A Deterministic Agent-based Model with Antibody Dynamics Information in COVID-19 Epidemic Simulation

  1. Zhaobin Xu
  2. Hongmei Zhang

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Abstract

Accurate prediction of the temporal and spatial characteristics of COVID-19 infection can provide favorable guidance for epidemic prevention and control. We first introduce individual antibody dynamics into an agent-based model. Antibody dynamics model can well explain the antibody fading effects through time. Based on this model, we further developed an agent-based approach which considers the dynamic behaviors of each individual antibodies. The method can effectively reflect the dynamic interaction between the antibody and the virus in each host body in the overall population. Using this method, we can accurately predict the temporal and spatial characteristics of the epidemic. It can quantitatively calculate the number and spatial distribution of infected persons with different symptoms at different times. At the same time, our model can predict the prevention and control effect of different prevention and control measures. At present, China’s dynamic zero strategies mainly include large-scale nucleic acid test, isolation of positive infected persons and their close contacts. Our model demonstrates that for a less infectious and more virulent variant, this approach can achieve good preventive effect. The effect of reducing social contacts and quarantining only positive infected persons is relatively weaker on epidemic control. This can explain why China’s targeted epidemic-control measures had an excellent performance in 2020 and 2021. However, our model also warns that for the highly infectious and less virulent variant, targeted epidemic-control measures can no longer achieve effective control of the epidemic. Therefore, we must choose to quarantine potential infected groups in a wider range (such as the quarantine of secondary close contact and tertiary close contact) or coexist with the virus. Furthermore, our model has a strong traceability ability, which can effectively conduct epidemiological investigation to unearth patient number zero based on the early epidemic distribution. In the end, our model expands the traditional approaches of epidemiological simulation and provides an alternative in epidemic modeling.

Major findings

First, a method was developed to integrate the characteristics of individual antibody dynamics into epidemic prediction;

Second, this model can effectively predict the spatiotemporal characteristics of patients with different symptoms (including asymptomatic patients, mild and severe patients, etc.);

Thirdly, this model proves that China’s dynamic zero strategy which include the quarantine of close contact people is more efficient than just isolating positive cases;

Fourth: This model also reflects the limitations of targeted epidemic-control strategies and warns that for the highly infectious and less virulent variant, targeted epidemic-control measures can no longer achieve effective control of the epidemic;

Fifth, this model can help epidemiological research and find out patient zero according to the early incidence of the epidemic.

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  1. ScreenIT

    SciScore for 10.1101/2022.05.11.22274979: (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

    Antibodies
    SentencesResources
    To further account for this possibility, we adda new set of equations as shown below: p denotes antigen-like substances in the environment; q denotes antibodies bound to antigen-like substances in the environment.
    antigen-like
    suggested: None
    Assuming that the number of individuals in the population is N, the antibody virus complex in each individual is represented as xi, the concentration of antibodies is represented as yi, virus concentration is represented as zi, environmental antigenic substances is represented as pi, and the antibody-environment antigen complex is represented as qi.
    antibody-environment
    suggested: None
    Software and Algorithms
    SentencesResources
    Matlab codes can be accessed at: https://github.com/zhaobinxu23/antibody_dynamics_agent-based_model
    Matlab
    suggested: (MATLAB, RRID:SCR_001622)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    However, a critical limitation of the Markov chain model is the antibody waning effects does not follow simple mathematical function. As shown in Figure 1B, the declination of protection effects brought by antibodies cannot be represented as simple mathematic equation. Therefore, we developed an antibody dynamic theory and attempted to integrate the antibody information into the prediction of population morbidity. Our antibody dynamics model can well explain the protection cycle of vaccines or primary infection, it can also be used to predict the protection duration of natural infection or vaccination [20]. The application of ordinary differential equations is much more accurate and physically reliable compared to the usage of simple math functions. This model is a deterministic model which could generate a fixed morbidity landscape given specific population contact matrix and the antibody dynamic parameters for each individual within certain group. The antibody concentration together with the virus loading amount within host body can more explicitly and accurately reflect the probability of infection and the number of viruses released to the environment at different time points, which provides a significant improvement toward our previous Markov chain model. Meanwhile, this deterministic agent-based model inherits the benefits of Markov-chain model. Each individual is affected by the state of its surrounding agents by the application of contact matrix. He can spread the viru...

    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.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2022.05.11.22274979v1 on medRxiv