Dec 6, 2020

Explaining One Year with Sars-Cov-2 and Covid-19 – Deciphering Distorted Data – Providing Actual Infection Rates and Final Deaths – Outlining the Epidemic End – Warning Against the RNA Vaccine

The text responds to the following questions
* What part of the population has really been infected by Covid-19?
* How quickly does Covid-19 spread in the population?
* How does the 2nd wave differ from the 1st one?
* How does the age affect the probability of death from Covid-19?
* What is the real lethality of Covid-19?
* What will be the final number of deaths?
* Do the protective measures help?
* How do the countries differ from each other?
* Which of 200 countries does handle Covid-19 optimally and which reasonably?
* How should the epidemic have been handled?
* When will the epidemic end?
* Is the vaccination needed?
For short responses go to the end of this text.

The best source of updated data about the Cov-2 pandemic in all countries is Worldometers (here), and some data can be found in the WHO site here (here). I provided a half-year Corona Summary updated till the end of July in CorSum, reviewing the known facts and data about the virus and the disease, about the pandemic origin, its spreading around the world and handling in various countries. A short extract of CorSum is here, and also at the end of this text.

The current text includes updates till the end of November (see Table 5), but importantly it tries to decipher the real state of the current pandemic from the flood of contradictory information and from the available data fragments. The newest findings, showing high infected population fractions and good acquired immunity, signalize the oncoming end of the world’s predicament – however, the end might be messed up by the political and healthcare establishment, which is incompetent, corrupt, and motivated to go for broad, even though not duly tested, vaccination. 

Assessing the extent of infection
The number of reported cases somehow reflects the number of infected people, but it is not clear how. Unfortunately, most politicians and other responsible officials do not realize that the number of positively diagnosed people and the number of infected people are two different things. It would seem obvious that it is necessary, for planning further actions and for managing the epidemic, to know what fraction of the population is infected. The infected population fraction in percent (IPF) may be obtained by testing randomly selected representative population samples. Unfortunately and incomprehensibly, no well controlled testing of randomly selected groups of population has so far been repeatedly performed in any one of 200 countries of the world.

Serologic testing (detecting antibodies against Cov-2 virus proteins in blood) may reveal the people who have been infected in the past. However, the serologic testing actions usually included non-representative samples, they did not employ well defined testing methods, and did not provide clear results. An example for all: a broad serologic testing in Czechia, organized by the government, showed 0,4% of positive cases by the end of April, but a parallel private testing with more sensitive serologic tests showed 5% (here).

The actually active cases can be revealed by PCR testing (viral RNA from nasal swab is converted to DNA, which is amplified by the PCR method and detected) or by quicker but less reliable antibody-based tests (an antibody against Cov-2 proteins provides a color signal when contacting the virus or its proteins in nasal swab). The total number of so-far-infected people from PCR-based or antibody-based tests might be deduced as follows. For example, Slovakia has recently tested a great part of the population by quick antibody-based tests (here) and found about 1% population to be positive. No official conclusions have been outlined, but the following might be hypothesized: a person is infectious 10 days, so the testing revealed only one 10-days portion, one month contains 3 such portions, there have been 30 such portions from the beginning of 2020 till the end of October; in the first January portion, the positive ratio was 0 % and in the last October portion it was 1%; if simply assuming a linear increase, the mean ratio during the whole time was 0.5% (exponential increase would provide less than 0.5% for the mean value), and 30 portions would provide (30 * 0.5% =) 15% infected in total. This deduction thus provides an estimate of at most 15% IPF in Slovakia in the end of October, but with many caveats; firstly, if the quick test has for example 99.5% specificity (i.e., 0.5% results are false positives), then 0.5% of the positively identified people would in fact be never infected, which means that one half of said 1% population identified as having been infected would be identified falsely. The quality of the test kits is critical.

Extrapolations based on growing numbers of positively tested people may be misleading too, as the number of reported cases increases both due to the increased number of infected people and due to the increased number of tests; for example, the increase of cases from 900 to 1800 per million in Israel between April 7 and May 12 (see Table 5) need not mean that the number of all infected people doubled, because the number of performed tests increased four fold in said period.

The above considerations suggest that merely crude assessments of the infected population fractions can be obtained.

The ratio of infected people to positively tested people – infected population fraction (IPF %)
As explained in CorSum, the number of people having been infected by Cov-2 has been 2 to 100 times higher than the number of reported positively tested cases; the ratio is called quotient M here (see for example Table 5). For example, M was 100 in India in May (here). Nature suggested a more than 50-fold increase in coronavirus infections compared to officially admitted cases in California in April (here). An October review of 17 countries shows a still broader range for M (here). Various models provided M between 2 and 40 for the U.S. in June to August, such as 4-20  (here, here, here). M is often around 10 (here, here, here, here).

My July Summary (CorSum) estimated a probable range of IPF values for the countries of Table 5 as follows (for comparison, a June estimation of New Scientist is in parenthesis in red): 3-8% in Singapore, 15-30% in U.S. (12), 25-50% in Sweden (7.5), 5-8% in Spain (6), 5-10% in United Kingdom (6), 10-30% in Italy (18), 5-8% in France (18), 5-15% in Germany (6), 6-10% in Israel (2.5), 2-4% in Czechia, 0.5-1% in Australia, 0.5-2% in Korea, 0.5-3% in Japan (4), and 0.05-0.2% in China (5).

If applying a crude rule that the number of really infected is ten fold higher than the number of reported cases (quotient M = 10), a first assessment of the IPF value toward the end of October can be obtained for the countries of Table 5 as shown in the 2nd column of Table 6 below (the reported cases per million people, blue values in Table 5, are divided by 10,000 to get the number of positive cases per 100 people, and this fraction is multiplied by 10).

A second assessment for the October IPF value, the 3rd column of Table 6, is obtained from the lower limit of my July IPF range by increasing it proportionally to the growth of the reported cases, i.e. the lower limit is multiplied by the ratio of reported cases for the end of October and the end of July (the ratio is obtained from Table 5; for Sweden, for example: 1.42 * 25% = 36%). As no July range is available for Switzerland, the second assessments is replaced by a published May collective immunity value of 9% (here) adjusted to the end of October (the ratio of October to May reported cases from Table 5 is 4.0 for Switzerland). The average IPF value of the two assessments is in the 4th column. Said assessments try to get hands on the infected fraction which is so difficult to obtain. For comparison, 5th column shows estimations of the population fractions exhibiting positive serologic reactions (reflecting IPF values) as published on October 23 (Rostami et al., here).
The values from the Rostami’s publication support the values as here deduced, except for East-Asian countries. Generally, the infection rates for Australia and Eastern Asia are substantially lower than in other parts of the world. As for China, all values published by the Chinese government, as shown in Table 5, are obviously improbable and totally unreliable, so that also any derived values are unreliable. 

