Sep 19 (IANS) In the last 24 hours, India logged 4,858 new Covid-19 cases and 18 deaths, the Union Health Ministry said on Monday.
The new fatalities have pushed the nationwide death toll to 5,28,355.
The active caseload rose to 48,027, accounting for 0.11 per cent of the country’s total positive cases.
The recovery of 4,735 patients in the last 24 hours took the cumulative tally to 4,39,62,664. Consequently, India’s recovery rate stands at 98.71 per cent.
Meanwhile, the daily and weekly positivity rates stood at 2.76 per cent and 1.78 per cent, respectively.
Covid cases/Ians
Also in the same period, a total of 1,75,935 tests were conducted across the country, increasing the overall tally to over 89.17 crore.
As of Monday morning, India’s Covid-19 vaccination coverage exceeded 216.70 crore.
Over 4.08 crore adolescents have been administered with a first dose of Covid-19 jab since the beginning of vaccination drive for this age bracket.
Sep 18 (IANS) India on Sunday reported 5,664 fresh Covid cases in the last 24 hrs, against 5,747 Covid cases reported on previous day, said the Union Health Ministry.
In the same period, the country has recorded 35 more Covid related deaths, taking the national fatalities tally to 5,28,337 as per the report.
Meanwhile, the active caseload of the country has marginally risen to 47,922 cases, accounting for 0.11 per cent of the country’s total positive cases.
The recovery of 4,555 patients in the last 24 hours took the cumulative tally to 4,39,57,929. Consequently, India’s recovery rate stands at 98.71 per cent.
Vaccine
Meanwhile, India’s Daily Positivity Rate has been reported to be 1.96 per cent, while the Weekly Positivity Rate in the country currently also stands at 1.79 per cent.
Also in the same period, a total of 2,89,228 tests were conducted across the country, increasing the overall tally to over 89.15 crore.
As of Sunday morning, India’s Covid-19 vaccination coverage exceeded 216.56 crore.
Over 4.08 crore adolescents have been administered the first dose of Covid-19 jab since the beginning of vaccination drive for this age bracket.
Artificial intelligence (AI) can be used to detect COVID-19 infection in people’s voices by means of a mobile phone app, according to research to be presented on Monday at the European Respiratory Society International Congress in Barcelona, Spain [1].
The AI model used in this research is more accurate than lateral flow/rapid antigen tests and is cheap, quick and easy to use, which means it can be used in low-income countries where PCR tests are expensive and/or difficult to distribute.
Ms Wafaa Aljbawi, a researcher at the Institute of Data Science, Maastricht University, The Netherlands, told the congress that the AI model was accurate 89% of the time, whereas the accuracy of lateral flow tests varied widely depending on the brand. Also, lateral flow tests were considerably less accurate at detecting COVID infection in people who showed no symptoms.
COVID-19 infection usually affects the upper respiratory track and vocal cords, leading to changes in a person’s voice.
Covid/commons.wikimedia.org
“These promising results suggest that simple voice recordings and fine-tuned AI algorithms can potentially achieve high precision in determining which patients have COVID-19 infection,” she said.Moreover, they enable remote, virtual testing and have a turnaround time of less than a minute. They could be used, for example, at the entry points for large gatherings, enabling rapid screening of the population.”
The app is installed on the user’s mobile phone, the participants report some basic information about demographics, medical history and smoking status, and then are asked to record some respiratory sounds. These include coughing three times, breathing deeply through their mouth three to five times, and reading a short sentence on the screen three times.
The researchers used a voice analysis technique called Mel-spectrogram analysis, which identifies different voice features such as loudness, power and variation over time.
“In this way we can decompose the many properties of the participants’ voices,” said Ms Aljbawi. “In order to distinguish the voice of COVID-19 patients from those who did not have the disease, we built different artificial intelligence models and evaluated which one worked best at classifying the COVID-19 cases.”
Its overall accuracy was 89%, its ability to correctly detect positive cases (the true positive rate or “sensitivity”) was 89%, and its ability to correctly identify negative cases (the true negative rate or “specificity”) was 83%.
“These results show a significant improvement in the accuracy of diagnosing COVID-19 compared to state-of-the-art tests such as the lateral flow test,” said Ms Aljbawi.
The patients were “high engagers”, who had been using the app weekly over months or even years to record their symptoms and other health information, record medication, set reminders, and have access to up-to-date health and lifestyle information. Doctors can assess the data via a clinician dashboard, enabling them to provide oversight, co-management and remote monitoring.
