A new machine-learning program accurately identifies COVID-19-related conspiracy theories on social media and models how they evolved over time–a tool that could someday help public health officials combat misinformation online.
“A lot of machine-learning studies related to misinformation on social media focus on identifying different kinds of conspiracy theories,” said Courtney Shelley, a postdoctoral researcher in the Information Systems and Modeling Group at Los Alamos National Laboratory and co-author of the study that was published last week in the Journal of Medical Internet Research. “Instead, we wanted to create a more cohesive understanding of how misinformation changes as it spreads. Because people tend to believe the first message they encounter, public health officials could someday monitor which conspiracy theories are gaining traction on social media and craft factual public information campaigns to preempt widespread acceptance of falsehoods.”
The faster-spreading B.1.1.7 variant of SARS-CoV-2 first detected in the United Kingdom, the coronavirus that causes COVID-19, is quickly on its way to becoming the dominant variant of the virus in the United States, according to a study from scientists at Scripps Research and the COVID-19 test maker Helix.
The findings, which appear today in Cell, suggest that future COVID-19 case numbers and mortality rates in the United States will be higher than would have been otherwise. The analysis suggests that the variant, which has been detectable in an increasing proportion of SARS-CoV-2 samples, is 40-50 percent more transmissible than SARS-CoV-2 lineages that were previously dominant. Other studies have found evidence that the B.1.1.7 variant may be about 50 percent more likely to cause fatal COVID-19.
At the time of writing, coronavirus disease 2019 (COVID-19) is seriously threatening human lives and health throughout the world. Before effective vaccines and specific drugs are developed, non-pharmacological interventions and numerical model predictions are essential. To this end, a group led by Professor Jianping Huang from Lanzhou University, China, developed the Global Prediction System of the COVID-19 Pandemic (GPCP).
Jianping Huang is a Professor in the College of Atmospheric Sciences and a Director of the Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, China. He has for a long time been dedicated to studying long-term climate prediction, dust-cloud interaction, and semi-arid climate change by combining field observations and theoretical research. Lockdown in early 2020 seriously affected his research. Therefore, stuck at home, he held online discussions with his team members on how their experience of developing climate system models might be able to contribute to fighting the pandemic. He didn’t expect much response, but was surprised and touched when many of his colleagues responded enthusiastically.
Even before public announcements of the first cases of COVID-19 in Europe were made, at the end of January 2020, signals that something strange was happening were already circulating on social media. A new study of researchers at IMT School for Advanced Studies Lucca, published in Scientific Reports, has identified tracks of increasing concern about pneumonia cases on posts published on Twitter in seven countries, between the end of 2019 and the beginning of 2020. The analysis of the posts shows that the “whistleblowing” came precisely from the geographical regions where the primary outbreaks later developed.
To conduct the research, the authors first created a unique database with all the messages posted on Twitter containing the keyword “pneumonia” in the seven most spoken languages of the European Union – English, German, French, Italian, Spanish, Polish, and Dutch – from December 2014 until 1 March 2020. The word “pneumonia” was chosen because the disease is the most severe condition induced by the SARS-CoV-2, and also because the 2020 flu season was milder than the previous ones, so there was no reason to think it to be responsible for all the mentions and worries. The researchers then made a number of adjustments and corrections to the posts in the database to avoid overestimating the number of tweets mentioning pneumonia between December 2019 and January 2020, that is to say in the weeks between the World Health Organization (WHO) announcement that the first “cases of pneumonia of unknown etiology” had been identified – on 31 December 2019 – and the official recognition of COVID19 as a serious transmissible disease, on 21 January 2020. In particular, all the tweets and retweets containing links to news about the emerging virus were eliminated from the database to exclude from the count the mass media coverage of the emerging pandemic.
Sweden kept preschools, primary and lower secondary schools open during the spring of 2020. So far, little research has been done on the risk of children being seriously affected by COVID-19 when the schools were open. A study from Karolinska Institutet in Sweden has now shown that one child in 130,000 was treated in an intensive care unit on account of COVID-19 during March-June. The study has been published in New England Journal of Medicine.
So far, more than 80 million people have become ill with COVID-19 and globally, almost two million people have died from the disease. Many countries have closed down parts of society in order to reduce the spread of infection. One such measure has been to close schools.
