Now casting and Forecasting of COVID-19 outbreak in the National Capital Region of Delhi

  1. Bharathnag Nagappa
  2. Manikandanesan Sakthivel
  3. Yamini Marimuthu
  4. Aayushi Rastogi
  5. Archana Ramalingam
  6. Shiv Kumar Sarin

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Objectives

The study aimed to estimate the disease burden due to COVID-19 in the scenarios of unchecked spread and with various public health interventions in New Delhi.

Methods

We adopted Susceptible, Exposed, Infected and Recovered (SEIR) model to estimate the course of COVID-19 outbreak in Delhi population and effect of public health intervention on the pandemic. We first estimated the basic reproductive rate (R 0 ) based on the evidence from Wuhan, then ran the model considering no intervention implemented, followed by case isolation, social distancing, and lockdown, each implemented in isolation and in combinations to estimate the number of cases. Markov’s model was used to estimate the number of cases in various clinical scenarios of the disease. Sensitivity analysis conducted to estimate the effect of asymptomatic cases on case based interventions.

Results

Estimated R 0 in Delhi population was 6.18 (range 4.15 – 12.2). Effective reproductive rate (R t ) was least for case isolation (3.5). Lockdown showed highest reduction (28%) in number of prevalent cases on peak day and 22% reduction in patients in need of intensive care unit (ICU). Case isolation and lockdown together resulted in 50% reduction in number of prevalent cases and 42% reduction in patients in need of ICU care. Sensitivity analysis showed that the effect of case isolation was inversely proportionate to the proportion of asymptomatic (hidden) cases.

Conclusions

Interventions should be implemented in combinations of individual and community level interventions to gain better outcome. Identifying and isolation of all cases as early as possible is important to flatten the pandemic curve.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.05.01.20087783: (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:
    The present model also has a few limitations. The populations living in a rapidly developing new metropolis may be highly heterogeneous due to caste, religion or economic power, and if that affects the basic reproductive rate in significant scale, the estimates might vary. However, the NCR is a well-developed and rather stable metropolis and we believe it is relatively less heterogeneous in nature than many two tier towns in the country and thus will have lesser effect on the estimates.[16] Literatures were uncertain about when the infected person starts to shed the virus, and the most commonly found time is 12 hours before symptoms appear.[20] We had assumed that at the end of incubation period, infected person starts to shed the virus which applied for both symptomatic and asymptomatic cases. In summary, interventions implemented in Delhi are time buying interventions to prepare and act to mitigate the effect of pandemic and to make it manageable. Interventions should be implemented in combinations of individual level and community level interventions to gain better outcome. Identifying and isolation of all cases as early as possible is important to flatten the pandemic curve.

    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 10.1101/2020.05.01.20087783v2 on medRxiv
  3. Version published to 10.1101/2020.05.01.20087783v1 on medRxiv