UPDATED! April 3, 2020. The coronavirus mortality rate varies significantly by country. In this post, I look at the mortality rates for ten countries and assess factors that affect these numbers. After discussing the trends, I provide a rough estimate for where the actual fatality rate might lie.
To start, take a look at the current mortality rates for ten countries and the worldwide rate. These countries are the same ones I’ve been tracking in my article about coronavirus curves and different outcomes.
| Country | Rate |
| Italy | 12.1% |
| Spain | 9.2% |
| World | 5.2% |
| China | 4.0% |
| India | 2.8% |
| US | 2.4% |
| Japan | 2.4% |
| South Korea | 1.7% |
| Taiwan | 1.5% |
| Germany | 1.3% |
| Singapore | 0.4% |
That’s a large range of values! However, we can learn even more when we look at these numbers over time.
This chart is a bit messier than my graph of confirmed cases. Don’t worry. I’ll cover what to look for below!
I start each country on the graph at the point when they have at least 20 cases—just as I do for my chart of confirmed cases. I deleted 0% values that occur very early to declutter the graph. The X-axis indicates the number of days since 20 cases. The Y-axis is the mortality rate as a percentage. The formula for calculating mortality rates is the following:
Johns Hopkins provided the data and they are current up to March 27, 2020.
How Do We Interpret these Mortality Rates?
What do these numbers tell us? Here are some important caveats!
These data reflect a sampling of individuals who sought testing, tested positive, and then subsequently died. That’s a very specific group of people. Consequently, mortality rates calculated from these data represent the percentage of individuals who died after testing positive.
That’s not quite what we want to know. We’d really like to know the overall mortality rate in the population—which brings us to the statistical concept of generalizability. Can we take the results of this sample and generalize beyond it?
When scientists conduct studies, their goal isn’t to understand just a particular group of participants. For example, in clinical trials for a new medication, scientists want to understand its effectiveness in the general population rather than just the relatively small sample of study subjects. In other words, they want to generalize the results from the sample to the population.
In statistics, generalizability depends on certain conditions. The most commonly known of these conditions is random sampling. However, there are various other conditions, which I discuss in detail in my Introduction to Statistics ebook.
The conditions under which health care workers collected the coronavirus data satisfy generalizability requirements to varying degrees in different countries. Consequently, countries with similar circumstances can appear to have different mortality rates. While these numbers are not perfect, we can learn a lot from them and even derive a rough estimate of the actual mortality rate.
Sampling Problems and Selection Bias in the Mortality Data
I’m sure you’ve heard there has been inadequate testing in various countries. However, this problem doesn’t only cause underreporting. It also biases the results. Individuals seeking testing are nowhere near to being a random sample that reflects the larger population.
When a disease is new and tests are scarce, there is no routine testing of individuals. Instead, severely ill people are likely to show up at a hospital seeking a coronavirus test. In more severe conditions, hospitals won’t even test all incoming patients, but only a subset of those with the most critical symptoms. Meanwhile, those with milder conditions are not tested.
Under these conditions, the mortality rate among those who are tested is higher than those who are not tested. For the fatality rate, the number of confirmed cases in the denominator does not include milder cases. Consequently, many of these rates calculated from the available data are higher than the real value for a general population that includes milder cases. This positive bias means we can’t always generalize from the sample data (tested individuals) to the general population.
As the scope of testing increases, countries can include milder cases, which lowers the mortality rate. The graph shows falling mortality rates for both South Korea and the United States, and increased testing is a likely reason.
On the other hand, Germany has consistently tested individuals both with severe symptoms and those with milder cases, which introduces less bias. On the graph, Germany has a relatively low mortality rate, which has increased from 0.17% to 0.69%. Germany might provide a good indication of the true mortality rate for coronavirus.
Similarly, South Korea has had large-scale testing very early on. Their mortality rate ranges from 0.46% to 1.38%. Interestingly, it started at 1% on February 6th, decreased to a minimum of 0.46% on March 1st, and has since increased to 1.52%.
Related post: Populations, Parameters, and Samples in Inferential Statistics
Overloaded Health Care Systems
In other countries, overwhelmed health care systems can increase the coronavirus mortality rate. In these countries, coronavirus patients are more likely to die because the inundated systems don’t have the resources to provide lifesaving care. For example, there can be shortages of ICU beds and ventilators, among other critical resources.
Shortages in medical care likely explain the sharply increasing mortality rates in both Italy and Spain.