Calculator generates mortality risk estimates for individuals and communities based on sociodemographic info and medical history
A new online calculator for estimating individual and community-level risk of dying from COVID-19 has been developed by researchers at the Johns Hopkins Bloomberg School of Public Health. The researchers who developed the calculator expect it to be useful to public health authorities for assessing mortality risks in different communities, and for prioritizing certain groups for vaccination as COVID-19 vaccines become available.
The algorithm underlying the calculator uses information from existing large studies to estimate risk of COVID-19 mortality for individuals based on age, gender, sociodemographic factors and a variety of different health conditions. The risk estimates apply to individuals in the general population who are currently uninfected, and captures factors associated with both risk of future infection and complications after infection.
Partisan pandemic: How partisanship and public health concerns affect individuals’ social mobility during COVID-19
In the United States, political partisanship has played a much stronger role in individuals’ decisions to limit their social mobility during the COVID-19 pandemic than the local incidence of the disease in their own communities, according to a new survey-based study of 1,135,638 million responses collected between April 4 and September 10, 2020 via Survey Monkey from randomly chosen people. The analysis, based on an average of 6,744 responses daily during the study period, suggests partisanship has been roughly 27 times more important than local COVID-19 prevalence for explaining individual mobility. In addition, Joshua Clinton and colleagues also found that self-identified Democrats were 13.1% less likely to be socially mobile compared with independents, while Republicans were 27.8% more likely to be mobile. Notably, this gulf widened over time, driven largely by increasing unwillingness on the part of self-identified Republicans to limit their mobility. The researchers weighted each day’s sample cohort to be representative of the country’s adult population using current estimates from the U.S. Census Bureau’s American Community Survey. Each survey asked respondents to identify their political affiliation (Democrat, Republican, or Independent) and to report whether, in the preceding 24 hours, they had engaged in activities such as going to a restaurant, visiting family or friends, taking a walk, exercising, getting groceries, receiving medical care, or going to work. While the analysis primarily focused on the daily aggregate of all such activities, the researchers also found similar trends among individual activities as well, and further noted that the gap was even more pronounced when looking at riskier, voluntary activities such as eating at a restaurant or visiting with family or friends. “These differences have tremendous consequences for the ability of the United States to limit the spread of COVID-19,” the authors write, further noting that their results “add to a growing consensus that partisanship is a key factor in explaining behavior and attitudes surrounding the COVID-19 pandemic.”
The strong foundation of global health research and development (R&D) that greatly accelerated the development of COVID-19 innovations is now being weakened by pandemic pressures that are diverting funding and expertise away from other dangerous diseases and putting clinical trials and scientific endeavors around the world on indefinite hold.
That’s a key conclusion of a new report released today from the non-profit Global Health Technologies Coalition (GHTC). It’s based on qualitative interviews with experts in academia, philanthropy, industry, government agencies and product development partnerships discussing how the battle against one all-consuming global health threat–COVID-19–is affecting efforts to combat a wide range of other diseases still sickening and killing millions of people worldwide.
“A lot of veterans of global health R&D are confronting a confounding situation of a pandemic that has generated new appreciation for the value of their work while at the same time potentially causing long-term harm to the field,” said GHTC Director Jamie Bay Nishi. “Global health R&D has always subsisted on thin budgets and a tight-knit coalition of infectious disease experts–and both of these, funding and talent, are being redirected to COVID-19, which is putting many important projects in a precarious position.”
Following the first recorded death of an anaesthetist from COVID-19 in the UK in November 2020, a review of available data published in Anaesthesia (a journal of the Association of Anaesthetists) shows that unexpectedly, despite their perceived increased exposure to COVID-19 patients and high-risk procedures, anaesthetists and intensive care doctors appear to be at lower risk of being infected with SARS-CoV-2 and developing COVID-19.
The analysis was carried out by Professor Tim Cook, Consultant in Anaesthesia and Intensive Care Medicine, Royal United Hospitals Bath NHS Foundation Trust, Bath, UK, and School of Medicine, University of Bristol, UK, and Dr Simon Lenanne, General Practitioner, Ross-on-Wye, Herefordshire, UK. The authors undertook the review following the death on November 12, 2020, of anaesthetist Dr Krishnan Subramanian, who was aged 46 years and worked at the Royal Derby Hospital, UK.
The majority of pregnant women who tested positive for COVID-19 on arrival to the delivery room were asymptomatic, according to a paper by Mount Sinai researchers published in PLOS One on Thursday, December 10. The pregnant patients who tested positive for the coronavirus were also more likely than those who tested negative to identify as Hispanic and report their primary language as Spanish.