Adding Structure to Statistics: A Study on COVID-19 Dynamics in India

  1. Arundhati Dixit
  2. Sarthak Vishnoi
  3. Sourabh Bikas Paul

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Abstract

This study pertains to COVID-19 in India, and begins by uncovering the statistical relationship between three time series-number of cases, number of deaths, and number of tests each day, using structural vector autoregression. Further, impulse responses of the before-mentioned series are studied. Effect of temperature and humidity on number of cases is analysed using the fixed effects model on city-level panel data. The next model utilises exponential smoothing for forecasting and conjecture for identifying peak specific to this data is presented. Lastly, multiple iterations of compartmental modelling, possible scenarios, and effect of underlying assumptions is analysed. The models are used to forecast number of cases (regression for short term and epidemiological for long term). In the end, policy implications of different modelling exercises and ways to check the implication for policy planning are discussed.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.26.20113522: (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: Thank you for sharing your code and data.

    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

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.05.26.20113522 on medRxiv