Living in a busy city doesn't increase the chance of getting Covid-19, but overcrowding does, a new study reveals. AC-19, which was withdrawn from Google's app store last year over alleged concerns of government spying, tracks positive cases and deaths by geographic location. After investigating the link between density and virus transmission in the city, the researchers found that'density alone cannot be considered a risk factor'. The experts stress the difference between high urban density – a high number of people inhabiting an urbanised area – and overcrowding. The right figure shows the state of pandemic spread at the city level and the left one depicts the status at the national level.
Beauty, it is said, resides in the eye of the beholder. What if that beholder is a machine learning model being trained to describe and classify fine works of art? That's what AI researchers at Zhejiang University of Technology in China are attempting to find out by comparing the ability of different models trained on a growing list of image data sets to classify artwork by genre and style. Whether these models can be trained to respond emotionally remains to be seen. Preliminary results from one study published earlier this month in the journal of the Public Library of Science highlighted the utility of using convolutional neural networks (CNNs) for demanding tasks like art classification.
Consumers' choices of which restaurants to patronize are based not only on their food cravings but on whether they can meet the diners' desires for safety and convenience. The pandemic has led many customers to replace indoor dining experiences with takeout and delivery purchases instead, and many have been turning to digital tools like websites, mobile apps and scannable QR codes posted in restaurants' windows to help them easily place these orders. Eateries are looking to cater to this shifting consumer demand and to spare their staff from close customer contact that could increase employees' risk of catching the virus. These two motivations are driving restaurants to adopt various technologies to facilitate swift, remote customer interactions. Millennial and Generation Z diners appear particularly swayed by such tools, with 61 percent saying that the ability to pay digitally is a key factor in influencing their restaurant choice.
Objective: The aim of this study was to explore the role of the AI system which was designed and developed based on the characteristics of COVID-19 CT images in the screening and evaluation of COVID-19. Methods: The research team adopted an improved U-shaped neural network to segment lungs and pneumonia lesions in CT images through multilayer convolution iterations. Then the appropriate 159 cases were selected to establish and train the model, and Dice loss function and Adam optimizer were used for network training with the initial learning rate of 0.001. Finally, 39 cases (29 positive and 10 negative) were selected for the comparative test. Control group: an attending physician b and an associate chief physician b did the diagnosis only by their experience, without the help of the AI system.
How can the resurgent epidemics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during 2020 be explained? Are they a result of students going back to school? To address this question, Monod et al. created a contact matrix for infection based on data collected in Europe and China and extended it to the United States. Early in the pandemic, before interventions were widely implemented, contacts concentrated among individuals of similar age were the highest among school-aged children, between children and their parents, and between middle-aged adults and the elderly. However, with the advent of nonpharmaceutical interventions, these contact patterns changed substantially. By mid-August 2020, although schools reopening facilitated transmission, the resurgence in the United States was largely driven by adults 20 to 49 years of age. Thus, working adults who need to support themselves and their families have fueled the resurging epidemics in the United States. Science , this issue p. [eabe8372] ### INTRODUCTION After initial declines, in mid-2020, a sustained resurgence in the transmission of novel coronavirus disease (COVID-19) occurred in the United States. Throughout the US epidemic, considerable heterogeneity existed among states, both in terms of overall mortality and infection, but also in the types and stringency of nonpharmaceutical interventions. Despite these stark differences among states, little is known about the relationship between interventions, contact patterns, and infections, or how this varies by age and demographics. A useful tool for studying these dynamics is individual, age-specific mobility data. In this study, we use detailed mobile-phone data from more than 10 million individuals and establish a mechanistic relationship between individual contact patterns and COVID-19 mortality data. ### RATIONALE As the pandemic progresses, disease control responses are becoming increasingly nuanced and targeted. Understanding fine-scale patterns of how individuals interact with each other is essential to mounting an efficient public health control program. For example, the choice of closing workplaces, closing schools, limiting hospitality sectors, or prioritizing vaccination to certain population groups should be informed by the demographics currently driving and sustaining transmission. To develop the tools to answer such questions, we introduce a new framework that links mobility to mortality through age-specific contact patterns and then use this rich relationship to reconstruct accurate transmission dynamics (see figure panel A). ### RESULTS We find that as of 29 October 2020, adults aged 20 to 34 and 35 to 49 are the only age groups that have sustained SARS-CoV-2 transmission with reproduction numbers (transmission rates) consistently above one. The high reproduction numbers from adults are linked both to rebounding mobility over the summer and elevated transmission risks per venue visit among adults aged 20 to 49. Before school reopening, we estimate that 75 of 100 COVID-19 infections originated from adults aged 20 to 49, and the share of young adults aged 20 to 34 among COVID-19 infections was highly variable geographically. After school reopening, we reconstruct relatively modest shifts in the age-specific sources of resurgent COVID-19 toward younger individuals, with less than 5% of SARS-CoV-2 transmissions attributable to children aged 0 to 9 and less than 10% attributable to early adolescents and teenagers aged 10 to 19. Thus, adults aged 20 to 49 continue to be the only age groups that contribute disproportionately to COVID-19 spread relative to their size in the population (see figure panel B). However, because children and teenagers seed infections among adults who are more transmission efficient, we estimate that overall, school opening is indirectly associated with a 26% increase in SARS-CoV-2 transmission. ### CONCLUSION We show that considering transmission through the lens of contact