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The World Health Organisation (WHO) Director-General Tedros Adhanom Ghebreyesus said that more research needs to be done to better understand the extent to which COVID-19 is being spread by people who don't show symptoms.

"Since early February, we have said that asymptomatic people can transmit COVID-19, but that we need more research to establish the extent of asymptomatic transmission," the WHO chief said at a virtual press conference from Geneva on Wednesday, Xinhua news agency reported.

"That research is ongoing, and we're seeing more and more research being done," he added.

Saying that the world has been achieving a lot in knowing the new virus, the WHO chief told reporters that "there's still a lot we don't

"WHO's advice will continue to evolve as new information becomes available," he said.

Tedros stressed that the most critical way to stop transmission is to find, isolate and test people with symptoms, and trace and quarantine their contacts.

"Many countries have succeeded in suppressing transmission and controlling the virus doing exactly this," Tedros said.

Meanwhile, Michael Ryan, executive director of WHO Health Emergencies Program, said Wednesday that the COVID-19 pandemic is still evolving.

"If we look at the numbers... this pandemic is still evolving. It is growing in many parts of the world," he said. "We have deep concerns that health systems of some countries are struggling, under a huge strain and require our support, our help and our solidarity."

He said "each and every country has a different combination of risks and opportunities, and it's really down to national authorities to carefully consider where they are in the pandemic."

In Europe, the risk issue now are about travels and the opening of the schools, around risk management, mass gathering, surveillance and contact tracing, said the WHO official.

In Southeast Asian countries, where to a great extent transmissions have been under control, governments are more concerned about the re-emergence of clusters, while in South America, the issue of PPE for health workers has not gone away, said Ryan.

As regards Africa, Ryan said the death rates have been very low in the past week, but the health system can be overwhelmed, as it would have to cope with other diseases such as malaria.

New York, Feb 26:  A new wearable sensor that works in conjunction with artificial intelligence (AI) technology could help doctors remotely detect critical changes in heart failure patients days before a health crisis occurs, says a study.

The researchers said the system could eventually help avert up to one in three heart failure readmissions in the weeks following initial discharge from the hospital and help patients sustain a better quality of life.

"This study shows that we can accurately predict the likelihood of hospitalisation for heart failure deterioration well before doctors and patients know that something is wrong," says the study's lead author Josef Stehlik from University of Utah in the US.

"Being able to readily detect changes in the heart sufficiently early will allow physicians to initiate prompt interventions that could prevent rehospitalisation and stave off worsening heart failure," Stehlik added.

According to the researchers, even if patients survive, they have poor functional capacity, poor exercise tolerance and low quality of life after hospitalisations.

"This patch, this new diagnostic tool, could potentially help us prevent hospitalizations and decline in patient status," Stehlik said.

For the findings, published in the journal Circulation: Heart Failure, the researchers followed 100 heart failure patients, average age 68, who were diagnosed and treated at four veterans administration (VA) hospitals in Utah, Texas, California, and Florida.

After discharge, participants wore an adhesive sensor patch on their chests 24 hours a day for up to three months.

The sensor monitored continuous electrocardiogram (ECG) and motion of each subject.

This information was transmitted from the sensor via Bluetooth to a smartphone and then passed on to an analytics platform, developed by PhysIQ, on a secure server, which derived heart rate, heart rhythm, respiratory rate, walking, sleep, body posture and other normal activities.

Using artificial intelligence, the analytics established a normal baseline for each patient. When the data deviated from normal, the platform generated an indication that the patient's heart failure was getting worse.

Overall, the system accurately predicted the impending need for hospitalization more than 80 per cent of the time.

On average, this prediction occurred 10.4 days before a readmission took place (median 6.5 days), the study said.