Tracking variants of Covid-19 through genetic sequencing helped scientists understand more about the virus, how to contain it and what treatment worked best, an online public health forum heard.
Doctors and researchers discussed how data modelling and artificial intelligence to track known cases and mutated variants of the virus aided efforts to combat new infections.
A huge amount of "clean" data from public testing, vaccinations and population studies have proved critical in containing new outbreaks when spikes occurred, experts told the Covid-19 Response public health conference, supported by the Abu Dhabi Health Centre.
The scientific methods were discussed by experts from Mohammed Bin Rashid University, in Dubai, and Khalifa University, in Abu Dhabi, doctors on the front line and geneticists in an “unprecedented” level of collaboration.
“There are a number of emerging strains of interest from the UK, Brazil and South Africa – and also California and New York,” said Hanif Khalak, director of technology at G42 Healthcare, an Abu Dhabi-based research company that aided phase three trials of the Sinopharm vaccine.
"The more data we can work on will benefit the UAE, considering how fast the virus is evolving.
“As it continues to mutate, novel variants will be discovered, so genome sequencing and surveillance is key.
"There is likely to be variation between both virus and genetic makeup, so that is likely to make people respond differently to the virus and its treatment," Mr Khalak said.
UAE builds picture of Covid-19
Genetic sequences of the novel coronavirus were tracked as early as January 2020 in Dubai, as scientists tried to understand more about the emerging virus first reported in China.
The genomic epidemiology of the first 50 cases of Covid-19 were recorded in the UAE between January 29 and March 18.
Reported cases at the end of January showed 24 per cent had direct origin in Asia, with 28 per cent clustered with Iranian strains of the virus in mid to late February.
Into March, and 48 per cent of cases were linked with European strains of Covid-19.
Clinical characteristics of those infected revealed 37 per cent were overweight and 28 per cent obese.
Of those, 74 per cent were aged 15-49, 18 per cent had recently travelled and 16 per cent reported contact with a recently infected person.
Doctors evaluating the impact of the virus in the early cases recorded in Dubai found those aged over 40 were twice as likely to be hospitalised than younger people with coronavirus.
Experts at the Mohammed Bin Rashid University of Medicine and Health Sciences also found obese or overweight patients, and those with diabetes, were up to four times as likely to suffer severe symptoms and require intensive care.
The data was vital in enabling medics to understand who was most at risk, in order to shape the current vaccination programme.
“We found there were different levels of viral load with different strains, we could see that from the data,” said Mr Khalak.
“From a public health perspective, if we knew where and how these mutations were running through at a national level, we could understand the relationships between people and the variants they had, so we could do tracing.
“This was essential to know the transmission line between people we could identify so we could contain and understand those paths.”
Scientists noted the impact of artificial intelligence to understand the huge amount of data that became available, as more was understood about Covid-19.
Data sharing key to overcoming pandemics
They also said the pandemic exposed a “myriad of inadequacies” of healthcare data and how it was shared.
An estimated 150,000 publications on the virus are expected in 2021 – one every three minutes – creating huge challenges for healthcare analysis.
Dr Anthony Chang, chief intelligence and innovation officer at Children’s Hospital of Orange County in the US, said there was a “dire need” for an agile clinical science approach in dealing with future pandemics.
“Any approach needs to be done in real time, so caretakers have access to the information they so badly need to care for those afflicted with infection,” he said.
“There is a mismatch between a data science approach to epidemiology and the complex nature of pandemics with its high degree of human behaviour and biomedical uncertainty.”
