Rates of SARS-CoV-2 transmission and vaccination impact the fate of vaccine-resistant strains
Scientific Reports volume 11, Article number: 15729 (2021) https://doi.org/10.1038/s41598-021-95025-3
Simon A. Rella1, Yuliya A. Kulikova2,
Emmanouil T. Dermitzakis 3 Emmanouil.Dermitzakis at unige.ch
Fyodor A. Kondrashov1 fyodor.kondrashov at ist.ac.at
This simulation study found that, as with influenza,
a viral mutation is a virtual certainly when only vaccination is used
the result is vaccine-resistant strains
Vaccination alone won’t counter rise of resistant variants: Study
MDEdge Report of the study on this page
Study ignores solutions already in use around the world
- drugs/supplements to prevent and/or fight the infection
Also: Non-vaccination solutions are vital in regions of the world that cannot afford vaccines
Vaccination publications in VitaminDWiki 87 as of Aug 1, 2021
- Warning - mass vaccinations typically create mutations that are vaccine resistant - July 29, 2021
- Vitamin D, C, A, and E, as well as Iron, Se, and Zinc each augment vaccine response – July 2021
- Problems with vaccine use during a pandemic - Dr. Bossche interview with transcript - April 22, 2021
- Vaccines Are Pushing Pathogens to Evolve – May 2018
- Reasons why the virus might mutate and become immune to the COVID-19 vaccine – Nov 2020
- Worrisome New Evidence That Vaccines Are Less Effective Against Variants - March 2021
- Vaccines are plan A for COVID-19 Immunity (no plan B ) - Sept 11, 2020
- COVID-19 and variants are here to stay (he fails to mention that vitamin D might help) - June 2021
- COVID-19 vaccines look good in the short term, but probably not good for the long term
- If ANY of these vaccination problems occur, the US will not accomplish herd immunity
Vitamin D and Virus
- Vaccine response improved by Vitamin D (Shingles in this case) – Jan 2021
- Obese get less benefit from vaccines: influenza, hepatitis B, rabies and now COVID-19 - March 2021
- Why Vitamin D is better than the Flu Vaccine – Nov 2018
- Vitamin D 10 x better than Flu Vaccine if you have very low vitamin D – Feb 2017
- COVID-19 5X worse if poor Vitamin D gene (CYP2R1) – June 2021
- It is very easy to activate a poor Vitamin D Receptor
- 12 items in both Virus and Receptor categories as of July 2021
- COVID-19 inflammation extinguished by 60,000 IU of vitamin D nanoemulsion daily for a week – RCT May 2021
COVID-19 treated by Vitamin D - studies, reports, videos
- As of April 29, 2022, the VitaminDWiki COVID page had: 19 trial results, 37 meta-analyses and reviews, Mortality studies see related: Governments, HealthProblems, Hospitals, Dark Skins, 26 risk factors are ALL associated with low Vit D, Fight COVID-19 with 50K Vit D weekly Vaccines Take lots of Vitamin D at first signs of COVID 126 COVID Clinical Trials using Vitamin D (March 2023) Cost to prevent a COVID death: 11 dollars of Vitamin D - Nov 2022
5 most-recently changed Virus entries
Vitamin D plus others fight COVID-19
- COVID-19 risks reduced by Vitamin D, Magnesium, Zinc, Resveratrol, Omega-3, etc. (auto-updated)
- Many drugs, such as Vitamin D, decrease the risk of COVID-19 – July 2021
- Vitamin D is one of 14 ways proven to treat COVID-19 – July 2021
- Vitamin D has the most supporting science of all micronutrients to fight COVID-19 – May 2021
- Many supplements appear to fight COVID-19 – vitamin D cited 52 times – May 2021
- How Vitamin D, Magnesium, Omega-3 and Zinc prevent and treat COVID-19 and many other health problems – June 2021
- Nutrients etc, which fight viruses and fortify the immune system
- COVID-19 doctors not allowed to use treatments that work - Dr McCullough Video and transcript May 2021
- Several talks
Fight viral infection WITHOUT Vitamin D
- Ivermectin and COVID-19 - many studies - Note: Ivermectin increases amount of vitamin D getting to cells
- Similar effectivenss as Vitamin D, but Vitamin D also fights (prevents/treats) 40+ other health problems
Vitamin D plus others improve vaccine effectiveness (and might prevent vaccine resistance)
Download the PDF from VitaminDWiki
Vaccines are thought to be the best available solution for controlling the ongoing SARS-CoV-2 pandemic. However, the emergence of vaccine-resistant strains may come too rapidly for current vaccine developments to alleviate the health, economic and social consequences of the pandemic.
To quantify and characterize the risk of such a scenario, we created a SIR-derived model with initial stochastic dynamics of the vaccine-resistant strain to study the probability of its emergence and establishment. Using parameters realistically resembling SARS-CoV-2 transmission, we model a wave-like pattern of the pandemic and consider the impact of the rate of vaccination and the strength of non-pharmaceutical intervention measures on the probability of emergence of a resistant strain.
As expected, we found that a fast rate of vaccination decreases the probability of emergence of a resistant strain.
Counterintuitively, when a relaxation of non-pharmaceutical interventions happened at a time when most individuals of the population have already been vaccinated the probability of emergence of a resistant strain was greatly increased.
Consequently, we show that a period of transmission reduction close to the end of the vaccination campaign can substantially reduce the probability of resistant strain establishment. Our results suggest that policymakers and individuals should consider maintaining non-pharmaceutical interventions and transmission-reducing behaviours throughout the entire vaccination period.
Our model suggests three specific risk factors that favour the emergence and establishment of a vaccine-resistant strain that are intuitively obvious:
- high probability of initial emergence of the resistant strain,
- high number of infected individuals 54 and
- low rate of vaccination 55.
