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.

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

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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.

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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.

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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.

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