Vaccinated Susceptibles Trigger the New COVID-19 Wave After Mass Vaccination
Vaccinated Susceptibles Trigger the New COVID-19 Wave After Mass Vaccination
Abstract
This hypothesis believes that some vaccinated persons become susceptible and trigger the current COVID-19 wave in some countries. These vaccinated susceptibles are more likely to be infected than unvaccinated persons.
The hypothesis can explain many weird phenomena during the COVID-19 pandemic after global vaccination, which cannot be explained by traditional theories. For example, why the infection curves in some countries are closely related to the vaccination curves, why the infection rate remains high in some countries with high vaccination rate. Furthermore, the hypothesis can predict the future trend of the epidemic and help people to develop new strategies to prevent and control COVID-19.
The estimated curve of COVID-19 cases in the UK based on the hypothesis and vaccination data is surprisingly similar to the actual epidemic curve, which strongly indicates that the continuous increase of vaccinated susceptibles is the main cause of the epidemic.
Mandatory vaccination, booster shots and lockdown cannot fundamentally solve the epidemic caused by vaccinated susceptibles, but only delay the outbreaks and even make the future situation worse.
I hope that more people will understand this hypothesis. If this hypothesis is correct, some countries will face huge crises in the coming months. Therefore, in order to avoid or alleviate these crises, people should take right measures in advance, not wrong measures to exacerbate future crises.
Three Important Assumptions
The new hypothesis is based on the following three important assumptions:
1. At least two groups of people are generated after vaccination: potential susceptibles and actual susceptibles.
2. The immunity of the two groups depends on the concentration of neutralizing antibodies, which wanes over time. When the ratio of the concentration of neutralizing antibodies to the amount of inhaled virus is lower than an infection threshold value, they will be vulnerable to infection.
3. Each dose of COVID-19 vaccine makes a certain percentage of the vaccinated population potentially susceptible.
Weird Phenomena Cannot Be Explained by Traditional Theories
After the worldwide vaccination campaign, some weird phenomena emerged during the COVID-19 pandemic. This new hypothesis can explain these phenomena that cannot be explained by traditional theories:
1. In some countries with high vaccination rates, such as the UK and Ireland, the COVID-19 case rates have remained high for several months.
2. Seven to nine months after vaccination, the infection rate of vaccinated population in some countries is higher than that of unvaccinated population.
3. Seven to nine months after vaccination, the infection curves in some countries are closely related to the vaccination curves.
4. The vaccine efficacy varies greatly under different circumstances. In large public gatherings and small enclosed places (such as prisons), the vaccine efficacy is much lower than that under normal circumstance.
At Least Two Different Groups of Population Generated After Vaccination
The various groups of population that may be generated after vaccination are listed as follows:
A. Group with permanent immunity.
B. Group with waning immunity
C. Potentially susceptible group with waning immunity.
D. Actual susceptible group.
Some people believe that there is mainly one type of population after injecting COVID-19 vaccines: Group B. The new hypothesis supposes that there are at least two groups after COVID-19 vaccination: Group C and Group D.
Why Are There At Least Two Groups of Population After Vaccination?
When the vaccine efficacy is less than 60% and the vaccinated individuals are all in Group B, even if the vaccination rate is 100%, the basic reproduction number (R0) of the Delta variant in vaccinated population will be much greater than 1, and the infection rate will increase exponentially. However, the actual infection curve in some countries, such as the UK, shows that the R0 is around 1, and the infection rate remains high for several months. Traditional epidemiological theories cannot explain this strange phenomenon.
There might be at least two groups after COVID-19 vaccination (Group C and Group D), rather than a single group. Together with other people with immunity, , such as those who are naturally immunized, Group C provides temporary herd immunity for susceptible people and prevents the exponential increase of infection cases. The immunity of Group C and Group D depends on the concentration of neutralizing antibodies (Ca) , which wanes over time. When the Ca value drops to a certain level, the individual in Group C would be classified into the susceptible group (Group D).
Requirement for Vaccinated Potential Susceptibles and Actual Susceptibles to Be Infected
If the ratio of Ca to the amount of inhaled virus (Av) is lower than an infection threshold (It) value, individuals in Group C will be vulnerable to infection. The following infection inequality (1) is the requirement for vaccinated potential susceptibles and actual susceptibles to be infected:
(1) Ca/Av<It
The infection threshold It is related to the type of vaccine, the version of virus, and the virus transmission route (aerosol or droplet). The more infectious the variant, the greater the It value.
