Editor's note: This story led off this week's Future of Learning newsletter, which is delivered free to subscribers' inboxes every other Wednesday with trends and top stories about education innovation. Joanna Smith, founder of an ed-tech company that helps schools curb chronic absenteeism, was thinking about how to pivot her company to provide services in a remote learning setting as many brick and mortar schools transitioned online last year. In April 2020, her company, AllHere, launched several new features to battle problems exacerbated by Covid-19, including an Artificial Intelligence-powered two-way text messaging system, Chatbot, for kids who weren't showing up to class regularly. Chatbot allows teachers to check in with families and provides 24/7 individualized AI support for struggling students. Families can also log on to the platform to get confidential health care referrals or help with computer-related issues.
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 AC-19, which was withdrawn from Google's app store last year over alleged concerns of government spying, tracks positive cases and deaths by geographic location.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.
It's hoped that COVID-19 vaccines will be the silver bullet that eventually allows society to return to normal. But even an accelerated inoculation campaign is unlikely to have a major impact on what appears to be a growing fourth wave of infections in Tokyo, according to research by a Tsukuba University professor. Setsuya Kurahashi, a professor of systems management, conducted a simulation using artificial intelligence that looked at how the vaccine rollout would help prevent the spread of the coronavirus in Tokyo if new infections rise at the same pace as during the second wave last summer. Even if 70,000 vaccinations per day, or 0.5% of the capital's 14 million people, were given to Tokyoites -- with priority given to people age 60 and over -- the capital would still see a fourth wave of infections peaking at 1,610 new cases on May 14, the study showed. The study also showed a fifth wave is expected to peak at 640 cases on Aug. 31.
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.
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.
COVID-19 patient triaging with predictive outcome of the patients upon first present to emergency department (ED) is crucial for improving patient prognosis, as well as better hospital resources management and cross-infection control. We trained a deep feature fusion model to predict patient outcomes, where the model inputs were EHR data including demographic information, co-morbidities, vital signs and laboratory measurements, plus patient's CXR images. The model output was patient outcomes defined as the most insensitive oxygen therapy required. For patients without CXR images, we employed Random Forest method for the prediction. Predictive risk scores for COVID-19 severe outcomes ("CO-RISK" score) were derived from model output and evaluated on the testing dataset, as well as compared to human performance. The study's dataset (the "MGB COVID Cohort") was constructed from all patients presenting to the Mass General Brigham (MGB) healthcare system from March 1st to June 1st, 2020. ED visits with incomplete or erroneous data were excluded. Patients with no test order for COVID or confirmed negative test results were excluded. Patients under the age of 15 were also excluded. Finally, electronic health record (EHR) data from a total of 11060 COVID-19 confirmed or suspected patients were used in this study. Chest X-ray (CXR) images were also collected from each patient if available. Results show that CO-RISK score achieved area under the Curve (AUC) of predicting MV/death (i.e. severe outcomes) in 24 hours of 0.95, and 0.92 in 72 hours on the testing dataset. The model shows superior performance to the commonly used risk scores in ED (CURB-65 and MEWS). Comparing with physician's decisions, CO-RISK score has demonstrated superior performance to human in making ICU/floor decisions.
Many tech workers find themselves on the precipice of change: their organizations may decide to continue remote work for the foreseeable future, they may adopt a hybrid work schedule or they'll return to on-premises full time. But while a recent study showed that most employees said they'll stay in their current positions, they also said they expected a greater return from their employers, which means if they don't get that "return," they'll be on the job market. Many organizations were hit hard by the impact of the pandemic and, coupled with the safety protocols required for workers who do go into the office, they might not be able to accommodate employees' demands. This represents potential for the tech industry, with pros looking for new positions and companies looking for new talent. LHH, formerly Lee Hecht Harrison, global provider of talent and leadership development, career transition and coaching, analyzed job openings for March 2021, and its findings can provide insight for tech pros, whether they want to remain in their jobs or if they might consider "putting feelers out."