Coronavirus: our study suggests more people have had it than previously estimated
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Many people suspect that they are now infected with COVID-19, although COVID-19 was actually diagnosed in only 0.5% of the UK population. Similar numbers have been reported in other countries. How many people actually had it is unclear. There is also uncertainty as to what percentage of people who receive COVID-19 will die as a result, although many models assume it is around 1%.
We believe there is too much confidence in reporting infection prevalence and death rate statistics for COVID-19. Uncertainties in the data and explanations for them are not taken into account in such statistics. In our new article, peer-reviewed and accepted for publication in the Journal of Risk Research, we developed a computer model that takes these uncertainties into account when estimating COVID-19 death rates. And we see a completely different picture.
Our model, known as the Bayesian Network, allows us to combine multiple data sources and assess how sensitive infection prevalence and death rate are to two dominant sources of uncertainty.
One of them is the accuracy of serological (antibody) tests, which is critically dependent on our ability to accurately measure whether a person has antibodies. We take into account factors such as false positive or negative rates for manufacturer test kits.
We also take into account the reliability of death data. This is important because the death rate, the probability of death of a patient infected with Covid-19, is defined as the number of deaths divided by the number of infected people in the community. If one of these variables is uncertain, all political decisions based on the resulting death rate are themselves unreliable or potentially dangerous.
Both factors are much less certain than reported. If we take them into account in our model, we have found high infection rates in the community in many regions of the world. For Kobe, Japan, our model showed that over 800 times more people had COVID-19 than reported. For England and Wales this number is 28 times higher.
The Imperial College team in the UK, which advises the British government, estimates the death toll at 1%. But that is uncertain. The team states that its model "is based on firm estimates of some epidemiological parameters such as infection mortality rate ..." while recognizing that "given the ongoing pandemic, we are relying on incomplete death data, with systematic bias in reporting and subject to future consolidation" .
When we resolved these uncertainties, we found that death rate estimates for the countries / regions we looked at were most likely in the 0.3% to 0.5% range.
Illustration of the new corona virus. Andrii Vodolazhskyi / Shutterstock
Although not covered in our study, we also applied our model to New York data. Here the "actual" number of deaths in NYC is given as 23,430, with an estimated death rate of 1.4%. However, when the data is entered into our model, the death rate estimate can be adjusted to a range between 0.6% and 1.3% - possibly half of the official number.
Uncertainties in the number of deaths
How can we explain these uncertainties? Each country calculates deaths differently - which is a problem at first. In many countries, the number of "actual" deaths is estimated by adding confirmed deaths where COVID-19 is listed on the death certificate in addition to a positive COVID-19 test result, deaths where COVID-19 is on the death certificate, however, none had a test and a statistical estimate of the “excessive deaths” (how many more deaths were probably there than normal).
For example, in New York City, the "actual" number of deaths is the total of 13,156 confirmed deaths where COVID-19 is listed on the death certificate in addition to a positive COVID-19 test result, 5,126 deaths where COVID-19 is listed on the death certificate but is where no test took place and 5,148 excessive deaths. However, we do not know whether some of these people have died from "with" or "with" COVID-19. Many of these deaths are said to be "factual" when they are actually highly uncertain.
In addition, excessive deaths are often calculated by comparison with the previous five years, except for years with "bad" influenza seasons - which is a problem. COVID-19 can also accelerate deaths that are imminent. And if the effects of the blockage prevent people with serious medical conditions such as strokes and heart attacks from accessing and dying, there is a risk that COVID-19 inclusion as "excessive deaths" may have seriously overestimated them.
This type of research is worth considering when discussing whether we are close to herd immunity or whether a "second wave" of the virus is likely. Using Sweden as an example, antibody studies show that 7% of COVID-19 occurred much more frequently a few weeks ago than confirmed cases that were proposed at that time. However, this is still far from the 65% who are believed to guarantee herd immunity. If Sweden has not reached herd immunity and has not ordered a blockade, why not increase the death toll?
A controversial explanation, which we did not consider in our study, is the existence of "dark matter of the antibody", which does not appear in antibody tests, but nevertheless offers some protection against the virus.
The immune system includes two types of white blood cells: T cells and B cells. But only B cells produce antibodies. Studies show that immunity from previous infections that are similar to COVID-19, like SARS-v1, can develop more quickly through T cell immunity than B cell immunity. This means that many people may have had a corona virus but did not develop antibodies - which led to an underestimation of the number of infections, even in our model.
While a recent study claimed that around 10% of the population of England and Wales could have been infected, the actual number could be even higher.
It is clear that we cannot fully trust death and infection rate statistics until we have more accurate data and include it in a model like ours.
This article is republished by The Conversation under a Creative Commons license. Read the original article.
The authors do not work for companies or organizations that would benefit from this article, and do not consult, or receive funding from, stocks. They have not disclosed any relevant affiliations beyond their academic appointment.
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