Requirement for a Potential Susceptible to Become an Actual Susceptible
A period of time after vaccination, when the ratio of Ca to the median value of the amount of inhaled viruses (Am) in infected persons is lower than It, the individual in Group C will become an actual susceptible and be classified into Group D. The following inequality (2) is the requirement for vaccinated potential susceptibles in Group C to be classified into Group D:
(2) Ca/Am<It
The median value of the amount of inhaled viruses (Am) in infected persons is affected by the type of virus variant, the season and non-pharmacologic measures.
Explanation of a New Epidemic Wave Triggered by Vaccinated Susceptibles
Seven to eight months after mass vaccination, the number of vaccinated actual susceptibles begins to increase rapidly, which triggers a new wave of epidemics with a high infection rate for a long time. Sometimes, the emergence of new virus variants or seasonal changes will suddenly cause a large increase in vaccinated susceptible population, which will trigger a new wave of epidemics in advance. This situation can make people mistakenly believe that the epidemic is mainly caused by a variant virus or the seasonal change.
In the early stage of the epidemic, the basic reproduction number (R0) is greater than 1, and the per capita infection rate of unvaccinated population is perhaps much higher than that of vaccinated population, which makes people mistakenly believe that it is an epidemic mainly caused by unvaccinated population.
When the increase in vaccinated susceptible population equals to the decrease due to infection, the infection rate will stabilize and remain high for a period of time. If the increase in vaccinated susceptible population is more or less than the decrease due to infection, the infection rate will increase or decrease accordingly. The fluctuation of the number of vaccinated actual susceptibles is related to the daily COVID-19 vaccine doses administered several months ago. This is why there is a somewhat similarity between the infection curve and the vaccination curve.
Estimated Curve of Daily Infection Cases Caused by Vaccinated Susceptibles in UK
The hypothetical Curve 1 in Figure 1 represents the distribution of the number of actual susceptibles over time. These vaccinated actual susceptibles are generated from a population who receives only one dose of COVID-19 vaccine on a certain day. In order to facilitate the following discussion, a simplified curve (Curve 2) is adopted instead of Curve 1. Curve 2 assumes that a large number of susceptible individuals appear concentrated on one day, and a fixed proportion of susceptible individuals appear on other days.
Curve 3 shows the distribution of the number of infection cases caused by vaccinated susceptibles when R0 is about 1. Curve 4 represents the distribution of infection cases caused by vaccinated susceptibles in the population who receives the 2rd dose on a certain day. There are two peaks in Curve 4 caused by each dose. Infection cases in Curve 4 are divided into two parts: "peak part" and "flat part" correspond to the peaks and flat part in Curve 4, respectively.
Based on the vaccination data from UK government[1], Curve A and Curve B in the following figure have been plotted, which correspond to “peak part” and “flat part” of the daily infection cases caused by the two doses of vaccination, respectively. The numbers of daily infection cases in Curve A and Curve B are added together to obtain the curve of total estimated daily infection cases (Curve C), which is very consistent with the curve of actual daily infection cases in the UK (Curve D)[2]. If the value of the part of Curve B before July 15 is multiplied by 7.2, Curve E can be obtained, which is very consistent with the front part of Curve D.
Some peaks and valleys of Curve C and Curve D always appear at the same time, for example, September 3rd, October 17th and November 5th. The high similarity of the two curves indicates that since mid-August, the epidemic in the UK is mainly affected by vaccinated susceptibles, which determine almost every peak and trough of the COVID-19 wave after mass vaccination in the UK.
If these assumptions are correct, the trend of the epidemic can be predicted based on this hypothesis. Curve C indicates that 40,000 to 60,000 infection cases per day in the UK might last until at least March next year. However, the recently implemented booster in the UK will change the height and shape of the part of Curve C after December, and the emergence of the new variant Omicron will lead to a substantial increase in daily infection cases.
Drawing of the Hypothetical Curves of Estimated Infection Cases
Curve A corresponds to “peak part” of the daily infection cases caused by the two doses of vaccination. The number of new daily infection case of “peak part” (Sp) can be calculated using the following formula (1):
(1) Sp=P1*D1m-n1+P2*D2m-n2
Dim-ni represents the number of people who are injected with the i-th dose of vaccine on the day m-ni. Pi is the effect factor corresponding to “peak part” caused by the i-th dose. The Pi*Dim-ni represents the number of infection cases on the day m due to the i-th dose, ni day after vaccination. The ni is the critical time point corresponding to “peak part”.
