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COVID-19: Salient Aspects of Coronavirus Infection, Vaccines and Vaccination Testing and their Implications

Pradeep K. Pasricha, Arun K. Upadhayaya
doi: https://doi.org/10.1101/2021.12.21.470882
Pradeep K. Pasricha
CSIR-National Physical Laboratory, New Delhi, India
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  • For correspondence: pradeep1951@rediffmail.com
Arun K. Upadhayaya
CSIR-National Physical Laboratory, New Delhi, India
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Abstract

In the present study, three basic aspects related to COVID-19 are presented.

  1. The occurrence of coronavirus infection is analyzed statistically as number of coronaviruses infected alveolar cells compared to normal alveolar cells in human lungs. The mole concept is used to estimate the number of normal alveolar cells per human lung. The number of coronavirus infections in infected alveolar cells is estimated from the published Lower Respiratory Tract (LRT) load data. The Poisson probability distribution is aptly applied to imply the incubation period of the coronavirus infection to be within day-3 to day-7, with the cumulative probability of 75%. The incubation period within day-0 to day-10 has a cumulative probability of 98%. It implies a 10-day quarantine to isolate an uninfected individual as a precautionary measure.

  2. Three vaccines to combat COVID-19, which adopt distinct paradigms while preparing them, are analyzed. These are Moderna’s mRNA-1273, Oxford-AstraZeneca’s ChAdOx1 nCoV-19 and Bharat BioTech’s COVAXIN. The mole concept is used to estimate the antigen mass density per dose of each of these vaccines as 10 g cm-3, 0.1 g cm-3 and 1 g cm-3, respectively. The vaccines are deemed to be compatible to neutralize the infection.

  3. A statistical analysis is performed of the Moderna’s mRNA-1273 vaccine efficacy of 94.1% and Oxford’s ChAdOx1 nCoV-19 vaccine efficacy of 62.1% in terms of groups of volunteers testing negative to vaccine by chance. In the Moderna vaccination testing scenario, since the probability of negative response of vaccine is small, the Poisson probability distribution for 95% cumulative probability is used to describe the vaccination testing in 300 samples of 47 volunteers each. Thus, 87% of samples have average group of 3 volunteers testing negative to vaccine. About 6% of samples have all volunteers testing positive to vaccine. In the Oxford vaccination testing scenario, since the probability of negative response of vaccine is finite, the Gaussian probability distribution for 95% probability is used to describe the vaccination testing in 75 samples of 120 volunteers each. Thus, 68% of samples have average group of 45 volunteers testing negative to vaccine. No sample has all volunteers testing positive to vaccine. A vaccine, irrespective of its efficacy being high or low, is necessary for mass immunization.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵1 Retired

  • ↵2 Environmental Sciences and Biomedical Metrology Division

  • https://www.biorxiv.org/content/10.1101/2020.05.16.097238v1

  • https://doi.org/10.1016/j.mbs.2020.108438

  • https://www.bharatbiotech.com/images/covaxin/covaxin-fact-sheet.pdf

  • https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(20)32661-1.pdf

  • https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)32661-1/fulltext

  • https://nihrecord.nih.gov/2020/12/11/nih-moderna-covid-19-vaccine-shows-promising-interim-results

  • https://www.cdc.gov/mmwr/volumes/69/wr/mm695152e1.htm?s_cid=mm695152e1_w

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted December 24, 2021.
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COVID-19: Salient Aspects of Coronavirus Infection, Vaccines and Vaccination Testing and their Implications
Pradeep K. Pasricha, Arun K. Upadhayaya
bioRxiv 2021.12.21.470882; doi: https://doi.org/10.1101/2021.12.21.470882
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COVID-19: Salient Aspects of Coronavirus Infection, Vaccines and Vaccination Testing and their Implications
Pradeep K. Pasricha, Arun K. Upadhayaya
bioRxiv 2021.12.21.470882; doi: https://doi.org/10.1101/2021.12.21.470882

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