Mid May Covidmeter findings:

1. Lock down does not prevent Covid-19 spread
2. China reporting standards are different than other countries. You can call it cheating if you like. It is just a tag, however Chinese propaganda will not like it
3. Covid-19 deaths toll scary in absolute numbers pales compared with population size
4. Both common cold and Covid-19 are here to stay, they will not take us all from here to eternity, just some individuals

World summary

Table below shows key numbers on Covid-19 development. ECDC data sets provide information on cases and deaths, so we can find some top scores. Metric come in some variations, they can be combined:

1. Suffix 1M – per 1 million inhabitants data.
2. Suffix 7d MA – 7 days moving average
3. Prefix population > – values calculated for countries above population threshold.

	date	country	value	population
max cumulative cases	2020-05-14	United States of America	1390746	327167434
max cumulative deaths	2020-05-14	United States of America	84133	327167434
max cumulative cases 1M	2020-05-14	San Marino	19032	33785
max cumulative deaths 1M	2020-04-27	San Marino	1213	33785
max daily deaths 1M 7d MA	2020-03-22	San Marino	63	33785
population > 100000, max cumulative cases 1M	2020-05-14	Qatar	9540	2781677
population > 100000, max cumulative deaths 1M	2020-05-14	Belgium	774	11422068
population > 100000, max daily deaths 1M 7d MA	2020-04-17	Belgium	29	11422068
population > 100000, max daily deaths 1M	2020-04-26	Ireland	48	4853506
worldldwide Covid1-19 cases	2020-05-14		4309508	7547997301
worldldwide Covid1-19 deaths	2020-05-14		298673	7547997301

1. USA leads in absolute numbers, it has big population but China one is even bigger and pandemic started there. China either quenched Covid-19 or implemented creative reporting, the former is easier.
2. In San Marino nobody died due to Covid-19 since 27th April and they report cases on daily basis. There is a good chance the virus penetrated entire population, killed 0.12%, This hardly qualifies as carnage.
3. Daily deaths maximum straight number (Ireland) and moving average (Belgium) were scored back in April
4. Absolute number of deaths is impressive but it represents around 1% of all deaths on Earth in Feb – mid May time frame. Human population is huge, we are mortal, 7.5 billion population produces in average over 200 thousands deaths per day.

Top countries

Figures below show top 30 countries selected by cumulative number of cases. The group represents 90% of total cases and 95% of total deaths.

1. Countries are sorted by cumulative cases per 1 million people
2. China (CN) is unique in its ability to stop Covid-19. No other country poor nor rich small nor big was able to repeat its success. China culture is not about social distancing. Is superb quarantine or rather creative reporting behind China success?

1. Countries are sorted by cumulative deaths per 1 million people
2. Again China is at the end. Normally country first hit by epidemic will suffer most, others can learn from its experience and prepare a better response. Maybe they are stubborn to learn and inept to implement solutions instantly developed in China?

1. Countries are sorted by cases mortality
2. Please note wide range of cases mortality rate, from 1% Russia to 20% France. This indicates wide difference in both death and case definition.

Covid-19 cases and deaths evolution

Figure below compare recent and reference values. We show Covid-19 cases and deaths. Countries are selected in the following way:

1. Dashed lines represent reference countries, we are looking for historical maximum in the following categories:
   1. cumulative cases (cc),
   2. cumulative cases per 1 million people (cc_1M),
   3. cases per 1 million people 7 days moving average (cma_1M)
2. Solid lines represent 3 countries with top recent cma_1M.
3. Country once selected is excluded from subsequent selections.

Rule for death comparison is similar to cases one.

1. Quatar reports 4 times more cases per million people than next country in this category, while its death toll is low (see earlier charts). We see historical maximum in cumulative cases per million and cases per million moving average now, so Quatar line is dashed. This can be explained either by superb testing coverage or long lag between infection (case) and death. Please note cases surge took place in April. Lock down measures did not help to prevent it.
2. Singapore (SG) was in lock down, observed social distancing, is geographically isolated, has strong somewhat authoritarian government. Yet it experienced Covid-19 outbreak in April. It seems no one but China can implement lock down properly.
3. Quatar, Bahrain and Kuwait are top 3 in recently detected cases. Persian Gulf is Covid-19 hot spot, again lock down does not prevent Covid-19 spread.

1. Deaths per 1 million people are dropping
2. In Italy we see a jump of daily deaths. Covid-19 takes long time to incubate and produce fatality, so it may be not directly related to lock down easing. Increased figure is still low comparing to historical values.