Curve B corresponds to “flat part” of the infection cases caused by the two doses of vaccination. The daily number of new infection case of the flat part (Sf) can be calculated with the following formula (2):
(2) Sf=Pf*Cm-k
Pf represents the effect factor corresponding to "flat part" caused by all doses. Cm-k represents the cumulative number of fully vaccinated people till the day m-k. k is the critical time point corresponding to the flat part.
In the case of only two doses of vaccination, the total estimated number of new daily infection case (Se) on the day m is calculated by the following formula (3):
(3) Se=Sp+Sf
The values of these parameters for the estimated infection cases in UK are supposed as follows:
P1=0.035, n1=213, P2=0.09, n2=236, Pf=0.0007, k=120
The critical time points (n1, n2, k) are consistent with the data from a Swedish COVID-19 vaccination study, which found that no vaccine effectiveness could be detected after 120~210 days (>210 days for BNT162b2, >120 days for ChAdOx1).[3]
Why the Infection Rate of Vaccinated Higher Than Unvaccinated in Some Groups
The infection rate of vaccinated persons in the UK over the age of 18 is higher than that of unvaccinated persons[4]. Among the population of 40-49 years old, the infection rate of vaccinated persons is 2.2 times that of unvaccinated persons.
Approximately 80% of the UK population over 18 have received at least one dose of COVID-19 vaccine and generate 85% infection cases in the population over 18. Because some persons have natural immunity or wear masks, the basic reproduction number (R0) of the Delta variant in the unvaccinated population should be much lower than the normal value (5-9)[5].
If 10% of population are vaccinated susceptibles, to maintain the real R0 in the total population around 1, the value of R0 should be 8.5 in the vaccinated susceptible population and 0.75 in the unvaccinated population, respectively (10%*8.5+20%*0.75=1). In fact, the proportion of susceptible people who are not infected every day is much lower than 10% of the vaccinated population, which suggests that vaccinated susceptibles are more likely to be infected than unvaccinated persons.
Proportions of Vaccinated Potential Susceptibles and Actual Susceptibles
Inferred from data in the UK, at least 15% of vaccinated population are potential susceptibles who may cause more than 30% of the population to be infected within a year. Since each dose will produce a certain proportion of potential susceptibles, after the third or fourth injection, once the booster doses are stopped, more than 60% of the population may be infected within a year. It seems that the COVID-19 vaccine is more like a drug than a vaccine in the conventional sense.
What is the real proportion of uninfected actual susceptibles in the vaccinated population in the UK during this period? People who understand this hypothesis can deduce a range of the proportion by analyzing the infection curve. More accurate calculations require large-scale clinical trials or real raw data. After determining this proportion, the real difference of the infection risk between vaccinated susceptibles and unvaccinated persons can be identified.
The Infection Curves in Some Countries Are Closely Related to the Vaccination Curves
The following figure compares the infection curves with the vaccination curves in four countries. The data used are all from ourworldindata.org. Since lacking data of each dose, it is difficult to draw an accurate estimated curve of infection cases. However, by comparing the infection curve and the vaccination curve, people will discover that the peak of the vaccination curve usually has a corresponding peak on the infection curve, and the time intervals between the two peaks is usually about 230 days, which is related to the kind of vaccine.
From January to April 2021, the vaccination curve of Turkey has five main peaks, corresponding five main peaks on the infection curve. Except the first pair of peaks, the time intervals between other four pairs of peaks range from 230 to 239 days. The time interval between the first pair of peaks is only 208 days, because the peak of Jan. 15th on the vaccination curve belongs to the first dose (when drawing the previous estimated infection curve of the UK, the time interval n1 of the peaks corresponding to the first dose is 213 days).
The time intervals between the two pairs of peaks on the curves of Singapore are all 202 days, probably because the two peaks on vaccination curve belongs to the first dose. The promotion of booster shots in Singapore may delay the appearance of the corresponding infection peaks of the vaccination peaks after 5/20.
The time intervals between the three pairs of peaks on the curves of Serbia range from 224 to 234 days. The booster shots may also delay the appearance of the corresponding infection peaks of the vaccination peaks after 3/14.
