Multilevel Integrated Model with a Novel Systems Approach (MIMANSA) for Simulating the Spread of COVID-19

  1. Arpita Welling
  2. Abhilasha Patel
  3. Padmaj Kulkarni
  4. Vinay G. Vaidya

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Abstract

Due to the spread of the coronavirus, public health officials grapple with multiple issues such as recommending a lockdown, contact tracing, promoting the use of masks, social distancing, frequent handwashing, as well as quarantining. It is even more challenging to find the optimal combination of these factors without the use of a suitable mathematical model.

In this paper, we discuss a novel systems approach to building a model for simulating the spread of COVID-19. The model, MIMANSA, divides an individual’s in-person social interactions into three areas, namely home, workplace, and public places. The model tracks the in-person interactions and follows the virus spread. When a new silent carrier is created, the model automatically expands and builds a new layer in the network.

MIMANSA has four control mechanisms, namely the exposure, infection rate, lockdown, and quarantining. MIMANSA differentiates between virus-infected patients, silent carriers, and healthy carriers. It can consider variations in virus activity levels of asymptomatic patients, varying the exposure to the virus, and varying the infection rate depending on the person’s immunity. MIMANSA can simulate scenarios to study the impact of many different conditions simultaneously. It could assist public health officials in complex decision making, enable scientists in projecting the SARS-CoV-2 virus spread and aid hospital administrators in the management of beds and equipment.

MIMANSA is trained and validated using the data from the USA and India. Our results show that MIMANSA forecasts the number of COVID-19 cases in the USA, and India within a 3% margin of error.

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  1. Version published to 10.1101/2020.05.12.20099291v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.05.12.20099291: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

    About SciScore

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  3. Version published to 10.1101/2020.05.12.20099291v1 on medRxiv