The COVID-19 pandemic affected the entire world in some way or the other.  |  Photo Credit: iStock Images
In a rapidly advancing globalisation that has turned the entire Earth into one huge village, speedy connectivity and communication also ensured a rapid advance of the COVID-19 pandemic that began with a strain of the novel coronavirus that first emerged in Wuhan, China in late 2019. Now, as per a science paper published in Nature Communications, "The spread of influenza can be modelled and forecast using a machine-learning-based analysis of anonymized mobile phone data. The mobility map, presented in Nature Communications this week, is shown to accurately forecast the spread of influenza in New York City and Australia."
The year 2020 dawned with the world bracing to handle a possible crisis and by the end of the year, global deaths reached nearly 2 million.
To cut the long story short, mankind has now been through so much in terms of mental agony, pain, loss, death, long-lasting illnesses and economic downslide - all on account of this pandemic - despite rapid advances in science - that it has begun to dread the prediction by environmentalists and scientists that we have just entered a pandemic era and more such pandemics are likely to come.
Predicting the onset of a Pandemic:According to a report in the BBC, a team of scientists has used artificial intelligence (AI) to work out where the next novel coronavirus could emerge.
The researchers are reportedly putting to use a combination of learnings from fundamental biology and tools pertaining to machine learning.
This is not mere conjecture and the scientists are taking ahead of what they have gained from similar experiments in the past. Their computer algorithm predicted many more potential hosts of new virus strains that have previously been detected.The findings have been published in the journal Nature Communications.
According to this report in Nature Communications, the spread of viral diseases through a population is dependent on interactions between infected people and uninfected people. The Building-models that predict how the diseases will spread across a city or country currently make use of data that are sparse and imprecise, such as commuter surveys or internet search data.
Dr Marcus Blagrove, a virologist from the University of Liverpool, UK, who was involved in the study, emphasises the need to know where the next coronavirus might come from.
"One way they're generated is through recombination between two existing coronaviruses - so two viruses infect the same cell and they recombine into a 'daughter' virus that would be an entirely new strain."
Scientists say that to get the prediction algorithm right, the first step was to look for species that were able to harbour several viruses at once. Lead researcher Dr Maya Wardeh, who is also from the University of Liverpool, successfully deployed existing biological knowledge to teach the algorithm to search for patterns that made this more likely to happen.
We were able to predict which species had the chance for many coronaviruses to infect them... Either because they are very closely related (to a species known to carry a coronavirus) or because they share the same geographical space.
This step concluded that many more mammals were potential hosts for new coronaviruses than previous surveillance work - screening animals for viruses - had shown.
How could the findings be useful?One thing that seems to be widely accepted is the claim by scientists that COVID-19 is not the last pandemic we are seeing and that scientists believe another pandemic will happen during our lifetime.The scientists say their findings could help to target the surveillance for new diseases - possibly helping prevent the next pandemic before it starts. But the researchers warn against demonising the animal species. They point out that "spill-over" of viruses into human populations tends to be linked to human activities like wildlife trade, factory farming and keeping animals cooped up in unhygienic conditions.
"But it's virtually impossible to survey all animals all the time, so our approach enables prioritisation. It says these are the species to watch," the University of Liverpool researcher added.
The scientists say the "ideal" use of this technique would be to help find viruses as they're recombining.
"If we can find them before they get into humans," said Dr Blagrove. "Then we could work on developing drugs and vaccines and on stopping them from getting into humans in the first place."
As they say, forewarned is forearmed.
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