Cases mortality comparison

We calculate cases mortality dividing cumulative deaths by cumulative cases. High and low cases mortality countries are selected in the following way:

1. More than 20 cumulative deaths and 10000 cumulative cases
2. Sort by cases mortality rate
3. Take top 5 and bottom 5 from above list
4. Add Germany to result

1. Top mortality countries is stable
2. Mortality rate in UK drops, this may be due to increased testing capacity or Covid-19 true cases peak – everybody prone to virus already infected.

Chart below shows countries with lowest mortality rate.

1. Russia has very high number of cases, above 110 thousands, this is order of magnitude higher than other countries on chart. Interesting is mortality rate for Russia stays constant. For other countries it went up once number of cases soared.
2. SIngapore has over 20 thousands cases and very low mortality rate, total deaths as of 14.05 are 21.

Cumulative deaths per 1 million people

1. Leaders group is constant
2. Italy shows increase, too early to declare it a trend

Covidmeter

Covidmeter compares cumulative deaths per million people for top countries with reference. Reference countries are:

1. Diamond Princess cruise ship
2. San Marino – country, enclave in Italy

DP development is closed, crew and passengers disembarked the ship. San Marino is a live group, the country has tiny population, over 30 thousands, but it was hard hit my Covid-19.

1. No country came close to Diamond Princess death toll declared limit at the beginning of April
2. San Marino still reports Covid-19 cases on daily basis, last death was reported 27th April. Neither Covid-19 nor common cold will be fully eliminated there.

Collateral damage comes from military, it means unintended death or injury resulting from military operations. There is a good chance Covid19 pandemic fight results in deaths of people not infected with the virus itself, so it has collateral damage.

Inflated death count

Belgium reported recently a substantial number of Covid19 deaths, it made it the world leader in mortality per million of people. There were comments in local media suggesting all deaths in nursery houses for old people were qualified as Covid19 related, without confirming actual virus presence in deceased. Indeed on official page info-coronavirus.be you can find information like the one above dated 17.04:

Of the 5 163 people who died, 44% died in hospital, 54% in a rest and care home, 0.6% at home and 0.2% in another location. The deaths in hospital are all confirmed cases. Deaths in rest homes are either confirmed cases (7.8%) or suspected cases (92%)

Source

Population mortality impact

Since half of deaths contributed to Covid19 are just suspected, there is clearly a bias to attribute all deaths to the virus, inflate its toll. On the same site you can find information on total mortality in Belgium, chart copied from the article is displayed below.

There is a clearly visible surge of daily deaths, from mid March to mid April, we can read the following numbers:

• Total deaths 300 to 600 (solid orange)
• Covid19 confirmed 0 to 100 (dashed green)
• Covid19 confirmed and suspected 0 to 300 (solid green)
• Covid19 suspected 0 to 200 – difference between Covid19 total and suspected – area between solid and dashed green

Covid19 collateral damage – suspected deaths

Covid19 suspected deaths are those from care (nursery) homes, who passed away and had no positive Covid19 test. It is not clear if medical authorities plan to confirm virus presence in post-mortem. We can presume suspected and confirmed deaths alike are reported as Covid19 deaths. This is a flaw, suspected deaths should be verified, there may not result from the virus directly. They may be just collateral damage resulting from:

1. Elevated stress level. Media stories about Covid19 mortality, especially among older people clearly make people anxious and raise stress level. Extraordinary lock down measures taken by government don’t make people tranquil. Permanent stress is hazardous, it deteriorates health and may result in premature death.
2. Medical resources focused on epidemic response reduce care level for patients with chronic disease. This does not result from bad faith, just shortage of resources and allocation inefficiencies.

Death from virus and collateral damage separation

In order to understand epidemic impact It is important to report death causes correctly. We have a combination of virus epidemic and fear epidemic. Inflating deaths related to the former does not make curbing the latter easier. Belgium decision to report suspected cases had big impact on reported numbers:

Of the 7 094 people who died, 45% died in hospital, 53% in a rest and care home, 0% at home and 0% elsewhere. The hospital deaths are all confirmed cases. The deaths in rest homes are both confirmed (10%) and suspected (90%) cases.

Source 26.04 report

7094 * 0.53% * 0.9 = 3384, almost 48% of total deaths are suspected ones. This introduces a huge margin of error to collected data. Some suspected cases are direct Civid19 victims, others are collateral damage, proportion remains unknown.

