COVID-19: What do we really know about the virus? Part 1

In my last post, I talked about why COVID-19 science is so confusing and how we can cut through the noise. Now I’m going to put my money (or at least my virtual word count) where my mouth is and follow my own advice: look at the science behind the numbers in the media. We have accumulated 7 months worth of data and scientific papers and I’m going to look for the answers to the most pressing questions that bug me (and many others, no doubt) about COVID-19 in a series of posts. If you have questions that you’d like answered, leave them in the comments!

Let’s start with the most important one..

How deadly is COVID-19?

One of the most important aspects for any disease is how deadly it is. For infectious diseases it is usually measured in two ways: 1) case fatality rate (CFR) – % of deaths out of all diagnosed cases and 2) infection fatality rate (IFR) – % of deaths out of all infected individuals. CFR will always be bigger or equal to IFR because IFR takes into account all the asymptomatic and potentially undiagnosed cases. Because of that IFR is also the more accurate measure and harder to measure.

At the beginning of the pandemic we heard CFR numbers as high as 10% and later, as testing ramped up, the numbers became smaller, but still pretty high, around 3%. Later, studies that looked at antibodies suggested that actually CFR and IFR are as low as 1% and lower. So what is our best estimate as of today?

Here I looked at studies estimating the IFR, because, as I discussed above, it is a more realistic representation of the deadliness of a virus. There are several ways to estimate and measure the IFR. 1) Closed population study – measure IFR on a contained population where everyone is tested, such as a cruiseship 2) Antibody study – measuring antibodies in a random, representative sample of the population and then extrapolating to the whole population to see what the total number of infected people might be and then estimating IFR from that number 3) Simulation study – creating a mathematical model of virus spread to estimate the actual number of infected people and then IFR. 4) Meta-analysis – this is one study to rule them all. It takes all available individual studies that tried to estimate IFR and using statistical modelling it finds the overall IFR. Luckily, all four types of studies of COVID-19 already exist, so we can compare estimates from four different methodologies.

TL;DR

The estimates from three of the studies are remarkably similar and place COVID-19 IFR at ~0.65% (for each 100,000 infected people there will be 650 deaths). The forth study, gives a higher estimate (1.3%) but its confidence interval (the interval within which the true IFR is likely to be in) is quite large and overlaps the estimates and the confidence intervals from the other studies. Also notable that the lower boundary of three of the estimates is hovering around ~0.38%, which means the true IFR is less likely to be lower than that.

Infection fatality rate (IFR) estimates from studies with four different methodologies. The blue boxes show the Confidence Intervals (the interval within which the true IFR is likely to be in) and the black dots the actual estimate of the IFR.

COVID-19 mortality rate changes with age. The older you are the higher the chances of mortality. For children, for each 100,000 infected, there will be 1-2 deaths. For those 65+, for each 100,000 infected, there will be 6000 deaths. This means that the overall number of deaths from COVID-19 and its deadliness is highly dependent on who gets infected.

How does this number compare to the flu? The estimated CFR (based on symptomatic cases) for the 2018-2019 for the flu is 0.096% (for each 100,000 cases there will be 96 deaths). The IFR would be equal to or lower than this number. So currently COVID-19 is 6-7 times deadlier than seasonal flu. Like COVID-19, flu CFR increases with age. For kids the CFR is 0.003% while for adults 65+ it is 0.83%. When COVID-19 treatments or/and vaccines with be introduce, the virus will become less deadly.

Detailed study analysis

1) Closed population study – The case of the Diamond Princess cruise ship

Diamond Princess is a cruise ship that was quarantined by the coast of Japan during the month of February due to COVID-19 infection on board. There were 2,666 passengers and 1,045 crew on board. By March there were 696 positive cases, out of which ~50% were asymptomatic. Because of the high testing rate of the people on the ship (3,063 tests were performed,) British epidemiologists were able to estimate the IFR for COVID-19. They estimated the overall IFR to be 1.3% (95% confidence interval (CI): 0.38–3.6). IFR varied by age: for those older than 70 the IFR was 6.4% (CI 2.6–13) [1]

2) Antibody study – estimating IFR in Geneva Switzerland

I chose this study out of many others based on antibodies, because it broke down the IFR by age and also improved on many issues that plagues earlier studies of this type (according to Dr Kilpatrick, an infectious diseases researcher from UC Santa Cruz). Note that this is a preprint, so it has not yet been officially reviewed by journal reviewers.

In the study, American and Swiss epidemiologists estimated the number of infections using antibody testing repeated weekly 5 times [2]. They estimated the IFR for different ages using the antibody prevalence data and registered deaths data:

Age groupIFR(%)
5-90.0016%
10-190.00032%
20-490.0092%
50-640.14%
65+5.6%
All ages0.64% (CI 0.38-0.98)

3) Modelling study – estimating IFR using statistical modelling of data from China and other countries

This study [3] was chosen because it was already published in Lancet, the top medical journal, and because it combined data from multiple sources and broke IFR down by age.

Researchers from Imperial College London, collected detailed data on cases and deaths in China and people repatriated from China as well as cases from 37 other countries that occurred by the time the analysis was done [3]. They used various types of estimation (e.g. estimated onset-to-death time, infection ratio in people repatriated from China, because all of these people were tested, etc) and modelling to obtain IFRs for different age groups:

Age groupIFR(%)
0-90.0016%
10-190.0069%
20-290.03%
30-390.084%
40-490.16%
50-590.59%
60-691.93%
70-794.28%
80+7.8%
All ages0.66% (CI 0.39–1.3)

4) Meta-analysis – an estimate based on 26 studies from different countries

Epidemiologists from the University of Wollongong selected 26 qualifying studies out of more than 260 records found in various scientific databases. Using the estimates from these 26 studies and statistical modelling, they estimated the IFR rate to be 0.68% (Confidence interval: 0.53-0.82%) [4]. However, the authors noted that the 26 estimates were very heterogeneous (different from each other) and that can undermine the estimate they made based on these studies. The authors conclude:

The main finding of this research is that there is very high heterogeneity among estimates of IFR for COVID-19 and therefore it is difficult to draw a single conclusion regarding the number. Aggregating the results together provides a point-estimate of 0.68% (0.53-0.82%), but there remains considerable uncertainty about whether this is a reasonable figure or simply a best guess. It appears likely, however, that the true population IFR in most places from COVID-19 will lie somewhere between the lower bound and upper bounds of this estimate.

Sources:

[1] Russell Timothy W et al. “Estimating the infection and case fatality ratio for coronavirus disease (COVID-19) using age-adjusted data from the outbreak on the Diamond Princess cruise ship”, February 2020. Euro Surveill. 2020;25(12):pii=2000256. https://doi.org/10.2807/1560-7917.ES.2020.25.12.2000256

[2] JavierPerez-Saez et al. Serology-informed estimates of SARS-COV-2 infectionfatality risk in Geneva, Switzerland

[3] Verity, Robert, et al. “Estimates of the severity of coronavirus disease 2019: a model-based analysis.” The Lancet infectious diseases (2020).

[4] Gideon Meyerowitz-Katz, Lea Merone (Preprint) A systematic review and meta-analysis of published research data on COVID-19 infection-fatality rates

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