Global COVID infections up to six times higher than reported

Photo: Engin Akyurt/Unsplash
Photo: Engin Akyurt/Unsplash

COVID-19 infection rates in the UK, France and Belgium are much higher than reported and up to 17 times higher in Italy, new data shows.

Analysis also shows Australia had the best level of detection among 15 countries at the end of April but the rate of infection may still have been five times higher than what was officially reported at the end of August.  

In a paper published in Royal Society Open Science , researchers from Ikigai Research, The Australian National University (ANU) and the University of Melbourne show infection rates between March and August across 15 countries were on average 6.2 times greater than reported cases.

ANU co-author Professor Quentin Grafton said 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 USA.

"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," Professor Grafton said.

"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.

"In Australia, our modelling shows the actual rate of infected and recovered people 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 possibly infected. That’s much higher than the confirmed proportion of 0.10 per cent of the population.

"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."

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-19 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," Dr 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."  

Professor Grafton 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."

The research is published by Royal Society Open Science .