Increased Detection coupled with Social Distancing and Health Capacity Planning Reduce the Burden of COVID-19 Cases and Fatalities: A Proof of Concept Study using a Stochastic Computational Simulation Model

  1. Pramit Ghosh
  2. Salah Basheer
  3. Sandip Paul
  4. Partha Chakrabarti
  5. Jit Sarkar

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Abstract

Objective

In absence of any vaccine, the Corona Virus Disease 2019 (COVID-19) pandemic is being contained through a non-pharmaceutical measure termed Social Distancing (SD). However, whether SD alone is enough to flatten the epidemic curve is debatable. Using a Stochastic Computational Simulation Model, we investigated the impact of increasing SD, hospital beds and COVID-19 detection rates in preventing COVID-19 cases and fatalities.

Research Design and Methods

The Stochastic Simulation Model was built using the EpiModel package in R. As a proof of concept study, we ran the simulation on Kasaragod, the most affected district in Kerala. We added 3 compartments to the SEIR model to obtain a SEIQHRF (Susceptible-Exposed-Infectious-Quarantined-Hospitalised-Recovered-Fatal) model.

Results

Implementing SD only delayed the appearance of peak prevalence of COVID-19 cases. Doubling of hospital beds couldn’t reduce the fatal cases probably due to its overwhelming number compared to the hospital beds. Increasing detection rates could significantly flatten the curve and reduce the peak prevalence of cases (increasing detection rate by 5 times could reduce case number to half).

Conclusions

An effective strategy to contain the epidemic spread of COVID-19 in India is to increase detection rates in combination with SD measures and increase in hospital beds.

HIGHLIGHTS

  • Increased Detection of COVID-19 cases must accompany Social Distancing and Health Capacity Planning to reduce the burden of cases and fatalities.

  • Interruptive Social Distancing is an effective alternative to continuous Social Distancing.

  • Given the overwhelming burden of COVID-19 fatalities, there is immediate need of co-ordination with the Private Healthcare Sector.

  • COVID-19 cases will be peaking after May, 2020 giving us time for Healthcare Capacity Building in the government and private sector both.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.04.05.20054775: (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
    2.1 The Model and its parameters: The Epidemic Model was built using EpiModel package [10] in R Language for Statistical Computing.
    EpiModel
    suggested: (EpiModel, RRID:SCR_018539)

    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:
    One important limitation of the present study is that the analysis on one geographically confined district may not be implicated to all Indian states and districts with diverse location, population density, availability of healthcare facility, population migration pattern and climatic variations. However, as a proof of concept study we believe our work provides a framework of intervention modalities towards developing policies in mitigating the present epidemic. Moreover, Epidemic Response Planning is needed at district level as inter-district movement become restricted during this period. Our study further suggests that co-ordination of Public and Private Healthcare Sectors would crucial by increasing the hospital beds as well as by supporting medical emergencies. Towards devising an effective strategy to contain the epidemic spread of COVID-19 in Indian context, our study emphasises the critical importance of increasing detection rates in combination with already existing SD measures and increase in hospital beds. Thus, the best strategy to this end is a combination of three levels of interventions at their optimum values.

    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

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  2. Version published to 10.1101/2020.04.05.20054775v1 on medRxiv