Regarding Europe, Israel and America, between 10% and 40% of their populations may have been infected by the end of October, and between 15% and 50% by the end of November (see Table 8 below).

Wave progression of Cov-2 in the countries
The alternation of stricter and softer measures results in changes of R0, and therefore the time curve of total reported cases has proceeded in distinct steps, so far mostly in two or three; correspondingly, the time curve of new daily cases (the derivative of the total cases curve) shows two, or somewhere three, separate peaks. The numbers of total deaths and new daily deaths follow the same pattern as the number of total positive cases and new daily deaths, respectively (see Worldometers here under individual countries).

Data describing the waves in 14 countries are presented In Table 7. From the curves in the Worldometers, I have read the approximate date when the 2nd wave, or eventually the 3rd wave, started; further, I have read the number of total cases and the number of total deaths for said date, and also for November 19 (the last date of this examination); the values for the first date correspond to the 1st wave; the difference between the values for the second date and the first date corresponds to the 2nd wave; the difference between the values for November 19 and the second date corresponds to the 3rd wave). The total cases and deaths are thus divided to two or three sub-quantities representing two or three “waves”. For example, by November 19, the U.S. reported about 12.1 million cases and 260 thousands deaths, of which 2.1 m and 119 k (m stands for million and k for thousand), respectively, can be ascribed to the first wave; 4.6 m and the 79 k, respectively, can be ascribed to the second wave (which started approximately on June 11); and 5.4 m and 60 k, respectively, can be ascribed to the third wave (which started approximately on September 11); the values have been recalculated per one million people to be better comparable (i.e. divided by 331 for the U.S.), and rounded (providing 6340, 360, etc., see Table 7). The "ratio%" is the number of dead per 100 cases.

Comparing the 1st wave with the following waves shows that the virus continues to spread in all above countries. The same holds for Australia and Eastern Asia, where they supposedly managed to contain the disease; it can be predicted that these countries may have delayed the disease, but they will go through the same course as the Western countries, if they do not start to vaccinate.
U.S., Europe, and Israel show many-fold increase of the cases in the succeeding waves compared to the 1st one (for example 3 fold in Germany and 40 fold in Czechia). Good news is that the mortality in succeeding waves decreases in most of these countries, and the best news is that the following waves show much lower apparent lethality (=fatality rate, see Ratio % in Table 7) than the first wave. It is also probable that the number of cases was somewhat underestimated in the first wave more than in the succeeding waves (the ratio of really infected to positively tested, M, decreased during time, see here); the apparent lethality in the second and third waves is about 1 (see Ratio % in Table 7), providing real lethality of about 0.1, if taking 10 for M

The most exceptional among the countries is Sweden, which shows the sharpest decrease of the deaths in the later waves; this same country has taken most liberal attitude toward the coronavirus epidemics, being the only country that has not closed its businesses, schools, and its borders, and that continued to live quite normally without unscientific measures and hysteric proclamations. For comparison, marked in the table are results for Sweden, and for Czech republic which has taken nonsensical measures like most other countries (both countries have about 10 million population).

Existence of different virus strains could theoretically also explain the differences in different epidemic waves. Sequencing of the virus genomes from these waves would help. 

Covid-19 propagation through the population
A simple model of an epidemic spread holds that the speed of the propagation (i.e., the number of newly infected cases per time unit) is proportional to the total number N of infected people and to the fraction of population S still susceptible to being infected:

dN/dt = c * N * S

If Nmax is the maximal number of people that can be infected in the population in the end (Nmax is maximally the whole population size, but theoretically can be lower, if some people are immune or excluded from population mixing), then said fraction S is 1 – N/Nmax , and

dN/dt = c * N * (1 – N/Nmax)

The constant c is advantageously written as lnR0 (natural logarithm of R0) wherein R0 has the meaning of the basic reproduction number, namely the number of the new infections created by one infected person during a time unit in the beginning of the epidemics:

dN/dt = ln R0 * N * (1 – N/Nmax)

R0 for Covid-19 is usually estimated to range between 2 and 5 per week. By two independent ways I have estimated the value of R0 at 3.3 per week (see here, “Person to person spreading rate”). Any infection-preventing measures reduce the value of R0, for example to 1.5.

As an example, the following curve shows % fraction of the infected people (100 * N/Nmax) in a population of 8,000,000 susceptible people in accordance with the above equation, when R0 is 2; the time unit is week. The infection propagation curve has the typical sigmoid shape of various systems comprising growth. The curve will be the same, whether this eight million “susceptible” people will be a part of a population of eight, ten or one hundred million people.             

             Fig. 1 Covid-19 propagation curve for R0 = 2 and Nmax 8,000,000.   

If the epidemic starts with one infected person, and if each person infects within a week two other people in an eight million susceptible population which freely mixes, the curve shows that it takes about 14 weeks before a visible population fraction, such as 0.5%, becomes infected; this “covert phase” is shown as a blue arrow in the following graph. The covert phase for R = 3 would be shorter by 1 month; if the epidemics started not with one ill but with 100 initially infected people, the covert phase would by also shorter by 1 month. It can be seen that the exponential character of the growth passes to nearly linear one at a population fraction of about 10% and stays such till a fraction of about 90%, as demonstrated by the red straight line drawn onto the curve (provided with an arrow).

Fig. 2 Covid-19 propagation curve of Fig. 1 with marked “covert phase” (blue) and the “linear phase” (red).          

If a country of 10 million inhabitants has 8 million people who can be infected, an epidemics starting on January 1 would be “perceptible” in April, if R0 = 2, and the number of new cases would linearly grow between June and July, providing (80% * 8 million/6 weeks= 152,380) about 150 thousand new cases every day; which would be 15,000 a day if 1/10 cases are reported. In September, the epidemics would be ended.

If the whole population is susceptible to the infection (Nmax equals the population size), then the infected population fraction has the same meaning as IPF in Table 6 above; IPF equals 100 * N/Nmax, and both Nmax and the population size can be excluded:

dIPF/dt = lnR0 * IPF * (1 – IPF/100)

However, if a part of the population cannot be infected, or if the epidemic cannot pass through the whole population, for example due to the existence of a threshold of herd immunity, as discussed below in the paragraph “Herd immunity”, then the “IPF” may mean the fraction of the susceptible or infectable population.

