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Social media is a popular source of information about health, including COVID-19 and testing information. One of the most popular communication channels used by adolescents and young adults who search for health information is TikTok—an emerging social media platform.
Objective: The purpose of this study was to describe TikTok videos related to COVID-19 testing.
Methods: The hashtag #covidtesting was searched, and the first 100 videos were included in the study sample. At the time the sample was drawn, these 100 videos garnered more than 50% of the views for all videos cataloged under the hashtag #covidtesting.
The content characteristics that were coded included mentions, displays, or suggestions of anxiety, COVID-19 symptoms, quarantine, types of tests, results of test, and disgust/unpleasantness. Additional data that were coded included the number and percentage of views, likes, and comments and the use of music, dance, and humor.
Results: The 100 videos garnered more than 103 million views; 111,000 comments; and over 12.8 million likes. Even though only 44 videos mentioned or suggested disgust/unpleasantness and 44 mentioned or suggested anxiety, those that portrayed tests as disgusting/unpleasant garnered over 70% of the total cumulative number of views (73,479,400/103,071,900, 71.29%) and likes (9,354,691/12,872,505, 72.67%), and those that mentioned or suggested anxiety attracted about 60% of the total cumulative number of views (61,423,500/103,071,900, 59.59%) and more than 8 million likes (8,339,598/12,872,505, 64.79%). Independent one-tailed t tests (α=.05) revealed that videos that mentioned or suggested that COVID-19 testing was disgusting/unpleasant were associated with receiving a higher number of views and likes.
Conclusions: Our finding of an association between TikTok videos that mentioned or suggested that COVID-19 tests were disgusting/unpleasant and these videos’ propensity to garner views and likes is of concern. There is a need for public health agencies to recognize and address connotations of COVID-19 testing on social media.
read the study report at https://publichealth.jmir.org/2021/6/e29528/
Lire l'article complet sur : publichealth.jmir.org
Via nrip
“ATMAN AI”, an Artificial Intelligence algorithm that can detect the presence of COVID-19 disease in Chest X Rays, has been developed to combat COVID fatalities involving lung. ATMAN AI is used for chest X-ray screening as a triaging tool in Covid-19 diagnosis, a method for rapid identification and assessment of lung involvement. This is a joint effort of the DRDO Centre for Artificial Intelligence and Robotics (CAIR), 5C Network & HCG Academics. This will be utilized by online diagnostic startup 5C Network with support of HCG Academics across India.
Triaging COVID suspect patients using X Ray is fast, cost effective and efficient. It can be a very useful tool especially in smaller towns in India owing to lack of easy access to CT scans there.
This will also reduce the existing burden on radiologists and make CT machines which are being used for COVID be used for other diseases and illness owing to overload for CT scans.
The novel feature namely “Believable AI” along with existing ResNet models have improved the accuracy of the software and being a machine learning tool, the accuracy will improve continually.
Chest X-Rays of RT-PCR positive hospitalized patients in various stages of disease involvement were retrospectively analysed using Deep Learning & Convolutional Neural Network models by an indigenously developed deep learning application by CAIR-DRDO for COVID -19 screening using digital chest X-Rays. The algorithm showed an accuracy of 96.73%.
read more at http://indiaai.gov.in/news/drdo-cair-5g-network-and-hcg-academics-develop-atman-ai
Lire l'article complet sur : indiaai.gov.in
Via nrip
Mobile health (mhealth) tools delivered through wireless technology are emerging as effective strategies for
- delivering quality training,
- ensuring rapid clinical decision making and
- monitoring implementation of simple and effective interventions in under-resourced settings.
We share our early experience of development and deployment of the InStrat COVID-19 health worker training application (App) based on the MediXcel Lite #mHealth platform by Plus91 technologies in Ogun state, Western Nigeria where the country's first case was reported.
This App was designed to
- directly provide frontline health workers with accurate and up-to-date information about COVID-19;
- enable them to quickly identify, screen and manage COVID-19 suspects;
- provide guidance on specimen collection techniques and safety measures to observe within wards and quarantine centres dealing with COVID-19.
The App was deployed in 271 primary health care facilities in Ogun State and a total of 311 health workers were trained. Of the 123 health workers who completed knowledge pre-and post-tests, their average test score improved from 47.5(±9.4) % to 73.1(±10.0) %, P < 0.0001 after using the tutorial.
Rapid adoption and uptake were driven largely by public-private sector involvement as well as certification with reported satisfaction levels of over 95%.
Challenges encountered included a lack of universal availability of android phones for frontline health workers, lack of internet access in remote areas and a need to incentivize the workers.
The timely deployment of this App targeting primary health care workers, mostly in hard-to-reach areas, obviated the need for conventional didactic training with potential of savings in training costs and time and could be applied to similar contexts.
This novel use of mobile health training to shore up training of front line health workers in a resource-limited setting during a pandemic has applicability to similar contexts.
Lire l'article complet sur : www.researchgate.net
Via nrip
Future Healthcare
Future of Public Transit, Health & Wellness in COVID-19 Economy.
Via TechinBiz, Richard Platt
Why COVID-19 is so hard to Track
Seeker sat down with two professors of medicine to find out how COVID-19 attacks our bodies and why it’s so hard to track and control
Credit Now This
Via TechinBiz, Sandra Boyer
Les écoles seront fermées à partir de vendredi, mais les mesures de confinement ne sont toujours pas obligatoires, alors que le nombre de décès causé par le coronavirus a progressé de 30 % en une journée, mercredi.