The early rates in China were similarly caused by the inability of hospitals to handle the high rate of patients. I’ve read reports that the initial Chinese rate was 4%. However, I don’t have data for those early days. My data begin on January 22, 2020. Interestingly, like South Korea, the Chinese mortality rate shows a similar pattern of an initial decrease followed by a rise. China had a 3.1% mortality rate on January 22nd. That rate decreased to a minimum of 2.05% on February 5th, after which it increased up to 4.03%.
Undoubtedly, both overwhelmed hospitals and selection bias play a role in the high rates for Italy, Spain, and China.
Related post: Coronavirus: Exponential Growth and Hospital Beds
Coronavirus Mortality Rates and Population Characteristics
The characteristics of the infected population can affect mortality rates. For example, the first cases in Washington were among the elderly in nursing homes, which raised the fatality rate. As the coronavirus spread to younger populations, that rate has fallen.
As the virus moves from between various subpopulations with a country, it can affect the overall mortality rate. Perhaps this explains the mortality rate curves on the graph that change directions?
Mysteries and the True Coronavirus Mortality Rate
As you can see, a variety of factors affect the mortality rate within countries. Additionally, we can’t always generalize the mortality rates from the sample data to the general population. These data aren’t perfect and it’ll take much more study to determine the real coronavirus mortality rate. However, we are able to glean significant trends and information from these data.
A mystery for me are countries like Japan, South Korea, and China that had a declining mortality rate, reached a minimum, and then started a systematic increase. Perhaps that reflects the virus spreading to more vulnerable groups?
How do we estimate the coronavirus mortality rate that we’d see when the data aren’t biased and health care systems are not overwhelmed? What’s the rate we’d calculate if we could include all coronavirus cases in our dataset—from the very mild to extremely severe? We can get some hints about that from the graph!
The United States had inadequate testing and is beginning to experience health care shortages, and yet its rate had gone down to 1.2%. Let’s call that a good upper-limit for now. Additionally, countries that have had widespread testing and no overburdening of health care systems have sustained rates that fall between 0.5% and 1%. Let’s consider that a most likely range. Finally, and tantalizingly, Singapore has a tiny mortality rate that is currently at 0.27%. For now, let’s say that this is the lower-bound.
Based on these data, a rough estimate is that the coronavirus mortality rate falls between 0.3% and 1.2%, with it more likely falling between 0.5% and 1%. If it falls within the narrower range, that’s 5 to 10 times more fatal than the flu! However, these ranges are educated guesses. Scientists will require much better data to nail it down!
Thanks so much for your great website. Have learnt a great deal from it.
One thing that has long interested me, and for which Governments, at least here in Australia, have avoided providing any information, is what locations most coronavirus transmissions occur. By locations, I mean places like aged care facilities, shopping centres, small retail outlets, hospitals, bars / restaurants, sports venues, schools / universities, beaches etc.
Death numbers and death rates (per million etc) are no doubt of great influence and of use for comparison purposes but not always the most important. By way of example, in my home State of Victoria, Australia, we have recorded 750+ deaths, but over 80% of these have been in aged-care facilities and, perhaps, another 10% in hospital transmissions. In other words, ‘only’ 10% of cases have been community transmitted. In other jurisdictions, the reverse has been the case.
I feel such information would be invaluable in planning lockdown arrangement. We have been in Stage 4 restrictions for 2 months (schools, retail outlets, bars, restaurants, playgrounds etc closed, 8pm curfews, no travel further than 5kms from home, only 1hr per day out of the home for exercise only, no contact with friends or relatives…..). I just wonder whether this extreme ‘lockdown’ has been justified based on very low community transmissions rates (before the Stage 4 restrictions).
Is such information available? Do we know where people are most likely to be infected?
I know if you don’t have it, no-one does!
Kind regards….Stuart (Melbourne, Australia)
The reported cause of death is a matter of debate it seems. Some countries doesnt report COVID19 deaths as such unless they died at a hospital. Deaths at nurseries and at home seems to be grossly underreported. However it seems like Sweden does a good job reporting those deaths also as COVID19 deaths if they have shown any suspicious symptoms. This pushes the Swedish death figures up while others are probably way to low.
The CDC in the U.S. has started this practice as well. Even if the number of Swedish deaths is higher than it would’ve been using the same methods as some other countries, there is still an upward trend in the daily number of COVID19 deaths, which should be worrying. It’s worrying the number of cases and deaths are both increasing. Coronavirus has shown that it can quickly start getting out of control.
