CoviD19
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.
Lire l'article complet sur : www.wired.com
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
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
“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