Medical resources allocation issues examples

• Hungary decided to empty hospitals. In early April the Hungary government ordered hospitals to ensure that over 30,000 are available for Covid19 patients, the number was then increased to 40000. Hungary has recorded 250 deaths from the Covid19 as of 24.04, and has 2,383 known cases. This had an impact on existing patients who were forced home.
• Italian province Lombardy hard hit my Covid19 had many hospital beds available, but was short on family doctors and general practitioners. Patients who should have been recovering at home were taken to hospital, putting undue stress on capacity. Doctors making house visits were not adequately protected. Some died and others may have inadvertently spread the virus. Details here.

Final advice

Stay calm and breath normally. Excessive fear of Covid19 can kill you faster than the virus. Virus can sneak in, decision to join collateral damage ranks is on you.

I generally consider Reuters a valuable source of information. However a combination of 2 recent articles about Covid19 in China reveals Reuters interpretation of data is somehow naive. Data analysis led me to some China conspiracy hypothesis outlined at the end of this post, but let us see source data first:

April 23 Three negatives and a positive: problems with coronavirus tests in China

• Trader He Ximing from Wuhan caught the coronavirus
• X-rays showed his lungs had white blotches, similar to those found in coronavirus patients, but his nucleic acid test was not positive so a hospital declined to admit him.
• As a precaution, a committee that manages his housing compound put him in quarantine for 14 days.
• on March 28, he took a fourth nucleic acid test, which was again negative, but he was also tested for antibodies and got confirmation
• A survey by Chinese doctors in February looking at samples from 213 patients suggested a false-negative rate of about 30%.

Antibodies presence typically means one was infected and build resilience to the virus. Infection may be symptoms free. Article focuses on individual calamity of trader, but ignores meaning of numbers.

April 22: Recovered, almost: China’s early patients unable to shed coronavirus

• As of April 21, 93% of 82,788 people with the virus in China had recovered and been discharged, official figures show.
• Some patients are confined to test centers for at least 28 days and obtain two negative results before being allowed to leave.

Data analysis

Let us combine information from 2 articles listed above. If Covid19 test has false negative rate of 30% it means probability of passing 2 consecutive tests is:

0.3 * 0.3 = 0.09 or 9%

Since 93% of 82,788 infected people had been discharged we can estimate number of false negatives among them:

0.93 * 82788 * 0.09 = 6929

In plain words if Reuters numbers are true there have been close to 7 thousands people infected with Covid19 and showing no symptoms, released to the society. This should have caught editors’ attention if they still bother to employ one. Simple coin toss will produce 50% false negatives, so 30% is a huge number. In order to go below 1% false negatives, you need to run 4 consecutive tests if a single test false negative rate is 30%. Even 1% false negatives will result in around 700 infected patients release.

Now if Chinese authorities claim they have save general public from Covid19 by applying strict quarantine measures and isolating all cases, above numbers make the claim suspicious. Release of 7000 contagion sources to population without immunity would result in immediate surge of new cases. Let’s have a look at China cases reported since March:

We see daily cases trend toward 0 indicating no surge of new cases whatsoever. This can be explained in the following way:

1. General population build resilience to Covid19. This may not be the case at whole country level yet, but places which went over epidemic, like Wuhan, now have immune population. False negatives can be released with no substantial risk.
2. China understood from statistical data on Wuhan outbreak, Covid19 is initially deadly to a small fraction of population. Since vast majority of cases are either symptom free or mild population immunity builds quickly. Thus Covid19 is contagious, dangerous for individuals but innocuous for overall population.
3. Social distancing and quarantine help to manage outbreak, but are not essential to curb epidemic.

Assuming above is true why is China not willing to share the knowledge? Perhaps they see it as an opportunity to build economical advantage over other countries. EU and US economies under lock down are taking a heavy toll. They will likely slump into depression if Covid19 measures are not lifted fast. Why EU does not care to verify if Chinese scenario outlined above is true? We have plenty of data at our disposal to do it.