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We developed and validated an accurate in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model that improved upon SOFA. The COVID-19 pandemic created an emergent need for a novel, accurate, and location and context-sensitive EHR-computable tool to predict mortality in hospitalized patients with and without COVID-19. Because developing a new score can take years, a predictive model must rely on well-validated scores. In contrast, COVID-19 is a novel disease for which existing scores may be of limited but unknown predictive value. As such, a predictive framework relying on multiple previously validated scores that can incorporate new information but only keeps the new inputs that explicitly improve performance is required. Stacked generalization provides a solution. A stacked model is built upon one or more baseline model(s) (e.g. SOFA) and incorporates additional models only when they improve prediction. Materials and Methods We developed, verified, and deployed a stacked generalization model to predict mortality using data available in the EHR by combining five previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We verified the model with prospectively collected data from 12 hospitals in Colorado between March 2020 and July 2020. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index. Results The prospective cohort included 27,296 encounters, of which 1,358 (5.0%) were positive for SARS-CoV-2, 4,494 (16.5%) required intensive care unit care, 1,480 (5.4%) required mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94. In the subset of patients with COVID-19, the stacked model predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85. Discussion Stacked regression allows a flexible, updatable, live-implementable, ethically defensible predictive analytics tool for decision support that begins with validated models and includes only novel information that improves prediction. read the paper abstract at https://academic.oup.com/jamia/advance-article/doi/10.1093/jamia/ocab100/6273353 read the entire paper at https://academic.oup.com/jamia/advance-article-pdf/doi/10.1093/jamia/ocab100/37905236/ocab100.pdf Lire l'article complet sur : academic.oup.com
Via nrip
Since the start of the pandemic, new technologies have been developed to help reduce the spread of the infection. Some of the most common safety measures today include measuring a person’s temperature, covering your nose and mouth with a mask, contact tracing, disinfection, and social distancing. Many businesses have adopted various technologies, including those with artificial intelligence (AI) underneath, helping to adhere to the COVID-19 safety measures. As an example, numerous airlines, hotels, subways, shopping malls, and other institutions are already using thermal cameras to measure an individual’s temperature before people are allowed entry. In its turn, public transport in France relies on AI-based surveillance cameras to monitor whether or not people are social-distancing or wearing masks. Another example is requiring the download of contact-tracing apps delivered by governments across the globe. However, there are a number of issues. While many of these solutions help to ensure that COVID-19 prevention practices are observed, many of them have flaws or limits. In this article, we will cover some of the issues creating obstacles for fighting the pandemic. Issue #1. Manual temperature scanning is tricky Issue #2. Monitoring crowds is even more complex Issue #3. Contact tracing leads to privacy concerns Issue #4. UV rays harm eyes and skin Issue #5. UVC robots are extremely expensive Issue #6. No integration, no compliance, no transparency Regardless of the safety measures in place and existing issues, innovations are already playing a vital role in the fight against COVID-19. By improving on existing technology, we can make everyone safer as we all adjust to the new normal. read the details at https://www.altoros.com/blog/whats-wrong-with-ai-tools-and-devices-preventing-covid-19/ Lire l'article complet sur : www.altoros.com
Via nrip
With cases soaring across the globe, the Covid-19 pandemic is nowhere near its end, but with three vaccines reporting trial data and two apparently nearing approval by the US FDA, it may be reaching a pivot point.
In what feels like a moment of drawing breath and taking stock, international researchers are turning their attention from the present back to the start of the pandemic, aiming to untangle its origin and asking what lessons can be learned to keep this from happening again.
Two efforts are happening in parallel. On November 5, the World Health Organization quietly published the rules of engagement for a long-planned and months-delayed mission that creates a multinational team of researchers who will pursue how the virus leaped species. Meanwhile, last week, a commission created by The Lancet and headed by the economist and policy expert Jeffrey Sachs announced the formation of its own international effort, a task force of 12 experts from nine countries who will undertake similar tasks.
Both groups will face the same complex problems. It has been approximately a year since the first cases of a pneumonia of unknown origin appeared in Wuhan, China, and about 11 months since the pneumonia’s cause was identified as a novel coronavirus, probably originating in bats.
The experts will have to retrace a chain of transmission—one or multiple leaps of the virus from the animal world into humans—using interviews, stored biological samples, lab assays, environmental surveys, genomic data, and the thousands of papers published since the pandemic began, all while following a trail that may have gone cold.
The point is not to look for patient zero, the first person infected—or even a hypothetical bat zero, the single animal from which the novel virus jumped.
It’s likely neither of those will ever be found. The goal instead is to elucidate the ecosystem—physical, but also viral—in which the spillover happened and ask what could make it likely to happen again.
more at WIRED : https://www.wired.com/story/two-global-efforts-try-to-trace-the-origin-of-the-covid-virus/?utm_source=pocket-newtab-intl-en Lire l'article complet sur : www.wired.com
Via nrip
A Virus detection network to stop the next Pandemic
How can we stop the next pandemic before it starts? Disease researchers Pardis Sabeti and Christian Happi introduce Sentinel, an early warning system that detects and tracks viral threats in real time -- and could help stop them before they spre
Via TechinBiz, Sandra Boyer
Sachant qu'une pandémie risquait de se produire dans un futur proche, la BBC a simulé en 2017 la propagation d'un virus dangereux grâce aux données de géolocalisation de presque 30'000 volontaires. Diffusé par la RTS, le documentaire "Contagion" livre les résultats de cette expérience britannique.
Via Eric Bertrand
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One of the biggest learning from this pandemic for everyone associated with public health and disaster response is the need to communicate EFFECTIVELY . We cannot defeat disinformation without making the correct information reach the audience as well as get through to them in a manner they appreciate. That includes investing time and energy on creating effective (read correct yet simple to absorb) content of many different types and spreading them repeatedly across all possibly available channels where the audience expects to find them