Global Coronavirus Infections May Be Six Times Higher Than Reported, Finds Study

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Melbourne: The actual number of global infections in the COVID-19 pandemic could be up to six times higher than the reported number of cases, according to a modelling study. The researchers, including those from the Australian National University (ANU) and the University of Melbourne, found infection rates between March and August across 15 countries were on average 6.2 times greater than reported cases.

The data, published in the journal Royal Society Open Science, shows COVID-19 infection rates in the UK, France, Belgium and Italy are much higher than reported and in the case of Italy up to 17-times higher. The analysis also shows Australia had the best level of detection among the 15 countries at the end of April, but the rate of infection may still have been be five times higher than what has been officially reported at the end of August.

The study estimated the true number of infections across a combined population of over 800 million people in 11 European countries, as well as Australia, Canada, South Korea and the US, the researchers said. "We found COVID-19 infections are much higher than confirmed cases across many countries, and this has important implications for both control and the probability of infection," said study co-author Professor Quentin Grafton, from ANU.

"For example, our analysis has found more than 5.4 million in the UK, 8 per cent of the population, are or have been infected with the coronavirus," Grafton said. In Australia, he said, the model shows the actual rate — infected and recovered — at the end of August, may have been five times higher than reported, with 0.48 per cent of the population, or up to 130,000 people may have been infected.

That is much higher than the confirmed proportion of 0.10 per cent of the population, said Grafton. "These findings raise serious questions about how we deal with all facets of the coronavirus pandemic, including ongoing morbidity and life-long health impacts for people who have been infected, how we implement and manage lockdowns, and how we make sure we are on top of this pandemic more broadly," he said.

The analysis used "backcasting", a process that examines COVID-19 related fatalities and compares this with the time from infection to symptoms and time from symptoms to death. The authors state this method allows them to provide a 95 per cent confidence interval around their estimated true (population) infection rate.

"Simply put, we analysed statistics on how many people had died from COVID-I9 in a given country and then worked backwards to see how many people would have to have been infected to arrive at that number of deaths," Steven Phipps from Ikigai Research said. "Our method is a novel and easy-to-use method for estimating the true infection rate wherever there is reliable data on the number of fatalities attributable to COVID-19," Phipps said.

"Our approach is particularly advantageous in locations where there is little testing or limited capacity to forecast rates of infection but where there is a need, for the purposes of public health planning, for a population measure of COVID-19 infection, Grafton added.