patterns is fundamental to understanding which population groups are driving disease transmission. Over time, the share of age groups among reported deaths has been markedly constant, and the data provide no evidence that transmission shifted to younger age groups before school reopening, and no evidence that young adults aged 20 to 34 were the primary source of resurgent epidemics since the summer of 2020. Our key conclusion is that in locations where novel, highly transmissible SARS-CoV-2 lineages have not yet become established, additional interventions among adults aged 20 to 49, such as mass vaccination with transmission-blocking vaccines, could bring resurgent COVID-19 epidemics under control and avert deaths. ![Figure] Model developed to estimate the contribution of age groups to resurgent COVID-19 epidemics in the United States. ( A ) Model overview. ( B ) Estimated contribution of age groups to SARS-CoV-2 transmission in October. After initial declines, in mid-2020 a resurgence in transmission of novel coronavirus disease (COVID-19) occurred in the United States and Europe. As efforts to control COVID-19 disease are reintensified, understanding the age demographics driving transmission and how these affect the loosening of interventions is crucial. We analyze aggregated, age-specific mobility trends from more than 10 million individuals in the United States and link these mechanistically to age-specific COVID-19 mortality data. We estimate that as of October 2020, individuals aged 20 to 49 are the only age groups sustaining resurgent SARS-CoV-2 transmission with reproduction numbers well above one and that at least 65 of 100 COVID-19 infections originate from individuals aged 20 to 49 in the United States. Targeting interventions—including transmission-blocking vaccines—to adults aged 20 to 49 is an important consideration in halting resurgent epidemics and preventing COVID-19–attributable deaths. : /lookup/doi/10.1126/science.abe8372 : pending:yes
A coalition of AI researchers and health care professionals in fields like infectious disease, radiology, and ontology have found several common but serious shortcomings with machine learning made for COVID-19 diagnosis or prognosis. After the start of the global pandemic, startups like DarwinAI, major companies like Nvidia, and groups like the American College of Radiology launched initiatives to detect COVID-19 from CT scans, X-rays, or other forms of medical imaging. The promise of such technology is that it could help health care professionals distinguish between pneumonia and COVID-19 or provide more options for patient diagnosis. Some models have even been developed to predict if a person will die or need a ventilator based on a CT scan. However, researchers say major changes are needed before this form of machine learning can be used in a clinical setting.
In the earliest days of the pandemic, machine learning showed exceptional promise for COVID-19 diagnosis. Reliably, early machine learning models outperformed doctors in recognizing the telltale COVID-induced pneumonia on CT scans from hospitalized patients. Now, a year later, a team of researchers led by the University of Cambridge has concluded a review of COVID diagnosis ML models, finding that even in 2021, none of the proposed models are suitable for clinical use. The researchers whittled down 2,212 studies, eventually focusing on 62 studies – most of which were not peer-reviewed – published between January 1st and October 3rd of 2020, all of which presented machine learning models for diagnosing or predicting COVID-19 infection based on X-rays and/or CT scans. These 62 studies collectively described more than 300 such models – and the researchers found all of them substantially lacking.
An unknown number of people around the world are earning income by working through online labour platforms such as Upwork and Amazon Mechanical Turk. We combine data collected from various sources to build a data-driven assessment of the number of such online workers (also known as online freelancers) globally. Our headline estimate is that there are 163 million freelancer profiles registered on online labour platforms globally. Approximately 19 million of them have obtained work through the platform at least once, and 5 million have completed at least 10 projects or earned at least $1000. These numbers suggest a substantial growth from 2015 in registered worker accounts, but much less growth in amount of work completed by workers. Our results indicate that online freelancing represents a non-trivial segment of labour today, but one that is spread thinly across countries and sectors.
Researchers have used advanced AI and large sets of genomic data to unveil how humans have adapted to recent diseases. The method could also be applied to new pathogens such as the coronavirus that causes COVID-19, helping identify which gene mutations may be associated with more severe cases of the disease. The study, by researchers from Imperial College London, the Middle East Technical University, Turkey, and the Universita degli Studi di Bari Aldo Moro, Italy, is published today in a Special Issue of Molecular Ecology Resources, "Machine Learning techniques in Evolution and Ecology." Natural selection is the process by which beneficial gene mutations are preserved from generation to generation, until they become dominant in our genomes--the catalog of all our genes. One thing that can drive natural selection is protection against pathogens.
Academic research in board game playing AI has of course moved While artificial intelligence has been applied to control players' beyond most pedestrian board games, applying a diverse set of decisions in board games for over half a century, little attention algorithms for playing card games with millions of card combinations is given to games with no player competition. Pandemic is an exemplar such as Magic: the Gathering (Wizards of the Coast, 1993) , collaborative board game where all players coordinate to games of tactical card placement such as Lords of War (Black Box, overcome challenges posed by events occurring during the game's 2012)  and Carcassonne (Hans im Glück, 2000) , card games progression. This paper proposes an artificial agent which controls of team-based competition such as Hanabi (Abacusspiele, 2010)  all players' actions and balances chances of winning versus risk or Codenames (Czech Games Edition, 2015) , and many more. of losing in this highly stochastic environment. The agent applies Traditional board games such as chess  and backgammon a Rolling Horizon Evolutionary Algorithm on an abstraction of , as well as recent card games such as Race for the Galaxy (Rio the game-state that lowers the branching factor and simulates the Grande, 2007)  or digitized board games such as Hearthstone game's stochasticity. Results show that the proposed algorithm (Blizzard, 2014) [11, 18], focus on players competing to deplete another can find winning strategies more consistently in different games player's resources (pawns, hit points) or to accumulate more of varying difficulty.