Spreading curves for two different values of R0 and for three different population sizes are shown in Fig.3. It can be seen that all curves for certain R0 have the same shape for all population sizes, and are merely shifted to the right for greater populations (a 10-fold population increase will shift the curve by 2 weeks to the right for R0 = 3, and by 6 weeks for R0 = 1.5). Further, it can be seen that the slope of the “linear part” (approximately the slope in the inflex point) does not depend on the population size (as evidenced by the above dIPF/dt equation); the slope corresponds to the increase of the infected fraction from 10% to 90% achieved within about 4 weeks for R0= 3, within 10 weeks for R0 = 1.5, and within a year for R0 = 1.1, as read from the curves (being in accordance with the expected values from the above relation for IPF = 50%, which provides the slope in the inflex point of 25*lnR0).

An interesting and practical finding is that the initial exponential growth comprises only the first 10% of the involved population, and that it is followed by a nearly linear growth, of which infection speed is 20% population per week for R0 = 3, or 8% population per week for R0 = 1.5, or 2.5% population per week for R0 = 1.15. The linear phase continues till a change of the conditions or till achieving 90% infection. The “involved population” are all susceptible people or the people comprised in one wave.

The real progression of the Covid-19 epidemic in certain areas or in certain groups of people might be approximated/ modelled/ predicted by a combination of the above curves constructed for suitable values of R0, group sizes, and the initial number of infected people, provided that at least some points of the real progression are known, such as the number of infected people in the country at a certain time – however, real values are mostly missing for all times and for all countries (let us not forget that the number of reported cases and the number of infected people are totally different).

Sweden is taken below as an example for reconstructing a progression curve. The number of total cases can be obtained for every date in Worldometers (here), but the reported cases have no clear relation to the real number of totally infected by certain date; the last November case number is 243,129, which corresponds to 2.4% of the Swedish population, but the serologic testing showed 6-10% in April. By combining all available information, and by employing model progression curves like those in Fig. 2 and 3, a crude reconstruction of a Covid-19 propagation curve has been suggested for Sweden in Fig. 4 – see the sequence of red arrows representing linear approximations of distinct phases during the developments. The arrows outline a roughly two-wave progression from January till the supposed predicted end in spring 2021. Although the first cases appeared already in December 2019 in Sweden, the time axis of the graph starts on January 1, 2020; a “covert phase” of 10 weeks was taken from Fig. 3 for R0 = 3 and Nmax = 10,000,000 (R0 of 3.3 would make the phase shorter by two weeks, 50 initially infected instead of 1 would shorten the phase by 3 more weeks). After about 10 weeks (on March 3, 16, and 27), protecting measures were announced in Sweden, including restrictions in social contacts, which lowered the initial reproduction number of 3; this is corroborated by the lower speed of case growth as seen in the mentioned Worldometers curve of the total cases (and in Table 5). Serologic tests showed about 6% infection among the population in early April and roughly 10% around the end of April (here). The values of R0 of 1.15 and 1.05 fitted the initial slow case increase, the reduction in July probably reflecting the children summer holiday. More relaxed atmosphere (including softening some restrictions, returning children to schools) increased the case growth in the autumn. The “second wave” of October is best fitted by a curve for R0 = 1.4 and a population of 6.5 million (the supposedly remaining fraction of uninfected Swedes in the end of September); the curve predicts the continuing growth of the cases till 90% IPF toward the end of the year, followed by slow ending of the epidemics within six weeks (Tables 6 and 8 rely on M = 10 and therefore seem to provide lower IPF values than Fig. 4). The situation in Sweden is described as two waves, but in fact the development reflects continual changes in the R values, and can be separated to at least three distinct waves (Fig. 4 in fact comprises four waves). Real development may differ from the one outlined in Fig. 4, as the red “progression curve” of Fig. 4 comprises only one single flimsily confirmed point – i.e. about 10% infection around April.

The above analysis shows that the character of the case number growth may suggest where on a progression curve we are: exponential growth suggests that the infected population fraction is below 10%, while linear growth suggests that the infection fraction is above 10%, possibly up to 90% of the involved population (i.e. population which is involved in the specific wave). The above simple model could be extremely useful if we had measurements of randomly selected population samples supplying specific IPF values. 

Herd immunity
It is believed that certain levels of infection will slow down the disease spread and stop it, for example when one infected person will not be able to infect more than one other person, which will supposedly be achieved at infection levels of 1 – 1/R. This popular formula can be derived also from our above formula dN/dt = c * N * S by assuming that dN/dt = N (one ill will create one ill per time unit), providing 1 = c * S , yielding 1 = R * (1 – IPF/100), resulting in IPF/100 = 1 – 1/R (yielding IPF of 67% for R = 3). If a part of the population will be vaccinated or otherwise immunized, lower part of the population will have to be infected to achieve said herd immunity.

For Ro =  3.3, the threshold for reaching said herd immunity would be an IPF of about 70%. It is not clear how this would be reflected in the tentative infection curve of Fig. 4; possibly, “infected population fraction” could mean the fraction of susceptible part of the population, wherein the susceptible part would be 70% of the total population (see the paragraph “Immunity and herd immunity” below). In the paragraphs below, the point of reaching 90% IPF is called “the end of epidemic”, without taking the possible threshold of herd immunity into consideration. If such a threshold will turn out as relevant, the given values of IPF will mean a fraction of this threshold and not of the whole population.

The end of the epidemic
The case growth had exponential character with R0 = 3.3 in the beginning (see Table 5, for example Australia between March 3 and March 31). The succeeding government measures continually changed the rules according to which the population could have been mixed, resulting in alternatingly decreasing and increasing reproduction number. The infection growth in every wave has a course similar to the curves shown in Fig. 2 and 3, but  with shorter covert phase, followed by a short exponential phase and, if 10% of the population has been infected, by a linear phase lasting either till the following change of rules or till infecting 90% of available subjects. Under constant rules, detecting a linear period on the growth curve enables to estimate when the whole epidemics will end, if the number of really infected is known at least for one point within the linear period.

