Predictive Analysis of COVID-19 Spread in Sri Lanka using an Adaptive Compartmental Model: Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) Model

  1. R. M. Nayani Umesha Rajapaksha
  2. Millawage Supun Dilara Wijesinghe
  3. Toms K. Thomas
  4. Sujith P. Jayasooriya
  5. B. M. W. Indika Gunawardana
  6. W. M. Prasad Chathuranga Weerasinghe
  7. Shalini Bhakta
  8. Yibeltal Assefa

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Abstract

Novel Corona Virus (COVID-19) is still spreading throughout the world despite various degrees of movement restrictions and the availability of multiple safe and effective vaccines. Modelling in predicting the spread of an epidemic is important for health planning and policies. This study aimed to apply a dynamic Susceptible-Exposed-Infected-Recovered-Deaths (SEIRD) model and simulated it under a range of epidemic conditions using python programme language. The predictions were based on different scenarios from without any preventive measures to several different preventive measures under R 0 of 4. The model shows that more weight to personal protection can halt the spread of transmission followed by the closure of public places and interprovincial movement restriction. Results after simulating various scenarios indicate that disregarding personal protective measures can have devastating effects on the local population. Strict adherence, maintaining and monitoring of self-preventive measures are vital towards minimizing the death toll from COVID-19.

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  1. Version published to 10.1101/2021.08.09.21261819v2 on medRxiv
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    SciScore for 10.1101/2021.08.09.21261819: (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
    Python programming language was used for the analysis.
    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: We detected the following sentences addressing limitations in the study:
    Furthermore, one of the significant limitations of the model is that it does not include the natural death and birth rates assuming those are constant 4,20. In addition, during the COVID-19 pandemic, there are broad variations in estimations of Case Fatality Rate (CFR) that may be misleading. Countries may be more or less likely to detect and report all COVID-19 deaths. Furthermore, they may be using different case definitions and testing strategies or counting cases differently. Variations in CFR also may be explained in part by the way time lags are handled. Differing quality of care or interventions being introduced at different stages of the illness also may play a role. Finally, the profile of patients may vary between countries 15. The proposed model uses the predictors as given in the parameter table under the methodology section. The model was internally validated using the parameters available in the previous studies in the underpinning literature. As with any modelling approach, our findings relate to the assumptions and inputs of the model which lead to a major limitation. The assumptions with the greatest potential effect on our findings are the structural assumptions of a compartmental epidemiological model4. Furthermore, the predictive capability of the tool is highly dependent on several preliminary data for parameter estimation. This dependence may lead to data misinterpretation, especially considering the SIR model. Notably, an essential parameter in epidemic...

    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.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2021.08.09.21261819v1 on medRxiv