Analysis of SIR-Network Model on COVID-19 with respect to its impact on West Bengal in India

  1. Debnarayan Khatua
  2. Debashree Guha
  3. Samarjit Kar
  4. Anupam De
  5. Eshan Samanta

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Abstract

The recent global pandemic of SARS-CoV-2 (COVID-19) disease has prompted many researchers to formulate lock-down and quarantine scenarios while the main concern of the researchers is to model the spread and the possible duration of the COVID-19 infections and also research on how long this is going to last. It seems that most of the researchers have recognized lock-down as one of the major impact factors in their models. As a result of this in the absence of lock-down the models formulated would not contribute much significant results. Hence, in this work we decide to formulate a mathematical model which would be able to predict the spread and also the possible duration of the pandemic, by considering both partial lock-down and the corresponding unlocking situations. Employing SIR-network models and taking the various districts of highly populated areas of West Bengal, India as the nodes or vertices we attempt to model the spread and duration of the pandemic during both partial lock-down and unlock phases but separately. We consider the populations where the locally present people and the people who have undergone migration of some shorts are well mixed together. In the network that we have provided the pointed edges refer to the migrating workers that is those that move away from their regular habitats in want of work. We use this research to study not only the trends that are associated with COVID-19 outbreaks, but also to study the impacts of the Government policies and the improvisation of medical facilities on this outbreak in West Bengal. At the end, we attempt to throw light upon the crisis that the economy of the state may have to go through separately on partial lock-down and unlocking scenarios.

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    SciScore for 10.1101/2020.08.05.20169037: (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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.08.05.20169037v1 on medRxiv