Predictive Analysis for COVID-19 Spread in India by Adaptive Compartmental Model

  1. S. S. Singh
  2. D. K. Mohapatra

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Abstract

The role of mathematical modelling in predicting spread of an epidemic is of vital importance. The purpose of present study is to develop and apply a computational tool for predicting evolution of different epidemiological variables for COVID-19 in India. We propose a dynamic SIRD (Susceptible-Infected-Recovered-Dead) and SEIRD (Susceptible-Exposed-Infected-Recovered-Dead) model for this purpose. In the dynamic model, time dependent infection rate is assumed for estimating evolution of different variables of the model. Parameter estimation of the model is the first step of the analysis which is performed by least square optimization of priori data. In the second step of the analysis, simulation is carried out by using evaluated parameters for prediction of the outbreak. The computational model has been validated against real data for COVID-19 outbreak in Italy. Time to reach peak, peak infected cases and total reported cases were compared with actual data and found to be in very good agreement. Next the model is applied for the case of India and various Indian states to predict different epidemiological parameters. Priori data was taken from the beginning of nation-wide lockdown on 24 March to 6 July. It was found that peak of the outbreak may reach in the month of August-September with maximum 4-5 lakhs active cases at peak. Total number of reported cases all over India would be in between three to five millions. State wise, Maharashtra, Tamilnadu and Delhi would be worst affected.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.08.20148619: (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

    Software and Algorithms
    SentencesResources
    The SIRD and SEIRD simulations were implemented by the odeint function in Python’s SciPy library [20].
    Python’s
    suggested: (PyMVPA, RRID:SCR_006099)
    SciPy
    suggested: (SciPy, RRID:SCR_008058)
    LMFIT module of Python library is used for the nonlinear least-square optimization [21].
    Python
    suggested: (IPython, RRID:SCR_001658)

    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

    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 10.1101/2020.07.08.20148619v1 on medRxiv