Hello Mr Frost,
Would you or any other statistically minded colleagues have any observations on the following graph. Irish Govt. say it’s evidence that the lock down measures are slowing the growth rate. Unfortunately the background data and underling assumptions have not been published.
https://twitter.com/SimonHarrisTD/status/1251238665696808960
Thanks so much.
Hi,
I’m a teacher of middle school math. I’d really like my students to read an article about the “average” age of patients or “average” age of mortality due to covid to stimulate a discussion on the effect of outliers on mean vs median and mode and the importance of reading and looking critically at graphs. Do you have anything geared toward this age group or suggestions on what resources I might look to for building this lesson? I believe that this article is a little above their current abilities.
Hi Jim,
Great job!
Would you consider to regularly update the “mortality by countries” graph? It seems nobody else does that – or at least nobody publishes it.
Mortality per 100k inhabitants by countries would be the icing on the cake….
Hi Dee,
Thanks! I haven’t been updating this particular post because I’ve updating another coronavirus post regularly. That other post has the current mortality per 100k you’re asking for! It doesn’t track it over time but has the current values for these countries. Please see my post for Coronavirus Curves and Different Outcomes to see those. I have them in a table near the beginning.
Hi Jim, thanks for your response. I guess my point all along is a matter of “picking your poison” no pun intended. which way is better? even with the flu, it has to be very difficult to attribute whether it was the flu or not that precipitated someone dying, I suspect the flu statistics are extremely low only because assume you have an 88 year old in a nursing home and they catch the flu and die, then it is probably not counted as the flu. many times they do not know anyway, that they caught the flu. So, the fact is that there may be 20,000 deaths attributed to COVID 19 and half of them were VERY sickly, and although we do not know, lets assume that 70% of those would have died 1 year. that leaves 7000 where it is debatable whether to say COVID 19 caused it. my way, which is to subtract and allow for all other deaths (something a good modeler SHOULD BE ABLE TO DO) can get a potentially closer estimate (or perhaps as good). so, even if they are about the same regarding margin of error (and you are right, we don’t know), one complements the other. thus, if there are 100K world wide deaths, there are people that believe it might be half that, and to be honest nobody really knows. but if the method I am espousing suggests 97K deaths, then it seems our confidence in that 100K is going to go up. but what surprises me is that NOBODY is talking about the method I am endorsing (at the very least as a complement).
Hi John,
I think the problem is more an undercounting of deaths from COVID19. And, that undercounting been recognized, as discussed in this Washington Post article.
“You can’t rely on just the laboratory-confirmed cases,” said Marc-Alain Widdowson, an epidemiologist who left the CDC last year and now serves as director of the Institute of Tropical Medicine Antwerp in Belgium. “You’re never going to apply the test on everybody who is ill and everybody who dies. So without doubt — it’s a truism — the number of deaths are underestimated globally because you don’t apply the test.”
I think the problem you’re describing is the opposite and seems to be smaller amount than the described underestimation. Someone tests positive, is in the hospital, and then dies. How many of those truly died from COVID19 versus something else? That’s also mentioned in the Washington Post article:
Marc Lipsitch, a professor of epidemiology at Harvard, said there are probably some people dying with covid-19 who are not dying of covid-19. Such misattribution is a problem for any cause of death, he said, but it is a minor issue that is “swamped by the opposite problem: deaths that are caused by covid but never attributed, so the death count is underestimated.”
From hospital reports, it sounds like there is a typical course of someone who presents with COVID19 symptoms, tests positive for COVID19, is admitted to ICU because of the COVID19 symptoms, other diagnostics are performed such as lung imaging, and then dies from those symptoms. It’s a common and recognizable sequence of events and diagnostic results that is attributable to COVID19. It’s fairly concrete that COVID19 contributed to their deaths. They might well have underlying conditions that also contributed. But, the medics note the nature of the deaths in conjunction with the extreme symptoms that cause those deaths. Perhaps a few of them would’ve died without COVID19 and they just happened to catch COVID19 and have all the same exact COVID19 symptoms, but actually died for other reasons. But the extreme coincidence of all that would have to be a small number.
The larger problem with estimating the number of COVID19 deaths is the underestimation due to the shortage of tests, supplies, and human resources to do all the testing during the pandemic crisis. For every 100,000 confirmed COVID19 deaths, there is actually a larger number of COVID19 deaths. Although, I wouldn’t even begin to guess the actual number–not less.
