Progression of COVID-19 in Indian States - Forecasting Endpoints Using SIR and Logistic Growth Models

  1. Bhoomika Malhotra
  2. Vishesh Kashyap

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Abstract

COVID-19 has led to the most widespread public health crisis in recent history. The first case of the disease was detected in India on 31 January 2019, and confirmed cases stand at 74,281 as of 13 May 2020. Mathematical modeling can be utilized to forecast the final numbers as well as the endpoint of the disease in India and its states, as well as assess the impact of social distancing measures. In the present work, the Susceptible-Infected-Recovered (SIR) model and the Logistic Growth model have been implemented to predict the endpoint of COVID-19 in India as well as three states accounting for over 55% of the total cases – Maharashtra, Gujarat and Delhi. The results using the SIR model indicate that the disease will reach an endpoint in India on 12 September, while Maharashtra, Gujarat and Delhi will reach endpoints on 20 August, 30 July and 9 September respectively. Using the Logistic Regression model, the endpoint for India is predicted on 23 July, while that for Maharashtra, Gujarat and Delhi is 5 July, 23 June and 10 August respectively. It is also observed that the case numbers predicted by the SIR model are greater than those for the Logistic Growth model in each case. The results suggest that the lockdown enacted by the Government of India has had only a moderate impact on the spread of COVID-19, and emphasize the need for firm implementation of social distancing guidelines.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.15.20103028: (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

    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: We detected the following sentences addressing limitations in the study:
    As with any study involving mathematical modeling, the current work involves caveats and limitations. The primary elements of the study are the data, the mathematical models applied and the numerical code, the sources and explanations for all of which have been included. However, future incidences such as the easing of social distancing measures may lead to a further increase in the number of cases than forecast. Additionally, factors that might increase the number of cases such as testing capacity have not been considered. While using the current forecast for a qualitative and quantitative understanding of the progress of COVID-19 in India, it would hence be prudent to consider these, as well as a number of other economics, social, demographic and medical factors.

    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.

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  2. Version published to 10.1101/2020.05.15.20103028v1 on medRxiv