Seroprevalence and attainment of herd immunity against SARS CoV-2

  1. Abhijit Paul
  2. Harshith B. Kadnur
  3. Animesh Ray
  4. Samrat Chatterjee
  5. Naveet Wig

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Abstract

The present study aims to predict the likelihood of and likely time required to attain herd immunity against COVID-19 in New Delhi due to natural infection.

Method:

An ODE-based mathematical model was constructed by extending the classical SEIR model to predict the seroprevalence rate. We estimated the parameter values for Delhi using available data (reported cases and the seroprevalence rate) and used them for future prediction. Also, changes in the seroprevalence rate with different possibilities of reinfection were predicted.

Results:

Maximum seroprevalence rate obtained through our model is 31.65% and also a reduction in the seroprevalence rate was observed for the upcoming one month (month of January, 2021) due to the reduced transmission rate. After increasing the transmission rate to the value same as the third wave in New Delhi, we obtained a maximum value of 54.96%. This maximum value significantly decreased with the reduction in the reinfection possibilities. Also, a little impact of the duration of persistence of antibodies, 180 vs 105 days, was observed on the maximum seroprevalence.

Conclusion:

This modelling study suggests that natural infection alone, as gauged by serial sero-surveys, may not result in attainment of herd immunity in the state of Delhi.

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  1. Version published to 10.4103/jfmpc.jfmpc_830_21
  2. ScreenIT

    SciScore for 10.1101/2021.01.22.21250328: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Experimental Models: Organisms/Strains
    SentencesResources
    The dynamics of these different classes across time (t) are described by the following set of differential equations:

    with S + E + P + U + T + RI + UR + UD + RR+ RD = N (total population) & S(0), E(0), P(0), U(0), T(0), RI(0), UR(0), UD(0), RR(0), RD(0) ≥ 0.

    S + E + P + U + T + RI + UR + UD + RR+ RD
    suggested: None
    Software and Algorithms
    SentencesResources
    Seroprevalence rate at a given time (t) are obtained from the following equation:

    Numerical simulation: All the numerical simulation for this mathematical model was performed in MATLAB by using the ODE45 function.

    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Nevertheless, some limitations noted in our model are,

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a protocol registration statement.

    About SciScore

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  3. Version published to 10.1101/2021.01.22.21250328v2 on medRxiv
  4. Version published to 10.1101/2021.01.22.21250328v1 on medRxiv