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A New Study Links COVID Vaccine Induced Immunological Memory And Neutralizing Antibody Levels

COVID-19 vaccinations have been shown to protect against SARS-CoV-2 infection. However, the dynamics of vaccine-induced immunological memory acquisition and neutralizing antibody synthesis remain unknown, restricting vaccine research and vaccination regimen design. Xin Gao, Jianwei Li, and Dianjin Li led a group of researchers from Peking University in Beijing, China, who used a mathematical model to describe the COVID vaccine-induced immunological memory response. They did this by fitting the virus infection and vaccination datasets. They optimized the immunization protocol, including the dose and timing of vaccinations, and anticipated the effect of the fourth dose. They used the viral transmission model to show how vaccines can cut down on the spread of viruses. This could be useful when making public health policy.

Model Set

The researchers built a simpler within-host immune network primarily focused on humoral immunity. This network was made up of three modules: the virus module (VV, which represented the SARS-CoV-2 virus and inactivated vaccines), the humoral effector module (EE, which represented virus-specific plasma cells and virus-specific neutralizing antibodies), and the humoral memory module (MM, which primarily meant virus-specific memory B cells). They developed a 3-variable ordinary differential equations model to represent the dynamic process of immune response to the SARS-CoV-2 virus. The SARS-CoV-2 virus proliferates following host infection in viral module VV, but inactivated vaccines do not. The SARS-CoV-2 virus or inactivated vaccines stimulate virus-associated antigens and activate host immunity. Antigen-specific B cells grow and become plasma cells and memory B cells when an antigen stimulates them. Plasma cells produce neutralizing antibodies to get rid of the virus.

Dynamic Of Virus Infection

The researchers employed viral load data from the early stages of infections to analyze the viral infection dynamics, which enabled them to fit the virus's growth process more precisely. To provide the dataset, the model was employed. According to the findings, the average viral proliferation rate for nine people was 3.3 per day, and the viral clearance rate was 0.018 per AU per day. They explored how the virus or vaccine stimulates immunological memory infection in addition to the dynamics of the SARS-CoV-2 disease. According to the findings, viral clearance takes 13.9 days, while forming a full humoral immune memory takes many months. On a temporal scale, the formation of particular immune responses and immunological memories differ significantly.

Vaccination Dynamic

The researchers studied the vaccine-induced immune response using the fitted parameters of viral clearance and immunological memory formation. Given that the inactivated SARS-CoV-2 in CoronaVac can't make new viruses, they gave three doses of vaccination data, including geometric mean titers after three ug and six ug doses of CoronaVac given in four different ways. doses of

They constructed a simple mathematical model to reproduce the dynamics of immunogenicity and immunological memory following CoronaVac vaccination based on data from viral load and geometric mean titers after immunization. They quantified CoronaVac's protection against SARS-CoV-2 infection by replicating the maximal viral load and recovery time after illness. They next refined the CoronaVac dosage and the booster injection schedule. Finally, we illustrated how vaccination affects viral propagation.

Due to the lack of immunological memory, the geometric mean titers caused by the first and second doses were restricted. Furthermore, since immunological memory was not completely developed when the second and third doses were separated by two months, geometric mean titers caused by the third dosage did not rise considerably compared to the double doses. When the second and third doses are separated by eight months, immunological memory is developed, and geometric mean titers generated by the vaccine are the greatest, up to tens of times higher than geometric mean titers before vaccination. Based on the simulation results, they think that the first two doses of inactivated vaccination are meant to help the body build up immunological memory. The third dose is intended to make the body neutralize antibodies after the body has learned how to fight off infections in the past.


The model provides a framework for predicting antibody levels elicited by inactivated vaccines at different vaccination dosages and regimens, which might be used in conjunction with clinical studies. Furthermore, this model might serve as a guide for future vaccine development.

About The Authors

Suleman Shah

Suleman Shah - Suleman Shah is a researcher and freelance writer. As a researcher, he has worked with MNS University of Agriculture, Multan (Pakistan) and Texas A & M University (USA). He regularly writes science articles and blogs for science news website immersse.com and open access publishers OA Publishing London and Scientific Times. He loves to keep himself updated on scientific developments and convert these developments into everyday language to update the readers about the developments in the scientific era. His primary research focus is Plant sciences, and he contributed to this field by publishing his research in scientific journals and presenting his work at many Conferences. Shah graduated from the University of Agriculture Faisalabad (Pakistan) and started his professional carrier with Jaffer Agro Services and later with the Agriculture Department of the Government of Pakistan. His research interest compelled and attracted him to proceed with his carrier in Plant sciences research. So, he started his Ph.D. in Soil Science at MNS University of Agriculture Multan (Pakistan). Later, he started working as a visiting scholar with Texas A&M University (USA). Shah’s experience with big Open Excess publishers like Springers, Frontiers, MDPI, etc., testified to his belief in Open Access as a barrier-removing mechanism between researchers and the readers of their research. Shah believes that Open Access is revolutionizing the publication process and benefitting research in all fields.

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