An idea on how to find the COVID-19 pandemic vaccine


As any person on our planet needs an obligation to prevent the COVID-19 pandemic, for themself and for others, I don't hesitate to state an idea of how to find the COVID-19 vaccine

Origin of the idea

COVID-19 caused terrible global damage with no signs of decline, lasted for nearly one year without a clear effective vaccine.

COVID-19 is a non-trivial pandemic. Vaccines before mass application must be tested in humans on a large scale. That requires a scientific measure worthy of the virus to thoroughly handle, avoiding dangerous side effects for the participants in the test.

COVID-19 opened a new stage of world medicine. Old knowledge cannot solve new problems. That requires discovering new laws of knowledge instead of deducing from old laws of knowledge. This is true of all disciplines including medicine. Fortunately, we have Machine Learning to fulfill this requirement.

Theoretical basis

In the third industrial generation, digitalization has removed a lot of unnecessary information, thereby shrinking a problem's large space into small. However, the digital itself is only used to quickly handle problems that have already been solved.
Today's Machine Learning, that is, in the fourth industrial generation, especially Genetic Algorithms is capable of synthesizing knowledge into new knowledge and thus solving unsolved problems, is very suitable for assist in the treatment of the coronavirus family with a large number of complex viruses that cause pandemics as the 21st century incident.

The destruction of nature and the extinction of many animals caused by humans disrupt the ecological balance. The coronavirus family that was only adapted in animals was forced to evolve over many generations, until COVID-19 was so sophisticated that it could live in humans and transmit disease at an unexpected rate with a way that the world medicine has yet to grasp.

But we believe that if the sun is still shining, we can completely control COVID-19 and its descendants in the future.

In fact, the world medicine has not been able to accurately determine COVID-19 and its evolutionary process and cannot describe the symptoms clearly. The leaf remedies of the problem only follow the virus because it has transformed into another variant. The evidence is that the second wave of COVID-19 outbreaks is raging with greater intensity and sophistication than before. Stereotyped prevention campaigns are both omitted and redundant. As a result, the epidemic is not stopped while economic losses are heavy.

To handle the root cause, we need to know the structure, basic properties, living conditions and other professional information of COVID-19 in order to determine the vaccine properly.

Genetic Algorithms - the highest representative of evolutionary machine learning, will come up with the problem at hand as an obvious obligation.


Medical professionals, microbiologists, epidemiologists need to give analysis, known characteristics or assumptions of coronavirus ancestors since they can only live in animals, as initialization parameters. The Genetic Algorithms will then evolve them until the generation where the virus is adapted to infect humans, COVID-19 must reveal its original form.

Not only virus identification, Genetic Algorithms can aid in vaccine production. It is the most powerful tool for solving complex constraint problems with an unlimited number of variables and constraints (up to the limit of computer resources) and any nonlinear mapping. Genetic algorithms can be used to solve the multi-goal problem with a reasonable medicinal ratio: correct the disease without causing side effects while being affordable, and satisfy other arbitrary conditions of the manufacturer.  So

Medicine + Genetic Algorithms = Successfully eradicate COVID-19

I am ready to provide the algorithms and cooperate with organizations and individuals interested in this solution.

All the best,
Pham Thanh Tuyen - Author of Football Predictions

Currently unrated


There are currently no comments

New Comment


required (not published)



What is 10 - 7?