A Comparative Study to Find a Suitable Model for an Improved Real-Time Monitoring of The Interventions to Contain COVID-19 Outbreak in The High Incidence States of India

  1. G.S Amrutha
  2. Abhibhav Sharma
  3. Anudeepti Sharma

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Abstract

Background

On March 11, 2020, The World Health Organization (WHO) declared coronavirus disease (COVID-19) as a global pandemic. There emerged a need for reliable models to estimate the imminent incidence and overall assessment of the outbreak, in order to develop effective interventions and control strategies. One such vital metrics for monitoring the transmission trends over time is the time-dependent effective reproduction number ( R t ). R t is an estimate of secondary cases caused by an infected individual at a time t during the outbreak, given that a certain population proportion is already infected. Misestimated R t is particularly concerning when probing the association between the changes in transmission rate and the changes in the implemented policies. In this paper, we substantiate the implementation of the instantaneous reproduction number ( R ins ) method over the conventional method to estimate R t viz case reproduction number ( R ins ), by unmasking the real-time estimation ability of both methodologies using credible datasets.

Materials & Methods

We employed the daily incidence dataset of COVID-19 for India and high incidence states to estimate R ins and R case . We compared the real-time projection obtained through these methods by corroborating those states that are containing high number of COVID-19 cases and are conducting high and efficient COVID-19 testing. The R ins and R case were estimated using R0 and EpiEstim packages respectively in R software 4.0.0.

Results

Although, both the R ins and R case . for the selected states were higher during the lockdown phases (March 25 - June 1, 2020) and subsequently stabilizes co-equally during the unlock phase (June 1-August 23, 2020), R ins demonstrated variations in accordance with the interventions while R case . remained generalized and under- & overestimated. A larger difference in R ins and R case . estimates was also observed for states that are conducting high testing.

Conclusion

Of the two methods, R ins elucidated a better real-time progression of the COVID-19 outbreak conceptually and empirically, than that of R case . However, we also suggest considering the assumptions corroborated in the implementations which may result in misleading conclusions in the real world.

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    SciScore for 10.1101/2020.09.14.20190447: (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).

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  2. Version published to 10.1101/2020.09.14.20190447v1 on medRxiv