I do think later studies, including using the method you mention, is something that can be performed later after the crisis passes. It will be interesting to see what those studies show! I think it’s just too early right now. For one thing, you can’t estimate the number of pandemic deaths when the pandemic isn’t over yet.
In short, I think you have a good idea. But, we’re still in the thick of the crisis!
This is exactly what I think too.
It appears as if mortality rate among confirmed cases is 21%
(Total deaths)/(total closed cases)
I mean
(Total deaths)/(total deaths + total recovered)
It is world average as well as indias
Hi Wink
No medical professional thinks the mortality rate is anywhere near 20%! Yes, you get that value for most places if you calculate for various places around the world, but it is incorrect.
Hi Jim,
any plans to add Belgium to the affected countries ? We are on position 3 regarding number of deaths/100.000 inhabitants….
I also invite you to check and/or connect with Professor Ingmar Nopens’ research from University of Ghent and his request for forming a multi disciplinary team regarding Corona counter measures and prediction models.
Thank you 🙂
China and others: Perhaps that reflects the virus spreading to more vulnerable groups?
Your next research could include the graphs of affected different ages in time.
That will tell whether some die sooner than others.
Have you looked into the BCG vaccinations? Another project?
And do you agree with security agencies about the data from China?
They have experience with Sars and their rates are not that different compared to other countries in the region?
Thanks!
Hi Liem,
The virus spreading to more vulnerable groups is one possibility that I mention in the post. Right now I don’t have good enough data to answer those questions. However, I’m sure that after we get through the pandemic, researchers from around the world will be looking into all sorts of questions like these. Hopefully, at that point we’ll have better data and more time!
On the one hand, I do have some concerns about the data from China. However, on the other hand, there’s no smoking gun that I can see which indicates they’re manipulating the numbers. As you say, their rates aren’t so different from neighboring countries. I’m also aware that they too Herculean efforts to contain the virus (eventually). Despite those austere measures, it still took a long time to control the virus. That all makes sense.
Hi Jim, i had a doubt regarding the interpretation od death rate. Shouldn’t the death rate be calculated on the basis of closed cases? By that i mean as per the site worldometer there are around 260000 closed cases with around 53000 deaths, doesn’t that make the death rate around 20%? Am i missing something here?
Link to the site-
https://www.worldometers.info/coronavirus/#countries
Hi Jagrit,
There are different ways of calculating mortality rates. They have the pros and cons, and some are more appropriate at different times/situations. Currently, the majority of cases are not resolved. Using closed cases as you suggest gives us completely unrealistic estimates. No one in the medical field thinks the mortality rate is anywhere near 20%! At this point, it’s more accurate to use all deaths/all cases. That method should asymptotically approach the correct value. Based on the information we have at hand, it’s the best we can do. In my article, I discuss the factors that affect these mortality rates based on data difficulties. What we have isn’t perfect, but we can still learn.
Hi- How often do you update corona stats? Thanks!
I’ve been meaning to update these! I update the stats on my other coronavirus post daily. I will update these very soon and probably add additional graphs! Thanks for asking!
What John Grenci is talking about in terms of “total deaths” has been done after Hurricane Katrina and after the hurricane in Puerto Rico. If I’m remembering correctly, it was done because reporting of the cause of deaths was not accuratel. So epidemiologists determined (from health records) the number of people who died around the same time in the year prior to the hurricane in PR and compared that to the number who died around the same time in the year of the hurricane. “Excess” deaths were then assumed to be the result of the hurricane and its aftermath.
SARS-CoV-2, our current pandemic virus, is not as novel as the media portrays. There’s already a wealth of data and analysis from the SARS 2003-2004 epidemic we can use to inform our decision making. While researching I often have to backtrack and find the date to remind myself I’m looking at SARS1 or 2.
Hi Lauren,
While the two viruses are related, there are some crucial differences. For example, from the NIH:
“If the viability of the two coronaviruses is similar, why is SARS-CoV-2 resulting in more cases? Emerging evidence suggests that people infected with SARS-CoV-2 might be spreading virus without recognizing, or prior to recognizing, symptoms. This would make disease control measures that were effective against SARS-CoV-1 less effective against its successor.”