As mentioned above, the fraction of infected in the population is usually about 10 times higher than the number of positively tested (M = 10). The following table takes the numbers of cases from Table 5 (blue data showing cases per million population) and converts them to IPF % (dividing by 10,000 and multiplying by 10); presented are IPF on November 30, and IPF monthly increments for three preceding months. Table 8 shows that the countries went through sharp increase of the case numbers in November. If the current wave is not interrupted by changing measures, and if R0 of this wave is 1.5 (near to an R value suitable for the present wave in Sweden and Czechia), we can calculate the time for reaching certain IPF within this linear phase; Table 8 shows the time for reaching IPF of 90% – the point called “the end of epidemic”. As shown above, the rise from 10% infection to 90% infection theoretically takes about 10 weeks for R0 = 1.5 (see Fig.3, R0 = 1.5), providing about 1 week for every 10% infection increment. For example, Spain reported 35604 cases per million by November 30 (see Table 5), corresponding to about 3.5% positively tested, providing 35% (M=10) infected population fraction; it takes about (90-35=55,  55/10=5.5) 5.5 weeks to get from 35% to 90% infection in the population for R0=1.5, being rounded to 6 weeks in the table. If a herd immunity is involved, the time to the end should be shorter.
Although R0 in some countries is around 1.5 or more (Sweden or Czechia), strict measures in others has lowered the effective R. Therefore, additional predictions, based on the November growth rate, have been deduced from the fresh November data as follows. The weekly increments of the new cases have been read from the WHO November reports (here); it was verified that the November period comprised at least two succeeding one-week case increments (confirming that November comprised a phase of the linear infection growth in these countries, even though the speed was later somewhat stopped in Spain, Britain, Switzerland, France, and Czechia due to the new strict measures). The time to achieving 90% infection has been calculated for each country, supposing that November speed continues. For example, Sweden had 243,129 cases on November 30, corresponding to about 2.4 million really infected (M=10), and the linear November period comprised about 30,000 new cases per week, corresponding to 300,000 infected (M=10); 90% of 10.1 million Swedish population makes about 9 million, of which (9 - 2.4 =) 6.6 will be infected within (6.6/0.3=) 22 weeks. This estimation is in the 3rd column of Table 9. The 2nd column shows the values from Table 8.

It seems that Sweden has more infected people than being provided by M=10, and indeed M was higher in Sweden (see Table 5) and possibly elsewhere too; therefore, another prediction has been deduced from the weekly increments, supposing M=20 to provide a broader range of predictions, while calculating the time to 90% infection and using either the weekly increment of said linear phase, or the increment of the last November week if it is lower than said linear one (due to the taken measures), thus providing a range of times in the 4th column of Table 9, the higher limit being in brackets. The 5th column shows the average value of the previous columns.
It can be seen that Czechia might come to the end around the New Year. Germany might overcome the epidemics in summer and other countries in spring, if not implementing new measures. Israel may take another year if not releasing the stringent measures governing the country from the beginning of the epidemic. The East Asian and Australian regions will have to suffer for another year, either trying to catch all cases or vaccinate the people, similarly to North-European countries which have managed to keep the infection low.

The 2nd column of Table 9 shows that the epidemics might be over in all countries within two months form November 30, i.e. by the end of January, if curfews and lockdowns are stopped, and if CoV-2 is allowed to spread with R0 1.5 or more; a part of the remaining several percent uninfected people may provide several more cases within the following three months, without any relevant effects.

The 3rd and the 4th columns of Table 9 show that the end of the epidemics may be delayed by several months due to the unnecessary and harmful measures taken by the politicians. For example, Israel’s cases linearly increased from August till October (see Table 5), adding a million new cases every month, which might have ended the whole problem next January; but the current slowdown may unfortunately keep the country in suspense for many more months to come. Entirely empty hospitals in Israel show that no “flattening the curve” is needed, and only opportunism of the epidemic managers keep the country frozen. Millions of vaccines are being imported now in Israel, and many people (of whom a half or more will have already been infected without knowing that) may now be treated with vaccines which have not been duly tested.

Since no hard data about the real IPF values are available, there is always a possibility of surprisingly different development, such as, for example, that some countries, and surely regions (like North Italy), have already come near to the epidemic end or to a threshold of herd immunity.

The above analysis suggests that without erroneous measures imposed by opportunist politicians and health managers, the epidemics might have passed through the populations during the first year, without any need for mass vaccination.

The deaths as a percent fraction of infected people is called lethality (or fatality rate). The deaths per million can be taken from Table 5 (red numerals), as well as the cases per million (blue numerals), their ratio is multiplied by 100 to get lethality percent in Table 10.
The overall lethality values are in the range of 0.22+0.14% (if excluding Singapore and China). As for China, none of their reported values are reliable. As for Singapore, some death reports are probably missing in Singapore, otherwise it would be difficult to explain the totally different lethality value of 0.01%. Relatively lower value for Israel might be explained by underestimated deaths, or by overestimating the number of infected, or both; M for Israel may be somewhat less than 10, as the broad tracking and testing may have caught more cases than elsewhere; nevertheless, the November lethality values exhibit a lower dispersion among the considered countries, the values being in the range of 0.14+0.09. 

Other factors affecting the mortality (deaths per million people) and lethality (deaths per 100 infected people) include the age composition of the population, since deaths per million of people of certain age increases 3 times for every ten years of age (see further below); as a result, the age of most victims is 65+. As the fraction of citizens 65+ is twice greater in Sweden than in Israel, 2-fold mortality and lethality would be expected for Sweden compared to Israel (in the end of the epidemic) – and the present ratio indeed equals 2.

Table 7 (waves) shows that the lethality has been gradually decreasing during the pandemic. It can be also seen in Table 10, which shows the average overall lethality during the whole epidemic till November to be 0.22%, while the lethality in November alone to be in average only 0.13%.  

Lethality of 0.13% is somewhat higher than usual estimations of 0.1% for influenza (here). However, as the numbers of new deaths grow slower that the numbers of new cases, the lethality will still decrease, so that the final fatality rates in most countries will be very close to the flu values.

Mortality and age effects
On data from Czechia, Israel, Sweden, and U.S. I have demonstrated an exponential growth of the Covid-19 mortality with age, showing that the fraction of people dying with CoV-2 increases about three times for every ten years of age (here).

The published data on Covid deaths in age groups (ten years cohorts in four countries) have been combined with the age distributions in said countries (see the Table 9). It can be seen that the deaths numbers (absolute, not recalculated per million people) seem to form approximately geometric series with a common ratio of between 2 and 4 till the age of about 75. The exponential character to still higher ages is corroborated when recalculating the deaths numbers in each age cohort per million people in said cohort. The three small countries (all having a population of about 10 million) show nearly the same average common ratio value of about 3.5 + 0.1 for the geometric series, despite different sample sizes. The U.S. show a lower value of 2.5. Thus, the fraction of people dying with Covid-19 in Czechia, Israel and Sweden increases 3.5 times for every ten years of age, and in the U.S. 2.5 times for every ten years of age. Roughly, the mortality (death per million) thus increases 3 times for every ten years in all countries.