Charts and tables updated with 23.04 data. Please go here for missing definitions. Key findings:

• Belgium probably over reported Covid19 deaths
• New cases at Singapore are surging, mortality is still low
• No unified definition of Covid19 inflicted death obstructs country data comparison
• Country lock down does not compensate high population density

Deaths per 1 million population

• Calculate daily deaths per 1 million people (deaths_1M) defined as:
  (deaths in country on given day) / (country population size) * (1 million)
• Take all countries from ECDC data set with more than 10 cumulative Covid-19 deaths and more than 100 detected cases, sort descending by deaths_1M
• Take top 10 countries from above list and plot results

Isle of Man joined top group, it has population of 80 thousands, 15 deaths

No changes in above one million population countries group

Cases per 1 million population

Ranking created like the one for deaths:

• Calculate daily cases per 1 million people (cases_1M) defined as:
  (new cases in country on given day) / (country population size) * (1 million)
• Take all countries from ECDC data set with more than 10 cumulative Covid-19 deaths and more than 100 detected cases, sort descending by cases_1M
• Take top 10 countries from above list and plot results

Please note Luxembourg is 3rd in all countries ranking, it has around 600 thousands inhabitants. It seems to be well after infections peak.

Singapore cases are growing rapidly, death count is still tiny. We’ll keep an eye on it.

High and low cases mortality countries

• More than 20 cumulative deaths and 10000 cumulative cases
• Sort by cases mortality rate
• Take top 5 and bottom 5 from above list
• Add Germany to result

Sweden replaced Netherlands in top 5. Saudi Arabia joined bottom 5 (exceeded 20 deaths threshold)

As of 2020-04-23 there were 29 countries meeting 100 cumulative deaths and 10000 cumulative cases threshold. They are listed in table below sorted by cases mortality rate top down. Poland is among new countries in the group.

Country	pop2018	CumCases	CumDeaths	CumDeaths_1M	CasesMortality
France	66987244	119151	21340	318.6	17.91
Belgium	11422068	41889	6262	548.2	14.95
United Kingdom	66488991	133495	18100	272.2	13.56
Italy	60431283	187327	25085	415.1	13.39
Sweden	10183175	16004	1937	190.2	12.10
Netherlands	17231017	34842	4054	235.3	11.64
Spain	46723749	208389	21717	464.8	10.42
Mexico	126190788	10544	970	7.7	9.20
Iran, Islamic Republic of	81800269	85996	5391	65.9	6.27
Brazil	209469333	45757	2906	13.9	6.35
United States of America	327167434	842629	46784	143.0	5.55
China	1392730000	83876	4636	3.3	5.53
Ecuador	17084357	10850	537	31.4	4.95
Canada	37058856	40179	1974	53.3	4.91
Ireland	4853506	16671	769	158.4	4.61
Switzerland	8516543	28186	1216	142.8	4.31
Poland	37978548	10169	426	11.2	4.19
Portugal	10281762	21982	785	76.3	3.57
Germany	82927922	148046	5094	61.4	3.44
India	1352617328	21393	681	0.5	3.18
Austria	8847037	14924	494	55.8	3.31
Peru	31989256	19250	530	16.6	2.75
Japan	126529100	11772	287	2.3	2.44
Turkey	82319724	98674	2376	28.9	2.41
Korea, Republic of	51635256	10702	240	4.6	2.24
Chile	18729160	11296	160	8.5	1.42
Israel	8883800	14592	191	21.5	1.31
Saudi Arabia	33699947	12772	114	3.4	0.89
Russian Federation	144478050	57999	513	3.6	0.88

Country insight

Change in layout of this section we compare countries selected after lock down measures severity. Numbers on list below reflect country ranking

1. Belgium – moderate: business shutdown, sport activities outside allowed. Population density 376/km2
2. France – severe: business shutdown, stay at home order so no sports outside. Population density 104/km2
3. Sweden – relaxed: Keep social distance but some public places remain open. Population density 23/km2

It seems Covid19 impact grows with population density. In my opinion lock down measures efficiency is limited in densely populated areas.

Daily deaths

Belgium ranking can be overstated. There were comments Belgium reported all deaths in nursery houses for eledery people as Covid19 caused without actual virus presence test. This practice has been probably abolish recently. We’ll try to investigate it further. Please note there is no unified definition of Covid19 victim, so deaths reported in countries may adopt different criteria. Unfortunately there is no information on total daily deaths in ECDC data set.

Daily cases

I do hope France under reports cases, otherwise 18% cases mortality looks scary.

Cumulative cases and deaths

Newcomer – Poland

Poland matches standard pattern: Low testing capacity focuses on more severe cases with higher death probability. In result cases mortality goes up.

Population mortality

• Calculate cumulative deaths per 1 million people (cumdeaths_1M) defined as:
  (total deaths in country till given date) / (country population size) * 1 million
• Take countries with population above 1 million
• Sort by cumdeaths_1M descending
• Select top 5 from above list

Belgium qualified all deceased in nursery houses as Covid19 victims without testing for actual virus presence. The line should be revised down probably.

