An equation that was first developed in the 1980s and has been primarily used to help traders foretell stock market activity can also be used to predict when coronavirus cases will reach their peak in different countries, according to a new study in the journal Frontiers in Physics.
Known as q-statistics, the tool is credited to Constantino Tsallis, who worked on the formula as a means of describing the behavior of complex systems – which include everything from high-frequency financial transactions to medieval trading networks, and, it seems, global pandemics.
For decades, the equation has been used to create graphs that describe the probability of stock exchanges. However, Tsallis says that after seeing a graph depicting daily coronavirus cases in China, he immediately noticed a striking similarity between these and the figures used by financial traders.
“The shape was exactly the same,” he explained in a statement.
By applying q-statistics to data from China, where the pandemic is generally considered to have passed its peak, Tsallis and his colleague Ugur Tirnakli developed a formula which they then applied to a number of other severely affected countries, including the US, the UK, Italy, and Brazil.
In their write-up, they explain how this allowed them to accurately predict the peak date for daily cases and deaths in each country, correct to within one week.
"The functional form seems to be universal," said Tallis. "Not just for this virus, but for the next one that might appear as well." The implication here is that by utilizing q-statistics it may be possible to pre-empt the spread of future pandemics, thereby giving health officials a head start in their attempts to introduce policies to protect populations.
The authors note that while their formula did also predict the severity of the peak in each country, this was less accurate than their forecasting of the date. This, they say, is probably because some countries employed more successful public health strategies than others, with the effectiveness of social distancing regulations having a significant effect on the number of people infected in each country.
Interestingly, they also point out that South Korea was the only country for which their equation did not work, indicating that their tool may need some fine-tuning before it can be totally relied upon to map the spread of any future pandemics.
It is also widely accepted that official statistics in each country are likely to be inaccurate as huge numbers of cases go unreported, and the study authors acknowledge that this also casts a shadow of doubt over their findings.
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