might be effective in mitigating its impact. We used social contact and mobility data, as well as demographic indicators linked to SARS-CoV-2 community testing data in England, to assess whether the spread of the new variant may be an artifact of higher baseline transmission rates in certain geographical areas or among specific demographic subpopulations. We then used a series of complementary statistical analyses and mathematical models to estimate the transmissibility of VOC 202012/01 across multiple datasets from the UK, Denmark, Switzerland, and the United States. Finally, we extended a mathematical model that has been extensively used to forecast COVID-19 dynamics in the UK to consider two competing SARS-CoV-2 lineages: VOC 202012/01 and preexisting variants. By fitting this model to a variety of data sources on infections, hospitalizations, and deaths across seven regions of England, we assessed different hypotheses for why the new variant appears to be spreading more quickly, estimated the severity of disease associated with the new variant, and evaluated control measures including vaccination and nonpharmaceutical interventions. Combining multiple lines of evidence allowed us to draw robust inferences. ### RESULTS The rapid spread of VOC 202012/01 is not an artifact of geographical differences in contact behavior and does not substantially differ by age, sex, or socioeconomic stratum. We estimate that the new variant has a 43 to 90% higher reproduction number (range of 95% credible intervals, 38 to 130%) than preexisting variants. Similar increases are observed in Denmark, Switzerland, and the United States. The most parsimonious explanation for this increase in the reproduction number is that people infected with VOC 202012/01 are more infectious than people infected with a preexisting variant, although there is also reasonable support for a longer infectious period and multiple mechanisms may be operating. Our estimates of severity are uncertain and are consistent with anything from a moderate decrease to a moderate increase in severity (e.g., 32% lower to 20% higher odds of death given infection). Nonetheless, our mathematical model, fitted to data up to 24 December 2020, predicted a large surge in COVID-19 cases and deaths in 2021, which has been borne out so far by the observed burden in England up to the end of March 2021. In the absence of stringent nonpharmaceutical interventions and an accelerated vaccine rollout, COVID-19 deaths in the first 6 months of 2021 were projected to exceed those in 2020 in England. ### CONCLUSION More than 98% of positive SARS-CoV-2 infections in England are now due to VOC 202012/01, and the spread of this new variant has led to a surge in COVID-19 cases and deaths. Other countries should prepare for potentially similar outcomes. ![Figure][2] Impact of SARS-CoV-2 Variant of Concern 202012/01. ( A ) Spread of VOC 202012/01 (lineage B.1.1.7) in England. ( B ) The estimated relative transmissibility of VOC 202012/01 (mean and 95% confidence interval) is similar across the United Kingdom as a whole, England, Denmark, Switzerland, and the United States. ( C ) Projected COVID-19 deaths (median and 95% confidence interval) in England, 15 December 2020 to 30 June 2021. Vaccine rollout and control measures help to mitigate the burden of VOC 202012/01. A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, VOC 202012/01 (lineage B.1.1.7), emerged in southeast England in September 2020 and is rapidly spreading toward fixation. Using a variety of statistical and dynamic modeling approaches, we estimate that this variant has a 43 to 90% (range of 95% credible intervals, 38 to 130%) higher reproduction number than preexisting variants. A fitted two-strain dynamic transmission model shows that VOC 202012/01 will lead to large resurgences of COVID-19 cases. Without stringent control measures, including limited closure of educational institutions and a greatly accelerated vaccine rollout, COVID-19 hospitalizations and deaths across England in the first 6 months of 2021 were projected to exceed those in 2020. VOC 202012/01 has spread globally and exhibits a similar transmission increase (59 to 74%) in Denmark, Switzerland, and the United States. [1]: /lookup/doi/10.1126/science.abg3055 [2]: pending:yes

At a moment when vaccines promise to end the coronavirus pandemic, emerging new variants threaten to accelerate it. The astonishingly fast development of safe and effective vaccines is being stymied by the glacial pace of actual vaccinations while 3,000 Americans die each day. Minimizing death and suffering from COVID-19 requires vaccinating the most vulnerable Americans first and fast, but the vaccine rollout has been slow and inequitable. Prioritization algorithms have led to the most privileged being prioritized over the most exposed, and strict adherence to priority pyramids has been disastrously slow. Yet without prioritization, vaccines go to those with greatest resources rather than to those at greatest risk.

IMAGE: Brendan Englot at Stevens Institute of Technology will leverage a new variant of a classic artificial intelligence tools to create robots that can predict and manage the risks involved in... view more Just like humans, when robots have a decision to make there are often many options and hundreds of potential outcomes. Robots have been able to simulate a handful of these outcomes to figure out which course of action will be the most likely to lead to success. But what if one of the other options were equally likely to succeed - and safer? The Office of Naval Research has awarded Brendan Englot, an MIT-trained mechanical engineer at Stevens Institute of Technology, a 2020 Young Investigator Award of $508, 693 to leverage a new variant of a classic artificial intelligence tool to allow robots to predict the many possible outcomes of their actions, and how likely they are to occur. The framework will allow robots to figure out which option is the best way to achieve a goal, by understanding which options are the safest, most efficient - and least likely to fail.