Detection of transmission change points during unlock-3 and unlock-4 measures controlling COVID-19 in India

  1. Manisha Mandal
  2. Shyamapada Mandal

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Abstract

Objective: To evaluate the efficiency of unlock-3 and unlock-4 measure related to COVID-19 transmission change points in India, for projecting the infected population, to help in prospective planning of suitable measures related to future interventions and lifting of restrictions so that the economic settings are not damaged beyond repair. Methods: The SIR model and Bayesian approach combined with Monte Carlo Markov algorithms were applied on the Indian COVID-19 daily new infected cases from 1 August 2020 to 30 September 2020. The effectiveness of unlock-3 and unlock-4 measure were quantified as the change in both effective transmission rates and the basic reproduction number (R0). Results: The study demonstrated that the COVID-19 epidemic declined after implementing unlock-4 measure and the identified change-points were consistent with the timelines of announced unlock-3 and unlock-4 measure, on 1 August 2020 and 1 September 2020, respectively. Conclusions: Changes in the transmission rates with 100% reduction as well as the R0 attaining 1 during unlock-3 and unlock-4 indicated that the measures adopted to control and mitigate the COVID-19 epidemic in India were effective in flattening and receding the epidemic curve. Keywords: COVID-19 in India, epidemiological parameters, unlock-3 and unlock-4, SIR model, Bayesian inference, Monte Carlo Markov sampling

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  1. Version published to 10.22270/jddt.v11i2.4600
  2. ScreenIT

    SciScore for 10.1101/2020.11.17.20233221: (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 codes for this research article pertaining to the analysis of unlock-3 and unlock-4 situation in India was run on Jupyter notebook using PymC3=3.8 and was based on GitHub repository (Priesemann-Group, 2020), by importing python-based data analysis toolkit (pandas); libraries for working with arrays (numpy), plotting (matplotlib), scientific and technical computing (scipy), and multi-dimensional arrays (theano); modules including Basic date and time types (datetime), System-specific parameters and functions (sys), and Python object serialization (pickle); package for Bayesian statistical modeling and probabilistic machine learning with MCMC and ADVI algorithms (PymC3) (Kucukelbir et al., 2007). 2.2.
    Basic
    suggested: (BaSiC, RRID:SCR_016371)
    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.

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  3. Version published to 10.1101/2020.11.17.20233221v1 on medRxiv