A mathematical model that can help project the spread of infectious diseases like the seasonal flu may not be the best way to predict the spread of Covid-19, researchers, including one of Indian-origin, have reported.
Called the R-naught, or basic reproductive number, the model predicts the average number of susceptible people who will be infected by one infectious person.
It is calculated using three main factors — the infectious period of the disease, how the disease spreads and how many people an infected individual will likely come into contact with.
Historically, if the R-naught is larger than one, infections can become rampant and an epidemic or more widespread pandemic is likely.
The Covid-19 pandemic had an early R-naught between two and three.
In a letter published in the journal Infection Control and Hospital Epidemiology, researchers have argued that lockdowns that have become necessary to help mitigate the Covid-19 pandemic have complicated predicting the disease’s spread by altering the normal mix of the population.
Arni Rao, a mathematical modeller at the Medical College of Georgia at Augusta University in the US and his co-authors instead suggested more of a dynamic, moment in time approach using a model called the geometric mean.
That model uses today’s number to predict tomorrow’s numbers.
Current number of infections — in Augusta today, for example — is divided by the number of predicted infections for tomorrow to develop a more accurate and current reproductive rate.
While this geometric method can’t predict long term trends, it can more accurately predict likely numbers for the short term, the researchers said.
“The R-naught model can’t be changed to account for contact rates that can change from day to day when lockdowns are imposed,” Rao explained. (IANS)