Covidmeter

San Marino reference line uptick due to additional death. Please remember population is tiny, every deaths is visible. Lack of common definition of Covid19 fatality can easily obscure this ranking.

Growing Covid-19 pandemic hit many countries, its severity is typically measured by number of detected cases and death toll. Since countries differ in population size, we have to take it into account while estimating true impact. Original Covidmeter used total (cumulative) deaths per 1000 people to estimate population impact. We can calculate similar ratio for daily deaths to build a faster indicator. Since numerator is lower we calculate daily deaths per million people. Let’s see what upgraded Covidmeter shows.

Continue reading →

Drawings and tables from 04-06 post updated with 04-08 data. For explanation please go to the 04-05 post.

Continue reading →

Drawings and tables from 04-05 post updated with 04-06 data. For explanation please go to the original. Since France cases mortality rate was going up rapidly we’ve made it country in focus.

Continue reading →

Covid-19 is taking a death toll, but is it significant from population perspective? We crunch some ECDC data released 5th of April, to measure how severe is Covid-19 impact so far on human population. We use this opportunity to refine Covidmeter concept introduced in previous post.

Continue reading →

Death is definitive. We are in Lent period, approaching Passion Week, resurrection promise is important for some of you. However for Covid-19 impact measurement purposes death is definitive enough. Number of infection cases can be under estimated, especially if there is no proper test coverage of entire population. Cases not producing symptoms or producing light/moderate symptoms are likely to stay under radar. Death cases are investigated thoroughly, every Covid-19 infection is likely to be discovered in post-mortem. We can use population mortality rate as a measure of Covid-19 epidemic development. We will name it Covidmeter for brevity. Let us present how it links with data.

Continue reading →

Let’s have a closer look at Covid-19 mortality rate. We focus on mortality among detected cases, since population fraction affected is still very tiny. Mortality rate defined as quotient of cumulative death cases and cumulative detected cases can vary between 0.3% and 11%, a factor of 30 difference. Quite a significant difference indeed.

Data in table below below come from 2020-03-30 ECDC set, columns are explained below the table. As predicted in my previous post USA took the top spot in number of Covid-19 cases, China is now number 3, Spain will likely displace it on March 31st. Please note China started to report new cases albeit number is low (121).

	DateRep	CountrY	Cases	Deaths	CCases	CDeaths	Mort%	pop2018
0	2020-03-30	United States of America	18360	318	143025	2509	1.75	327167434
1	2020-03-30	Italy	5217	758	97689	10781	11.04	60431283
2	2020-03-30	China	121	5	82463	3311	4.02	1392730000
3	2020-03-30	Spain	6549	838	78797	6528	8.28	46723749
4	2020-03-30	Germany	4751	66	57298	455	0.79	82927922
5	2020-03-30	France	2599	292	40174	2606	6.49	66987244
6	2020-03-30	Iran	2901	123	38309	2640	6.89	81800269
7	2020-03-30	United Kingdom	2433	209	19522	1228	6.29	66488991
8	2020-03-30	Switzerland	1122	22	14274	257	1.80	8516543
9	2020-03-30	Netherlands	1104	132	10866	771	7.10	17231017
10	2020-03-30	Belgium	1702	78	10836	431	3.98	11422068
11	2020-03-30	South Korea	78	6	9661	158	1.64	51635256
12	2020-03-30	Turkey	1815	23	9217	131	1.42	82319724
13	2020-03-30	Austria	522	18	8813	86	0.98	8847037
14	2020-03-30	Canada	869	1	6255	61	0.98	37058856
15	2020-03-30	Portugal	792	19	5962	119	2.00	10281762
16	2020-03-30	Brazil	352	22	4256	136	3.20	209469333
17	2020-03-30	Israel	628	3	4247	15	0.35	8883800
18	2020-03-30	Norway	257	2	4102	22	0.54	5314336
19	2020-03-30	Australia	284	2	4093	16	0.39	24992369

Table columns are explained below

• CASES, DEATHS – cases and deaths at reporting date (column DATEREP)
• CCASES, CDEATHS – cumulative cases and deaths
• MORT% – cumulative cases mortality rate CCCASES/CDEATHS in percentage points
• POP2018 – country population in 2018

Table below shows how top20 countries compare with entire world.

