Mathematical modelling can compute a more realistic picture of the infection rate of the novel Coronavirus that causes COVID-19, enabling better prevention and preparation, according to a team led by an Indian-origin researcher.
Such models can include information reported about the Coronavirus, including the clearly underreported numbers of cases, and factor in knowns like the density and age distribution of the population in an area, the researchers wrote in the journal Infection Control and Hospital Epidemiology.
"Actual pandemic preparedness depends on true cases in the population whether or not they have been identified," said Arni SR Srinivasa Rao from Augusta University in the US. "With better numbers, we can better assess how long the virus will persist and how bad it will get. Without these numbers, how can health care systems and workers prepare for what is needed?" Rao said.
Better numbers also are critical to better protecting the population and overall pandemic preparedness, according to Rao and his colleague Steven G Krantz, professor at Washington University in the US. "We wanted to provide info on the real magnitude of the problem, not just the tip of the iceberg," Rao said. The researchers used their mathematical model, which takes COVID-19 numbers from sources like the World Health Organisation.
They then used factors like an area's population density, the proportion of the population living in urban areas where people tend to live in closer proximity, and populations in three age groups -- zero to 14, 15 to 64, and over 65 -- to grow more accurate numbers. Because this virus is so infectious, they also considered "transmission probability," Rao said.
The researchers also looked at the number of new cases daily above 10 and up to the first reported peak, and the date ranges for those peaks as an indicator of the trend in reported case numbers.
Emerging information about how long the virus survives on a variety of surfaces and in the air will further refine their model, Rao said. They found, for example, that Italy did a comparatively good job of reporting early on, with 1 case reported for every four cases that Rao and Krantz projected by the cutoff date of March 9. That means about 30,223 cases were not reported, according to their model, and Rao noted that Italy had not reached its peak by their March 9 study deadline.
With such a small percentage of people actually being tested in all countries, particularly at that time, South Korea also was reporting one case for about every four likely cases, the researchers said. Spain was reporting 1 case for about every 53 likely actual cases, based on the mathematical model. That translates to about 87,405 cases and people not reported, they said. The two modellers saw some of the higher numbers they projected actually playing out within a week of their study's conclusion in several of these European countries, Rao said.
In China, with its huge population numbers at more than 1.4 billion and widely perceived inconsistencies in data reporting, they projected two ranges for the number reported compared to the actual number of cases. The modellers visualised the disparities between reported cases and what they projected with a Meyer wavelet, which as the name implies goes up, peaks, and then recedes like a wave. In this case, the higher the wave, the higher the underreporting, and lowering the wave means improved reporting, Rao said, of the consistent oscillations generated.
If reported numbers were more precise, mathematical models wouldn't be needed, he said, noting that underreporting is a problem for many conditions, not just COVID-19, including common, noninfectious problems like heart disease. "A model tells us something which has not been directly observed. It's a biological experiment done on computers rather than in a lab," he said. Rao notes the accuracy of reported cases likely has improved since March 9 with the slowly increased availability of testing, and that the earlier the testing, the earlier the actual peaking of infections. He encourages everyone to continue to use steps like social isolation and self-quarantine to protect themselves and others by helping fight continued spread of the virulent virus.