The people 85 years old or older form 2% US population (=6.6 million), so that about 60 000 Cov-2 deaths in this age group (as of September, see the Table) constituted less than 1% in the cohort. The probability that a person 85+ would die with Covid in the U.S. is 1%; the people around 75 years old have about three times lower probability of dying with corona – i.e. only 0.3%, the people around 65 old 0.1%, around 55 old 0.03%, around 45 old 0.01%, and around 35 old 0.004%. So, any panic is unfounded. Let us remember that about 1% of the population die every year regardless Covid-19. 

It as estimated (here) that Covid-19 mortality rate in the U.S. increases by about 9.5% per one year of age; this provides (1.09510 = ) 2.5 fold increase per 10 years of age – exactly the value as observed here in my Table 9, despite the fact that my data are from September and their from April. The publication further estimates an increase of 12% per year of age for South Korea, Italy, France, Germany, England and Wales, and Spain, which provides (1.1210 = ) 3.1 fold increase per 10 years. The increase of 3.5 for Czechia, Israel, and Sweden as observed here is somewhat higher (corresponding to 3.50.1 = 13% per year of age) than estimated for the above countries, but there are factors specific for different populations, as noted also in the cited publication.

What will the final deaths number be?
In the above paragraphs, the number of deaths per 100 infected (lethality) is assessed at roughly 0.1% for the later waves. The following table crudely estimates how many more people would die with corona till the end of the epidemic. The first prediction in the 4th column of Table 11 assumes that i) the infected population fraction (IPF) is as shown in Table 8, ii) the epidemic will stop at 90% IPF, iii) the lethality will now be 0.1% in all countries. For example, the U.S. seems to have 40% population being infected, so that 50% population would additionally be infected, which is about 166,000,000, of which 0.1% (=1/1000) would die. The second prediction in the 5th column of Table 11 assumes that a) the end of the epidemic will come as predicted in Table 9 (average), and b) the deaths will grow as quickly as they did during November (taken from Table 5); an average value is given in the 6th column. The last column sums the already reported deaths and the average predicted deaths.
It is not clear how an eventual herd immunity threshold would be reflected in the above estimations; one would expect lowering of the values, but the published estimations are higher than obtained here (here, here). Nevertheless, it is possible that IPF is higher than obtained for M=10 in some countries, at least for Sweden, and that the epidemic may stop at some herd immunity threshold lower than 90%; all these factors might reduce the average predicted additional deaths to a half. So that final deaths, for example, in Israel would be around 5000, in Switzerland around 7,000, in Sweden around 9000, and in Czechia around 10,000.

All the above estimations do not consider vaccination, which is planned to start soon, even though the predicted end of epidemic will overlap with the start of vaccination in many countries; the vaccination effects can hardly be predicted. Since there are no hard data available, all estimations here are very crude, and all predictions are still wilder. The Asian and Australian deaths have been very low so far, and predictions for these countries have no sense. It seems that the additional deaths (from now till end) will hardly exceed the present deaths numbers, with Germany and Israel being exceptions; the prediction for Germany of Table 11 seems rather high in relation to the present situation there.

Generally, final deaths in the U.S., Europe, and Israel will be far from apocalyptic predictions. The final mortality will roughly be close to 0.1% population, and they will constitute less than 10% of usual annual mortality in the end. How many of the deaths will, in longer run, appear as excessive in comparison to the previous years remains to be seen.

Excess deaths
As observed in the above paragraph on the predicted total deaths, about 0.1% population would die with Covid-19 during the whole epidemics, which may last roughly from December 2019 to February 2021, i.e. roughly 15 months. This makes CoV-2-associated annual mortality of (0.1%/1.25 = ) 0.08% population. Usual annual mortality is about 1/100 of the population, so that Covid-19-associated deaths would constitute 8% of the usual annual mortality, but only a part of this mortality will result in an annual excess deaths, since Covid-19 is not the main reason of deaths in many cases. A Czech analysis of October deaths showed that only 36% of the positively tested for CoV-2 in Czechia died directly due to the Covid-19 infection (here). This would suggest that a longer-term excess deaths would correspond to less than 8%, for example to (in the above case 0.36*8=) about 3%. For comparison, the average annual influenza-associated mortality rate (about 10 per 100,000, here) contributes to the usual annual mortality (which is as said above 1000 per 100,000) by 1%; of course strong flu pandemics may have contributed ten times more that that (here), easily overcoming Covid-19.  

Excess deaths due to CoV-2 will have to be assessed over longer periods of two years or more (here).

Interestingly, the above Czech analysis further showed that the 2020 October mortality was 14,000, compared to 9000 a years ago, wherein only 2,849 out of the 5000 excessive death were CoV-2 positively tested, so that 2,151 had to be assigned to other factors, showing that at least 40% of the excessive mortality may be caused by the reduced activity of the health system in the time of corona.

Differences between countries or regions
Comparing the final deaths in various countries strongly suggests that the measures have not affected the epidemic course as desired. For example, the complex and strict measures taken in Czechia resulted in more deaths than in Sweden who conducted the most liberal Covid politics. Observations that the number of infected and dead do not always parallel the protective measures taken in various countries have been expressed repeatedly (for example here), and that the measures should be softened (here). Closing schools, for example, has been harmful (here).

The measures have affected the speed of the virus spread by lowering its effective reproduction number, as demonstrated by the reconstructed propagation curve in Sweden (Fig. 4), but less clear is which measures had effects and how much. Other factors, beside the preventive measures, have an influence on the number of diseased and deceased people. These factors include, among others, the type of virus strain and the properties of the population; the population properties include genetic structure, age structure, compartmentalization due to its areal distribution and living habits. Some factors may affect the time course but not the final results, including the number of people bringing the infection from abroad or the timing of inoculation. Naturally, the number of deaths is a function of the infected population fraction, so that the values in different countries are comparable only at the same IPF levels (which, however, are not known and can be only roughly estimated).

Some differences may be explained by the population age. For example, the difference between Sweden and Israel, as seen in Table 8, namely roughly two-fold number of deaths per million in Sweden compared to Israel, might be explained by the fact that most deceased people are 65+, and the Swedish population has 20% people of this age segment, whereas Israel only 10% (see Table 9); if this should be the case, the strictest measures of Israel would then look unnecessary (if not stupid).

