New AI diagnostic can predict COVID-19 without testing

New AI diagnostic can predict COVID-19 without testing

Researchers at King's College London, Massachusetts General Hospital and health science company ZOE have developed an artificial intelligence diagnostic that can predict whether someone is likely to have COVID-19 based on their symptoms. Their findings are published today in Nature Medicine.

The AI model uses data from the COVID Symptom Study app to predict COVID-19 infection, by comparing people's symptoms and the results of traditional COVID tests. Researchers say this may provide help for populations where access to testing is limited. Two clinical trials in the UK and the US are due to start shortly.

More than 3.3 million people globally have downloaded the app and are using it to report daily on their health status, whether they feel well or have any new symptoms such as persistent cough, fever, fatigue and loss of taste or smell (anosmia).

In this study, the researchers analysed data gathered from just under 2.5 million people in the UK and US who had been regularly logging their health status in the app, around a third of whom had logged symptoms associated with COVID-19. Of these, 18,374 reported having had a test for coronavirus, with 7,178 people testing positive.

The research team investigated which symptoms known to be associated with COVID-19 were most likely to be associated with a positive test. They found a wide range of symptoms compared to cold and flu, and warn against focusing only on fever and cough. Indeed, they found loss of taste and smell (anosmia) was particularly striking, with two thirds of users testing positive for coronavirus infection reporting this symptom compared with just over a fifth of the participants who tested negative. The findings suggest that anosmia is a stronger predictor of COVID-19 than fever, supporting anecdotal reports of loss of smell and taste as a common symptom of the disease.

The researchers then created a mathematical model that predicted with nearly 80% accuracy whether an individual is likely to have COVID-19 based on their age, sex and a combination of four key symptoms: loss of smell or taste, severe or persistent cough, fatigue and skipping meals. Applying this model to the entire group of over 800,000 app users experiencing symptoms predicted that just under a fifth of those who were unwell (17.42%) were likely to have COVID-19 at that time.

Researchers suggest that combining this AI prediction with widespread adoption of the app could help to identify those who are likely to be infectious as soon as the earliest symptoms start to appear, focusing tracking and testing efforts where they are most needed.

Professor Tim Spector from King's College London said: "Our results suggest that loss of taste or smell is a key early warning sign of COVID-19 infection and should be included in routine screening for the disease. We strongly urge governments and health authorities everywhere to make this information more widely known, and advise anyone experiencing sudden loss of smell or taste to assume that they are infected and follow local self-isolation guidelines."

Story Source:

Materials provided by King's College London. Note: Content may be edited for style and length.

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Journal Reference:

1. Cristina Menni, Ana M. Valdes, Maxim B. Freidin, Carole H. Sudre, Long H. Nguyen, David A. Drew, Sajaysurya Ganesh, Thomas Varsavsky, M. Jorge Cardoso, Julia S. El-Sayed Moustafa, Alessia Visconti, Pirro Hysi, Ruth C. E. Bowyer, Massimo Mangino, Mario Falchi, Jonathan Wolf, Sebastien Ourselin, Andrew T. Chan, Claire J. Steves, Tim D. Spector. Real-time tracking of self-reported symptoms to predict potential COVID-19. Nature Medicine, 2020; DOI: 10.1038/s41591-020-0916-2

The Mathematics of Relationships, AI and Human Eco space

Category theory provides a structural framework for mathematics and is on its way to becoming a language for consciousness in the universe.” Learn how it relates to Haramein’s Holofractal Universe.

Forbes Magazine recently shed light on the fact that “there is a growing belief that the current understanding of science cannot wholly explain human life, mind, and consciousness, nor can it explain the nature and origin of life, matter, the environment, the universe and reality“. It summarizes a podcast held by the Author Jayshree Pandya called Risk Roundup, where she discussed Category Theory for application in cyberspace, aquaspace, geospace and space (CAGS) with Mathematical Physicist and Professor of Mathematics Dr. Baez.

Beyond doubt, the human body is an open system, so physical laws that do require a closed system, are applicable only under certain conditions – a mathematical framework for open system could improve our future creations. New Technologies require a better understanding of communication in a collective of entities and within its environment.

“In any system, we are dealing with on Earth, it is always very fundamentally an open system – its constantly being affected in unpredictable ways by the outside world and it is also affecting the outside world in unpredictable ways.”

Professor (Dr.) John Carlos Baez University of California, Riverside

“Based on the Mathematical Universe Hypothesis, the emerging reality is that we live in a relational reality. What does that mean? It means that the properties of the biosphere around us stem not from properties of its ultimate building blocks, but from the relations among these building blocks. (…) self-organization is an obvious principle which is embedded in our description of the universe (…) If an individual being is seen as a single unit, what defines and determines our behaviour and relationships?” the article reads. You can find it here.

RSF In Perspective

On Objects (Electrons, the Universe, and the PSU):

– Haramein’s Holographic Mass Solution (HMS) has been shown to be precise for astronomical objects like the Universe and Black Holes (Quantized Gravity), as well as for nuclear objects like the Proton and the Electron (Quantum Gravity). Now the recent paper by Haramein & Val Baker: Resolving the Vacuum Catastrophe: A Generalized Holographic Approach shows, in concord with lack of experimental proof, no need for dark matter or dark energy to describe universal dynamics. http://hiup.org/the-vauum-catastrophe/

– The Holofractal Universe provides an understanding on how the fundamental forces are structuring the insides of measurable fundamental objects. The Basic Building-Block Unit required for Category Theory would be in this case the tiny Planck Spherical Unit (PSU), a spinning grain (Voxel) of the quantum smoothie called spacetime.

RSF In Perspective

On Morphisms (Entanglement, Micro-Wormhole, Bonds and Relationships):

– One of the three mathematical entities in Category Theory are called morphisms (also known as maps or arrows). Each morphism f has a source object a and a target object b. Entangled states in the micro-wormhole network of spacetime voxels co-creating complexity and awareness would be ideally represented by morphisms, as both Category Theory and the Holofractal Universe Theory have topological fundamentals.

– The Unified Spacememory Network: from Cosmogenesis to Consciousness (DOI:10.14704.nq.2016.14.4.961) by Haramein, Brown and Val Baker discuss how feedback-loops of information flow are required for realistic timeline of the emergence of our universe. The exchange happens over the boundary via micro wormholes of holographic quantum entanglement with the outside world.

– Unified Physics and the Entanglement Nexus of Awareness (DOI: 10.14704.nq.2019.17.7.2519) published in May 2019 now further explains a mechanism of vacuum-state correlation of quanta in the neurobiological system resulting in a co-dependency of states. The information processing of awareness is discussed with reference to DNA, microtubules and coherent electromagnetic emissions by both water nanostructures and biomolecules. This explains how the rise of complexity via awareness guided entanglement can be expressed biophysically and leads to the resolution of the binding problem and the information loss paradox.