The mortality rate goes back up because the virus is now concentrated around the hospitals and is the primary vector for transmission. Most if not all of the testing is done in the hospitals. Therefore anyone who ends up in a hospital and dies will have a much higher likelihood of contracting the virus prior to death. All of these are now counted as a Covid death. This is akin to taking your temperature next to a burning building. The number will skew higher until you reach a peak of transmission to all deaths in the hospital that would have happened prior to the virus being identified. Basically, you go to the hospital with heart failure. Get the virus in the hospital. Die and now you are a Covid statistic.
Highly insightful, Jim.
Always simplifying complex matters in statistics!
Nice work;Mortality rate is as true as the denominator. If extensive testing counts even the 80-85% who are asymptomatic and don’t require trewtment in hospitals then the numbers may not reflect true rates of mortality visavis rexocery. Adding to the denominator could also be a state’s ploy to fudge.
Not everybody has a dry cough or fever (at least in Germany). But a difficulty in breathing is a bad sign. Go and get testet. All the best and get well soon.
Great analysis of mortality under various situation of testing adn characteristics of population tested. Very insightful.
As we know that the older people are at the highest risk. As from 2017 data it is known that the Italy has 2nd highest percentage people with age greater than 65(23%).
Hi Vinaykumar, Unfortunately, I’m sure that’s contributing to Italy’s high mortality rate. Although, it doesn’t necessarily explain why the rate has grown over time.
An informative and appreciated read. Thanks, Jim!
Hi Jim,
Thank you for this helpful article. In Michigan, where I live, the mortality rate for confirmed cases seems to be hovering around 2.5%. However, since we don’t know the actual number of cases I agree that the actual mortality rate has to be less than this. If your estimation of 0.5-1% for the true mortality rate is correct then that suggests that the actual number of cases may be between 2.5 to 5 times the number of confirmed cases.
In our state the number of confirmed cases is about 0.05% the population of the state. Using these numbers actual cases could be up to around 0.25% of the population for the state. The problem for us is that about 85% of the cases in the state is centered on the city of Detroit and its surrounding counties, which has taxed the resources and personnel in the epicenter of the outbreak for us.
Hey Jim Sir.
If a guy aged 22 is having difficulty in breathing, but don’t have Dry cough , fever.
Is he infected with covid 19 ?
Hi Aniket,
I’m not a medical doctor. However, I’d say that anyone of any age who is having difficulty breathing should see a doctor immediately! That would be true even before COVID19, but even more so now.
Please see a doctor and take care of yourself. Best wishes!
Thanks Jim ! I’ve been using the ‘new cases per day’ data for my curve, the Belgian data are still increasing day by day…
I’ll be adding graphs with new cases by day data with tonight’s update. Very informative!
Hi jim. Good one.
Based on this we can understand that the virus behaves different in different countries. So can we also say that there is a significant difference between the virus in every countries.
Italy and spain may be short of supplies but they are not so short for losing these many lives. I am saying the virus in ITALY is more infectious and dangerous than which has actually originated in china
Hi Krishna,
Thanks! Although, I think you might have missed a key point of my post. The virus in the Italy is the same as it is elsewhere. The mortality rate is being increased in places due to the selection bias I mention plus the shortage of resources. And, yes, Spain and Italy ARE that short on supplies.
Most of the deaths in India are those people who got infected aborad and travel to India…
All are old aged with diabetes and other health issues
Hi Vishal, The elderly and those with other underlying conditions do have a greater risk. However, younger people and those without underlying conditions still have a risk. It’s just not as high. That’s true everywhere in the world.
All countries, excepting China, had initial cases that started with travelers from abroad. That’s certainly how it started here in the U.S.! The problem is that it doesn’t stop there. It spreads very quickly.
Jim, nice article. You have mentioned a trend of falling mortality rate initially followed by a rise. In my view the reason could be the nature of advancement of disease symptoms.
The infected population starts to show symptoms after 1 to 3 weeks, most of these cases don’t come as positives even if they are tested. Thus the population which were infected in secondary or tertiary phase, which in terms of absolute numbers are exponentials of the primary infected group, starts hitting hospitals after one or two weeks of initial surge. As the infected patients’ absolute numbers increase exponentially and healthcare system becomes vulnerable by then, the death rate increases after this gap of time.
Also, in the secondary or tertiary phase probably more from older population or ones who are in immuno suppressed state with comorbid conditions acquire the infection, which also lead to higher fatality.
Thank you Jim for your statistical analysis. As a NJ physician in the middle of this pandemic, it made me feel more hopeful that somehow we will be able to handle this without too many lives lost 🙁
Hi Jolanta, thank you so much for serving on the front lines during these difficult times. I can’t imagine how difficult this must be for doctors and nurses!