East-Asian and Australian regions show strikingly less reported cases, and correspondingly less deaths. Some North-European countries, except for Sweden, also show less reported cases. For example Norway, that applied very strict measures, including total lockdown, has reported four times less cases than Sweden. The mentioned countries may have delayed the full corona outbreak, but they will have great problems when the epidemics in most other countries will have ended. The question is what the final number of deaths would be in these countries. Of course, the vaccination will change the picture.

It was suggested that, in addition to the existence of different CoV-2 strains, previous infections by non-CoV-2 coronaviruses might have changed the immunity levels differently in various areas (here).

Immunity to Cov-2, herd immunity
Despite many doubts, Cov-2 infection does usually result in the formation of immunity. Antibody detection rate was 92% in hospitalized patients and 79% in non-hospitalized patients in one study; the total IgM and IgG detection was 63% in patients with <2 weeks from disease onset, 85% in non-hospitalized patients with >2 weeks disease duration, and 91% in hospitalized patients with >2 weeks disease duration (here). 

Cellular immunity, even without humoral immunity (such as the formation of antibodies), was also observed. Immunity to Cov-2 may be more widespread than antibody tests suggest; allegedly, for every person testing positive for antibodies, two were found to have specific T-cells which identify and destroy infected cells (here). Several studies have reported T cell reactivity against SARS-CoV-2 in 20% to 50% of people with no known exposure to Cov-2 virus; donor blood specimens obtained in the US between 2015 and 2018 displayed in 50% T cell reactivity to SARS-CoV-2; an exposure to other coronaviruses in the past may be responsible; for example, T cells reacting with SARS-CoV-1 (causing 2003 SARS epidemic) were shown to last at least 17 years after infection (here). Preexisting CD4+ T cells induced by common cold coronaviruses, such as HCoV-OC43, gathered in the past common cold episodes might cross-react with Cov-2, making some people more immune to Cov-2 and explaining population heterogeneity in this respect (here). Such pre-existing immunity can’t be detected with usual tests. It was demonstrated that some convalescents with PCR-confirmed CoV-2 infection had undetectable CoV-2 specific IgG, but showed cellular immunity to CoV-2; sixty days after onset of symptoms, 17% participants had borderline or negative IgG against the S1 protein of CoV-2, whereas cca 80% of PCR-positive volunteers with undetectable antibodies showed T cell immunity against CoV-2, the same as convalescent donors with strong antibody responses; but participants without symptoms of CoV-2 infection and without household contact did not have immunity (here).

It is generally supposed that good collective immunity (herd immunity) against Cov-2 is achieved at about 60% infected population rate (here). Some models show that the herd immunity might be achieved, or the community spread might be slowed down, at a population infection rates of about 40% or even much lower, if considering age-structured community mixing rates instead of homogeneous immunization, and when taking into account population heterogeneity and compartmentalization (here, here, here, here, here, here). However, the mentioned infection rates of 40% have been achieved in various regions, and no special protective effects are reported. Moreover, even the popular formula (1 – 1/R) for a “herd immunity threshold” (see paragraph on Herd immunity above), need not necessarily work with Covid-19. Certain patterns of the population mixing and population compartmentalization have been considered as a reason for lowering the immunity threshold, but they can also increase it; the herd immunity threshold need not be achieved as expected, for example when a population comprises particular separated communities (here).

Different SARS-CoV-2 strains
There are currently more than 35,000 publicly available genomes of SARS-CoV-2 (here, here), but no ground-breaking findings have been published about the numerous mutants (here), which would change the general picture. Nature issue of 25 November stated that there was no reason to believe that some of the mutants would emerge as a lineage with increased transmission (here).

One of the mutants, namely D614G (replacing aspartic by glycine in the position 614 of the spike protein), was stated to result in a strain with different properties (June, here); namely, it was stated that the new strain exhibited better transmission, in spite of what was stated in said publication of November 25. Several weeks ago it was suggested that the existence of these two strains may explain different epidemic courses in various parts of the U.S. (here). New York City and East Coast regions were heavily hit during the initial Covid-19 but were less affected during the second wave, while south western regions behaved quite oppositely. The two regions were supposedly infected by two different lineages of the virus, with the East Coast lineage being allegedly more infectious. It was suggested that the existence of different CoV-2 strains, in addition to different levels of past exposures to previously known coronaviruses, caused the observed differences (here).

SARS-CoV-2 origin
As for the origin of the virus, its escape from the Wuhan Virology Institute is beyond any doubt. The only questions are i) whether the virus escaped intentionally, and ii) whether the virus was genetically changed while in the laboratory. Some interesting changes in its genome when compared to its close relatives have been described (here), but no decisive conclusions have been published. For example, 5’-UTR region was repeatedly mentioned in Czech media as comprising more mutations than expected (here); however, by comparing the genome sequences of SARS-CoV-2 with other coronaviruses, I demonstrated that CoV-2 is not less conservative in its 5’-UTR than other coronaviruses (here). The principal work to answer the critical questions remains to be done. 

The epidemic probably broke out in Wuhan already in summer 2019 (here). In view of the above mentioned covert phase of several months, the epidemic start, probably an escape from a laboratory, may be put to spring 2019. 

Sweden did introduce protective measures, starting in March, including limitations of social contacts and protecting senior citizens. Most of the measures were voluntary, including limiting traveling and using masks. Work from home was recommended, as well as distance learning at secondary schools and universities. But the schools, businesses, shops, and pubs remained open, and the country borders remained opened as well.

Most democratic countries, in contrast to Sweden, closed their borders, banned the right of the citizens to travel in and out of the country, closed all types of schools, shops, hotels, restaurants, small businesses, of which many went bankrupt. The government actions went beyond any limits imaginable a year ago, including restricting restaurants to certain hours, dividing children to small groups at school and forcing them to see their teachers in zoom, ordering the use of masks in cars or during walks in forests, forcing little children to wear masks, banning prayers in churches and synagogues, finally entirely abolishing kindergartens and all schools, prohibiting to drive between cities, banning celebrations of the traditional holidays, prohibiting visiting children, parents and grandparents, restricting exit out of one’s house to several hundred meters, and forcing the citizens to regularly listen to the advice of totally ignorant politicians.

Despite the above striking difference between reasonable Sweden and irrational world, the mainstream media, like Guardian and Time, call the Swedish way a disaster (here) or a dangerous falacy (here), without reasonable arguments, but even scientific media do not like the Swedish tolerant way, for example Nature mentioning it as not persuasive (here). 