	top 20	world	top %
Cumulative cases	649859	715669	90.8
Cumulative Deaths	32261	33574	96.1
population 2018	2601228993	7459101652	34.9
count	20	196	10.2

As we see top 20 countries cover 91% of Covid-19 cases detected worldwide and account for 96% deaths. Similar comparison for top 5 countries below:

	top	world	top %
CumCases	459272	715669	64.2
CumDeaths	23584	33574	70.2
pop2018	1909980388	7459101652	25.6
count	5	196	2.6

From table above we see Top 5 countries: had 459272 cases 23584 deaths in total, for population of 1909980388, close to 2 billion (US, 10E9). Of course China contributes more than half of the latter number. Let’s calculate some rations for top 5 countries:

• cases/population 0.024%
• deaths/population 0.0012%
• deaths/cases 5.14%

First two numbers are distorted by China huge population count. Covid-19 outbreak in China is officially declared contained, other countries are in full swing. Last number is both concerning and interesting because there are countries with high and low mortality rates. Lets have a look at them. From top 20 countries we have selected 5 with lowest and 5 with highest mortality rate. Data are presented below sorted on cases mortality in highest to lowest order.

Low and high cases mortality countries

Table below show low case mortality countries selected form top 20 table:

	DateRep	Countries	Cases	Deaths	CCases	CDeaths	mort%
14	2020-03-30	Canada	869	1	6255	61	0.98
4	2020-03-30	Germany	4751	66	57298	455	0.79
18	2020-03-30	Norway	257	2	4102	22	0.54
19	2020-03-30	Australia	284	2	4093	16	0.39
17	2020-03-30	Israel	628	3	4247	15	0.35

High mortality countries follow

	DateRep	Countries	Cases	Deaths	cCases	CDeaths	Mort%
1	2020-03-30	Italy	5217	758	97689	10781	11.04
3	2020-03-30	Spain	6549	838	78797	6528	8.28
9	2020-03-30	Netherlands	1104	132	10866	771	7.10
6	2020-03-30	Iran	2901	123	38309	2640	6.89
5	2020-03-30	France	2599	292	40174	2606	6.49

As we can see there is a wide difference in case mortality rate, it range s form 11.04% for Italy to 0.35% for Israel. You can argue Israel is in early development, but Germany has mortality rate at 0.79% and it has 57 thousands cases which is comparable with mortality record beating Italy 97 thousands cases. Italy and Germany are very similar countries in terms of size, population, wealth, economic development. I see no other explanation for order of magnitude mortality rate difference other than Covid-19 testing approach, discussed in previous post. Let’s have a look at mortality rate evolution over time.

Cases mortality rate evolution

We show picture for low cases mortality countries first. Data are shown as soon as cumulative deaths exceed 5. This eliminates initial fluctuations.

Please note Germany started with mortality rate around 0.2% of detected cases and its stayed at that level quite long. I believe the rate started to climb once growing number of cases made test diverted from general application to heavier cases. This is positive scenario.

For high mortality rate countries we show data once cumulative death cases exceed 20. Again this eliminate early fluctuations. Iran was an extreme example here, initial number of deaths equaled number of cases. Iran example shows how testing influences detected cases mortality rate.

One more comment on Iran curve. Is started very high, dropped to 2% range, then climbed back in a almost Italian fashion (favorite explanation lack of test of course). Recently we see Iran detected case mortality heading down. Iran rate is going better than for other high mortality countries. Does a poor Iran has a more reasonable approach to disease handling than wealthy European countries? They may have less means but doe to recent turbulent history has much more experience in handling emergency situations, thus better allocation of its limited resources.

Figure below shows low and high rate countries combined. Some low rate countries don’t qualify since 20 cumulative deaths threshold is applied.

It extremely important to understand cases mortality difference between Germany and high rate countries. It would allow us to estimate actual Covid-19 impact on population.

Final remarks

I write a lot about importance of broad population testing. I believe difference in cases mortality rate comes from distortion of cases detection process in high mortality countries. Tests are scarce and used on heavier cases, more likely to end as fatalities, thus mortality rate goes up. I try to get a reliable data on number of tests performed in countries, but no success so far. I have contacted ECDC (European Centre for Disease Prevention and Control) my primary source, but thy have no such data data available. I see no other way to estimate true number of Covid-19 cases in population than a test on a random sample coming from actual population distribution. This process has to be separate from testing patients showing clear Covid-19 symptoms. If anyone knows where to find a reliable data on number of tests performed in countries I’d appreciate if you share it.