The time intervals between the two pairs of peaks on the curves of Israel are 242 and 232 days, respectively. The booster shots would delay the appearance of the corresponding infection peak of the vaccination peak 2/22.
Since the time interval is related to the type of vaccine, the similarity between the infection curve and the vaccination curve would become weaker if a country adopts multiple vaccines.
Why Vaccine Efficacy Varies Greatly under Different Circumstances
In July 2021, after multiple, large public gatherings in Provincetown, Massachusetts, USA, a large outbreak of SARS-CoV-2 infections caused by the Delta variant was reported.[6] Approximately 74% of 346 cases occurred in fully vaccinated persons. The remaining 123 patients were unvaccinated, not fully vaccinated, or whose vaccination status was unknown. At that time, about 62% people in Massachusetts were fully vaccinated. It can be calculated that the risk of infection for fully vaccinated persons is at least 1.72 times that of unvaccinated persons in the large public gatherings. The possible reason is that the amount of inhaled virus (Av) in the large public gathering is 10-100 times that of normal conditions, which satisfies the infection inequality (1) and makes the vaccinated potential susceptibles become susceptible to COVID-19. This case indicates that vaccinated persons are more likely to be infected than unvaccinated persons under some special circumstances.
During July 2021, a COVID-19 outbreak involving the Delta variant was identified in a federal prison in Texas, infecting 172 of 233 (74%) incarcerated persons in two housing units. The attack rate was higher among unvaccinated versus fully vaccinated persons (39 of 42, 93% versus 129 of 185, 70%).[7] The amount of inhaled virus (Av) in the housing unit may be hundreds or even thousands of times that of normal conditions. When the infection inequality (1) is satisfied, all potential susceptibles become susceptible and infected. The vaccine efficacy in that case is only 25%, much less than that under the normal circumstance in the same period (>90%).
Prediction of Epidemic Trend Based on the Hypothesis
After global mass vaccination, the epidemic situations in various countries have become more complicated, and the traditional epidemic models are difficult to explain these strange phenomena and predict epidemic trends. This new hypothesis can not only explain these phenomena, but can predict the epidemic trends.
By analyzing raw data and conducting clinical trials in a country, relevant parameters can be accurately determined, so as to plot more accurate epidemic curve and predict the epidemic trend in the country.
How to Prove the Hypothesis
To prove this hypothesis is not complicated. If there are more accurate raw data for reference, this hypothesis can be further verified. When several large-scale clinical trials are well-designed and conducted, it can be accurately determined whether the hypothesis is correct in a few weeks.
If the development of the epidemic in the next few months is exactly the same as the prediction based on the hypothesis, its correctness is also be proven. However, it is too later for the world to take measures after months of verification
Effect of Some Preventive Measures on the Epidemic Caused by Vaccinated Susceptibles
Mandatory vaccination would increase vaccinated susceptibles and lose the control group. Once there is no control group, it is impossible to determine which strategies are more effective through comparison, and it would be difficult to accurately determine the values of these parameters through clinical trials, which are used in the hypothesis to predict the epidemic trend.
Since each dose makes a certain percentage of the vaccinated population potentially susceptible, booster shots are unsustainable and may make future COVID-19 outbreaks even worse.
The lockdown would accumulate vaccinated susceptibles continuously. Once the lockdown is released, the infection cases will increase exponentially. Therefore, the lockdown cannot fundamentally solve the epidemic caused by vaccinated susceptibles, but only delays the outbreak of the epidemic.
If this hypothesis is correct, some prevention measures will not only fail to prevent the spread of COVID-19, but will worsen the situation and even cause the collapse of the medical system and inevitable catastrophe.
If this hypothesis is correct, some countries will face huge crises in the coming months. Therefore, in order to avoid or alleviate these crises, people should take right measures in advance, not wrong measures to exacerbate future crises.
To Improve the Hypothesis
Regardless of your opinion on the COVID-19 vaccines, I hope more people can understand this hypothesis and provide more evidence to support or refute this hypothesis, so that we can further improve this hypothesis or find a more perfect theory to replace it.
Thanks a lot.
Doning Young, PhD
Founder & CEO of Sumfound Technology
2021/12/15
[1] Vaccinations in United Kingdom
[2] COVID-19 Cases by specimen date in United Kingdom
[4] COVID-19 vaccine weekly surveillance reports
[5] CDC report on the delta variant