In the beginning, Sweden exhibited high mortality caused by high numbers of older citizens in the care homes (here) which, even beside corona, have various chronic problems, like employing or accommodating immigrants (here, here). However, the deaths numbers in many countries have caught up with the numbers in Sweden, and it turns out that the Swedish rational and humane attitude not only did not result in more deaths, but on the contrary, the most extreme examples of lockdowns and strict measures led to more deaths in the end (see Table 7). In the list of all countries (here), Sweden was the 39th in the number of cases and the 24th in the number of deaths on December 5.

Treatments for Covid-19
Only a small part of the infected have clear symptoms; unusual fatigue and other unpleasant residual problems have been observed, including neurological problems, but how long and whether lasting problems will stay is unclear (here). Covid-19 complicating consequences comprise the formation of blood clots (here). However, the reduction of lethality during one year with CoV-2 may suggest that the health establishment has learned to better handle the difficult cases. The application of ventilation and intubation has been limited and also optimized, serum with antibodies has been applied in critical cases, steroids were used, and several drug combinations have been employed, comprising vitamin D or derivatives, which seems to reduce severity of the Covid-19 (here). Triple combinations of hydroxychloroquine with zinc and either azithromycin or doxycycline seem helpful (here, here).

A number of companies have developed a vaccine and started clinical trials. Ready for mass production are the vaccines of AstraZeneca, Pfizer, and Moderna (here). As the title of Nature article of November 25 reads: “COVID-19 vaccines poised for launch, but impact on pandemic unclear” (here). AstraZeneca injects a “harmless” modified common cold adenovirus which enters the patient’s cells and produces Covid spike protein (for this common cold adenovirus see here); Pfizer and Moderna inject a “harmless” modified RNA which enters the patient’s cells and there acts as a messenger RNA, thus producing Covid spike protein; the spike protein fragments are supposedly presented on the surface of the cells and elicit the required immune response. The RNA vaccine was reported to be more efficient, 95% versus 70%, but also more expensive. Moreover, Moderna requires transport and storage at -20°C and Pfizer even at -70°C. By the way, it would be strange if the same types of vaccine required so different storage conditions, and Pfizer seem to have shot themselves in the foot, when trying to look as dramatic as that.

A price may be in the order of $25 for the RNA vaccine dose (here), so that the two-dose vaccination (as planned) might make 100 billion when used for two billion people, so that there surely is good motivation to vaccinate the whole mankind, as supported by Bill Gates (here), who personally contributed to the development efforts. However, once the vaccine development used to take many years; it would be the first time in history, when a vaccine would be developed in less than one year and so quickly applied. Moreover, using the adenovirus vector is not a routinely employed method, and in regard to an RNA vaccine – this is an entirely new concept, never before practically tested, let alone routinely employed. It might be dangerous to try a totally novel method on the whole mankind.

One of the principal researchers who participated in developing the RNA vaccine, Prof. Drew Weissman, warned against dangers of the RNA vaccines in his article three years ago (here): "Potential safety concerns that are likely to be evaluated in future preclinical and clinical studies include local and systemic inflammation, the biodistribution and persistence of expressed immunogen, stimulation of auto-reactive antibodies and potential toxic effects of any non-native nucleotides and delivery system components. A possible concern could be that some mRNA-based vaccine platforms induce potent type I interferon responses, which have been associated not only with inflammation but also potentially with autoimmunity. Thus, identification of individuals at an increased risk of autoimmune reactions before mRNA vaccination may allow reasonable precautions to be taken. Another potential safety issue could derive from the presence of extracellular RNA during mRNA vaccination. Extracellular naked RNA has been shown to increase the permeability of tightly packed endothelial cells and may thus contribute to oedema. Another study showed that extracellular RNA promoted blood coagulation and pathological thrombus formation." (bold-marked by me)

The mentioned autoimmune effects, as well as a number of other potential problems, are frightening. I would not recommend to anybody to participate in the vaccination by a vaccine which has not passed through normal classical examination and approval process. Particularly, this new type of vaccine, RNA-vaccine, has never been tested before. In addition, when the vaccination starts, a half population will probably have been immunized by CoV-2 itself, as at least this fraction of the population will have been infected by then. 

How should the epidemic have been handled?
Already in spring, broad experience from many countries showed that Covid-19 resembles influenza in endangering mainly weak people, but unlike flu it does not attacks children. It was soon clear that the risk conditions are chronic heart disease, diabetes, chronic lung disease, chronic kidney disease, obesity, cancer, immunocompromised status and old age. Many deaths involve two or three underlying conditions. On the other hand, healthy people pass the infection with light symptoms or mostly without any symptoms. The first point in any strategy should have been to protect the vulnerable by using masks in their environs, limiting their contacts with other people, and PCR-testing people in frequent contacts with them. The second point should have been to regularly assess the status of the epidemic in the country and in the regions by randomly and representatively sampling the population, similarly to sampling voters for pre-election polls; even a sample of 4000 people might help (millions tests have been performed without a clear goal). Serologic tests and simultaneous PCR would give a clear picture of the epidemic in a certain moment; monthly repetitions would clearly show the development. The numbers of hospitalized, of difficult cases, and of deaths could have been easily predicted. Most infected could have stayed at home. As the third point, in case of overloading, the health system might be helped by restricting large sport and culture events like soccer games or cinema performances, and eventually by voluntarily restricting unnecessary free-time activities, if needed. The fact is that in many countries, including Israel and Czechia, the hospitals had never been so empty as in the time of corona. As fourth point, those who have went through the infection might have been serologically looked for and given a free-pass certificate (preferably international), which would absolve them of any potential restrictions (and would make them a pool of safe persons for certain functions or in the environs of the vulnerable). In view of yet unclear long-term effects, the voluntary infection would not have been supported, but normal functions of the society would not have been stopped in order to avoid further infections. Schools and businesses would stay opened, but nobody would be prevented from personally protecting oneself if reconcilable with her/his work. As the fifth point, the state would have invested large sums on research of the current virus and its origin, including promoting general knowledge of the related subjects in virology and epidemiology, aiming at explaining the epidemic, preventing the next one, and developing safe vaccines – preferably within international cooperation, but if not possible, only within one country. 

All other measures, including closures, lockdowns, curfews, bans, fines, distancing, bubbles, capsules, curve flattening, rating, traffic lights, tracking, maps, and risk indicators/points/scores,  have been ludicrous and harmful. 