Thank you for doing this, it’s fascinating. But could you please add the UK?
Thank you. Stay safe.
Hi Tracey,
I will add the UK soon in an update!
Hi Jim,
To your knowledge, any gaussian curve predictions possible with these limited set of data, I’ve seen marketing models and epidemiological prediction models about how and when to calculate the expected peaks and typical Gaussian distribution models, can you elaborate on this topic please ?
Hi Tom,
The peak of the curve refers to the rate of new cases. That’s an important issue because hospitals can only handle a certain rate of new case. If it exceeds that rate, they can’t treat all patients. So, that curve is different than the curves I show in this graph. Just so we’re all on the same page!
To assess the peak of the rate of new patients curve, you’d need to evaluate the number of new cases each day. Based on the rate of change, you might be able to project when it will reach its peak in cases where the rate of increase is already declining. For example, in Italy, they’re consistently having 6,000 new patients per day. That’s been steady for a week now. They’ve probably reached their peak.
However, in other cases where the rate of increase it increasing, they’ll need to use specialized epidemiological models. Those models can factor in other considerations and determine that even though the daily increase is itself increasing (such as it is in the U.S.), that there are other reasons to suspect that it will level out in several weeks. Unfortunately, I don’t have access to those models. But, they would do more than just look at the curve of new patients.
Much appreciate Jim. Keep it up
Hi Jim, thank you so much for setting this into the correct statistical context. Much appreciated! Kind regards, Julie
great post Jim,
yes, your are getting to actual morbidity rate by way of using the typical numerator and denominator for corona. but how accurate is the numerator (that being the number of deaths)? Is it possible that some of them are dying whereby it just hastened their death (i.e. they might have died within weeks or months anyway)? Should these be counted? I mean, perhaps a lot of older people, in general are dying, due in part to the flu, but that is never credited?
In light of that, I would like to propose another way to estimate, a way in which I have not seen or heard anybody mention (though I am sure those advocates are out there).
We should be able to estimate the number of COVID deaths by way of subtracting TOTAL deaths for some area by EXPECTED TOTAL deaths for that area. So, for example, assume that a small city in New York over the last 50 months of March has had x1, x2, …, x50 deaths.. A good mathematician can project for this year what should happen. He can allow for anything that might be relevant. A time trend factor might be used. Can do by per capita, etc. If one then projects 130, and there are 150, and we believe we have allowed for every other reasonable factor, then we can deduce that 20 died from COVID 19. Now, we might miss big on any given city. But if we do this for 100 cities, we should be pretty close, I surmise within 10% of the actual (though we do not know the actual).
Now, this misses on how many people are affected. But it might well be a better estimate on the impact as it relates to morbidity of COVID-19, and that is arguably more important in that it allows us to make more informed decisions regarding shutting businesses down, etc.
Hi John,
Those are all great questions. About the numerator, well the data we have work this way. If a patient tests positive for coronavirus, it increases the denominator by one. If that patient later dies, the numerator increases by one. I don’t know the exact details about how they determine that virus played a role. I presuming they’re not counting things like automobile accidents. But, I’m sure that if it’s coronavirus plus underlying conditions that collectively cause the death together, that IS counted. There might be some cases where they had another condition and would’ve died anyway, and they just happened to catch coronavirus and test positive for it right before that happened. I think that would be a small proportion of the cases though.
Right now we’re in the midst of the pandemic. I’m positive we don’t have the best data. They’ll have to unravel it and study it in more depth down the road. But, the data is good enough have a rough picture of what is happening.
I’ve heard of that approach you described (total deaths – expected deaths) for other issues. I’m drawing a blank on them at the moment. I’m thinking it might be air pollution or something along those lines. A wide-scale ecological issue. It wouldn’t be surprised at all if some study down the road does just that for coronavirus. Undoubtedly, that would have to occur after it’s all over and they can collect better data and fully understand the virus. You wouldn’t be able to do that with the “fuzzy” data we have now and scientists don’t really understand the full details about the virus itself. To develop a precise enough model to develop an estimate with a small enough margin of error takes time to perfect.
Thanks! Sort of what I thought but confirmed with data. Good read!
Great analysis and informative. Keep it up Jim
Good and thanks for the analysis jim. Can you please explain about the india mortality rate. Thanks in advance
Great analysis Jim 🙂 Thanks