By contrast, a counterintuitive result of our analysis is that the highest risk of resistant strain establishment occurs when a large fraction of the population has already been vaccinated but the transmission is not controlled. Similar conclusions have been reached in a SIR model of the ongoing pandemic 56 and a model of pathogen escape from host immunity57. Furthermore, empirical data consistent with this result has been reported for influenza 58. Indeed, it seems likely that when a large fraction of the population is vaccinated, especially the high-risk fraction of the population (aged individuals and those with specific underlying conditions) policy makers and individuals will be driven to return to pre-pandemic guidelines59 and behaviours conducive to a high rate of virus transmission60,61. However, the establishment of a resistant strain at that time may lead to serial rounds of resistant strain evolution with vaccine development playing catch up in the evolutionary arms race against novel strains.
Prior to discussion of the implications of our model we reflect on several properties of the assumptions and implementation of our model. In classical SIR-like deterministic models even a single individual infected with a vaccine resistant strain with reproduction number Rt > 1 will lead to automatic establishment of the strain in the population. In an analytical solution, a SIR-like model, even for Rt < 1 for the vaccine resistant strain, the number of infected individuals will tend to 0 but only as time tends to infinity. In actual populations, a single individual infected with a vaccine resistant strain still has a non-negligible chance not to infect anyone causing the variant to go extinct due to random stochastic forces 47. Therefore, the implementation of stochastic dynamics 62,63 of the vaccine resistant strain at low frequency in our model, considers the impact of random drift on its dynamics, which lies at the heart of extinction of rare strains.
We considered the dynamics of a single vaccine resistant strain, however, there may be different mutations that can lead to vaccine resistance. The emergence of different genotypes causing the same phenotype is analogous to a distinction in population genetics between alleles identical by state and by descent 64. In our model, the treatment of independent emergence of different mutations as a single entity does not influence the dynamics under the following two assumptions. First, that different mutations lead to exactly the same phenotype, which is vaccine resistance, and, second, that there is no recombination. However, the reported dynamics may be quantitatively different if either of the two assumptions do not hold.
We have not explored the parameter ranges of fah, fa,, the high and low rates of transmission, respectively, and F, the threshold between low and high rate of transmission. We selected the fah and fa,, to represent the known transmission values at the start of the pandemic 31-33. However, evolving strains are reported to have a higher rate of transmission 10 leading to higher fah and, possibly, fa, values than we used. An increase in the rate of transmission is not expected to qualitatively influence the reported dynamics, but would shift the probability density of establishment of a resistant strain (Fig. 3). Indeed, the peak probability at 60% vaccinated individuals roughly corresponds to the point at which for the given fah, Rt, the average number of transmissions for one infected individual, becomes less than 1. Because the reproduction number for the vaccine resistant strain, R„ is equal to (S + V)p/N(y + 8), the perk of the risk of establishment of the vaccine resistant strain would increase proportional to an increase of fa. An increase in either Fh or F, would lead to more individuals becoming infected and a proportionally higher rate of emergence of the vaccine related strain, but would not change the qualitativebehaviour of the model. Furthermore, an increase of Ft would lead to reversion to a high transmission rate with higher number of infected individuals, leading to shorter periods of low transmission and decreased probability of extinction of the vaccine resistant strains.
The results of our model provide several qualitative implications for the strategy forward in the months of vaccination. In our model, the probability of emergence of a resistant strain in one individual per day was in the range of 10**-5 to 10**-8 for a population of 10*7 individuals. For the entire human population of ~ 10**10 that probability would be 10-8 to 10"11, which does not seem improbably large. As of February 2021, ~ 10**9 individuals have been infected by SARS-CoV-265 with an average 14 days of sickness per individual 25, so > 10**10 number of total days of infected individuals. Furthermore, highly mutated strains may emerge as a result of long shedding in immunocompromised individuals, a rare but realistic scenario 66-68. Taken together, the emergence of a partially or fully vaccine-resistant strain and its eventual establishment appears inevitable.
However, as vaccination needs to be ahead of the spread of such strains in similar ways to influenza 23, it is necessary to reduce the probability of establishment by a targeted effort to reduce the virus transmission rate towards the end of the vaccination period before the current vaccines become ineffective. Conversely, lack of non-pharmaceutical interventions at that time can increase the probability of establishment of vaccine-resistant strains. For example, plans to vaccinate individuals with a high risk of a fatal disease outcome followed by a drive to reach herd immunity while in uncontrolled transmission among the rest of the population is likely to greatly increase the probability that a resistant strain is established, annulling the initial vaccination effort. Another potential risk factor may be the reversion of vaccinated individuals to pre-pandemic behaviours that can drive the initial spread of the resistant strain.
One simple specific recommendation is to keep transmission low even when a large fraction of the population has been vaccinated by implementing acute non-pharmaceutical interventions (i.e. strict adherence to social distancing) for a reasonable period of time, to allow emergent lineages of resistant strains to go extinct through stochastic genetic drift. The implementation of non-pharmaceutical measures at a time of high vaccination can also help reduce infectivity when the efficacy of vaccines is not perfect 69.
Additional factors that may make these measures even more effective are:
- (1) increased and widespread testing,
- (2) rigorous contact tracing,
- (3) high rate of viral sequencing of positive cases 58,70 and
- (4) travel restrictions.
Finally, while our model formally considers only one homogenous population, our data also suggest that delays in vaccination in some countries relative to others will make the global emergence of a vaccine-resistant strain more likely. Without global coordination, vaccine resistant strains may be eliminated in some populations but could persist in others. Thus, a truly global vaccination effort may be necessary to reduce the chances of a global spread of a resistant strain.