The current coronavirus pandemic has demonstrated that the Western democratic world is not able to cope with global problems. No leaders have risen up in any country to lead, and no political or scientific national or international body has come up with usable ideas. No systematic population random sampling has been performed. A dark cloud of fear has fallen upon the Western society and has frozen it. The health system has reduced its activities, the elderly homes prevented contacts with family members, contacts between parents and children have been prohibited, and people have been dying for many reasons other than corona. The media have not provided scientific information but only frightened and confused the public. Only rare voices dared to name China as the main culprit. China can be happy. Among democratic countries, only Sweden abstained from hysteria. Regarding nondemocratic countries, nothing can be expected from them, and of course they have been lying as usually.

Already in spring, it was clear that Covid-19 is not as dangerous as to stop all social and economic activities. Instead of protecting the vulnerable, the politicians in democratic countries were aping each other and locked their whole countries down; they will never admit errors, and they will never wonder why Sweden with less lockdowns has managed with less panic and less damages. Even if the pandemic will have faded away by itself, the corrupt Western officials will give credit to the vaccine and to themselves. 

As mentioned above, the virus does create both humoral and cell immunity, and it will fade away, probably next spring. Vaccines may contribute to the psychological recovery of the Western society, but they will be medically useless, as most population will have been immunized by the virus itself. However, without detailed serologic testing before the vaccination, nobody will be later able to distinguish the immunity caused by CoV-2 from the immunity caused by the vaccination.  

If lessons will not be taken from this pandemic, additional future events similar to Covid-19 may bring the Western democracy near its end.


The questions answered:
* What part of the population has really been infected by Covid-19? :  15-50% in many countries in December.
* How quickly does Covid-19 spread in the population? :  Without any measures, Covid-19 would pass through the population within 4 months, with mild measures 18 months, and with strict measures for several years.
* How does the 2nd wave differ from the 1st one? : By low lethality, usually 0.1% or less.
* How does the age affect the probability of death from Covid-19? : Very old have a chance of 1% to die on Covid, every 10 years of lower age  reduces the chance 3-fold.
* What is the real lethality of Covid-19? : About 0.1%.
* What will be the final number of deaths? : About 0.1% population will die with corona, but only a part will contribute to a longer term excess deaths, as mainly old and sick people die.
* Do the protective measures help? : Restrictions of the whole population prolong the epidemic and do not reduce the final deaths. Only protecting the most vulnerable may reduce the deaths.
* How do the countries differ from each other? : A wide range of different measures taken in the democratic countries have not resulted in much different results; the strictest measures have kept the cases and death near to zero in several countries – but only temporarily, and the economic costs would be too heavy if applied everywhere.
* Which of 200 countries do handle Covid-19 optimally and which reasonably? : None optimally, as none have systematically tested randomly sampled population groups; and Sweden relatively reasonably, as inflicting least psychological and economic damages.
* How should the epidemic have been handled? : 1) Randomized samples of the population should have been regularly tested both serologically and by PCR to assess the infected fraction both generally and in specific population segments; 2) personnel and families around ill senior people or chronically ill people should have been mask-protected and PCR tested; 3) large sport (soccer) and culture (cinema) gathering might have been limited if hospitals worked at full capacities, but immediately released when the capacities return; voluntary restrictions of free-time activities might also be considered; 4) serologically positive and recovered people should have obtained a free-pass certificate; 5) the state should have organized great efforts and resources on research of the current virus and its origin. 
* When will the epidemic end? : If the quick November growth of new cases is allowed to proceed without introducing new restrictions, the early spring will bring the end. Otherwise, dragging last stages of the epidemics will overlap with the vaccination actions, with unclear results.
* Is the vaccination needed? : No. The vaccination will be applied after a great part of the population will have already been infected. Quickly developed vaccines without due approval process may be dangerous for the mankind health.

The Main Points of CorSum:
a) A new virus epidemic started in Chinese Wuhan in summer 2019, after years of warnings against flimsy security in the Wuhan virology institute.
b) The number of positively tested people (which depends on the number of employed tests) has been dramatically announced in every country every day; however, the announced number of cases has been much lower than the number of really infected people, which has been usually 10 to 100 times higher, often 30 times higher, than the number of reported cases (the ratio of really infected to positively tested is called here as Ratio M).
c) The number of people who died with corona has been dramatically announced in every country every day, without mentioning that 1% population die every year, so that 3.3 millions usually die every year and 9000 every day in the U.S., 600,000 annually and 1600 daily in Italy, 100,000 annually and 275 daily in Sweden, etc. The deaths have been presented, after dividing by the reported cases, as a very high lethality of, for example, 3%, but the real lethality (after dividing by real cases) has been 0.1%, i.e. close to the lethality of flu.
d) Only 1% of the people who die with CoV-2 have no other health condition, and most are 65+ old; the number of deaths decreases 3 times for every ten years of the decreasing age; nearly no children die, in contrast to flu.
e) Data for 15 countries were tabulated from February till July in two-week and later one-month intervals (see Table 1), showing, among others, the number of cases per million people (=apparent morbidity), new daily cases, the number of deaths per million (=mortality), new daily deaths, and the number of tests per million; the infected population fraction (IPF) assessed from antibody survey was added where available. Additional parameters were calculated, including the Ratio M, the apparent lethality and the corrected lethality. The number of cases initially grew exponentially, ten fold in two weeks, corresponding to (10=) about 3.2 increase per week; this was in accordance with the generally accepted reproduction number of about 3.
f) Even mild Cov-2 infection seemed to confer cellular immunity against recurrent infection and thus contributed to the collective immunity.
g) The politicians around the world competed with each other in imposing strict measures and playing saviors of their nations, but all countries in fact imitated each other and were afraid to independently choose a strategy, except for Sweden, who decided to leave their public life open, and leave protection measures to the citizens’ free decision. The protective measures in various countries have seemed to affect the numbers of cases and deaths only minimally. Eastern Asia and Australia seemed to go through a milder disease; they aimed at catching and extinguishing all infections, but in the end experienced a “second corona wave” anyway. Sweden with its politics of minimal restrictions has achieved the least economic damages.
h) Only a repeated collection and testing of a representative population sample (RT-PCR of nasal swabs and antibodies in blood) would reveal the actually infected population fraction, and the population percentage having so far been infected (IPF), but surprisingly none of the 200 countries in the world has done such repeated testing, despite the feasibility and low cost. Haphazard antibody surveys in several countries until July have been employed, helping in rough estimation of IPF values at roughly 1-2% in Australia and Eastern Asia, 6-10% in Western Europe and Israel, roughly 20-30% in Italy and U.S., and possibly more in Sweden.

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