LONDON/CHICAGO (Reuters) – Predicting “peak virus” is often destined to fail. But that’s not to say it is pointless.
Many health, policy and economic experts worldwide are now trying to do just this with the epidemic of coronavirus disease spreading from China. They are working together to map the curve of the outbreak, but warn there are too many holes in the data to reliably predict when it will reach its peak – when the number of new daily cases starts to decline consistently.
“If anybody tells you when it will peak, it’s not worth it,” said Michael Osterholm, an infectious disease expert at the University of Minnesota in the United States.
Robin Thompson, a mathematical epidemiology specialist at Britain’s Oxford University who has published predictions about this outbreak and is actively working on updating them, agrees.
“In a situation like this where there are so many unknowns, it’s fair to say it’s impossible to predict with any kind of precision at all when the peak is going to happen,” he said.
Indeed, the China Daily newspaper reported this week that Zhong Nanshan, an expert at the Chinese Academy of Engineering who it said was part of the team tackling the outbreak, had amended his previous Jan. 28 prediction that the epidemic would peak in 7 to 10 days to a new forecast that it would now peak within 10 to 14 days of Feb. 2. It gave no details on what had changed the prediction.
Chinese authorities said on Thursday the death toll in mainland China jumped by 73 to 563, with more than 28,000 infections confirmed.
Economists at Fitch ratings agency said on Wednesday that the disease outbreak would have an impact China’s economic growth. Their range of possible scenarios, however, was broad.
“In a scenario where the virus peaks soon and starts to be contained within the next couple of weeks – causing panic to fade rapidly and official restrictions to be dismantled swiftly – there could be a smaller impact,” the Fitch report said.
“Alternatively, if the epidemic is not contained until well into the second quarter, growth could fall more steeply.”
Still, the high likelihood of being vague, or plain wrong, doesn’t render the prediction process redundant.
For everyone, from policymakers and hospital builders to airline schedulers, modelling an outbreak and manipulating predictive models with potential interventions is crucial to strategy planning.
The fundamentals of a disease epidemic model could include factors such as number of known infections, time passed, frequency of travel or human contact, the transmissibility, and any potential mitigating controls like quarantine or screening.
Thompson’s modelling, for example, looked at the potential risk of sustained transmission whenever a case of infection with the new coronavirus arrives in a country outside China.
It made a number of key assumptions, including that the imported case is similar to cases in China and that the new coronavirus is similarly transmissible to the virus that caused SARS. It then predicted the likelihood of sustained human-to-human transmission under degrees of surveillance – detection, diagnosis and reporting – from low-level or ineffective surveillance, to intense surveillance.
“This emphasizes the importance of surveillance efforts in countries around the world to ensure the ongoing outbreak will not become a large global epidemic,” Thompson explained.
“All of these things are what we desperately want to know as public health officials,” said Joe Bresee, an epidemiologist at the U.S. Centers for Disease Control and Prevention and a deputy incident manager on the outbreak.
Mike Tildesley, who develops mathematical models to simulate the disease spread at Britain’s University of Warwick, said one of the mantras of his discipline is that: “All models are wrong, but some are useful.”
And a model’s potential utility ultimately comes down to what the agency or department using it is seeking to do.
“If you are interested in mitigating the effect of a really large outbreak, it’s important not only to look at the average of times when it might peak, but also to look at the worst-case scenario,” he said.
Writing by Kate Kelland; Editing by Pravin Char