Multi-chain Fudan-CCDC model for COVID-19 in Iran

  1. Hanshuang Pan
  2. Nian Shao
  3. Yue Yan
  4. Xinyue Luo
  5. Ali Ahmadi
  6. Yasin Fadaei
  7. Jin Cheng
  8. Wenbin Chen

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Abstract

Background

COVID-19 has been deeply affecting people’s lives all over the world. It is significant for prevention and control to model the evolution effectively and efficiently.

Methods

We first propose the multi-chain Fudan-CCDC model which is based on the original Fudan-CCDC model to describe the revival of COVID-19 in some countries. Multi-chains are considered as the superposition of distinctive single chains. Parameter identification is carried out by minimizing the penalty function.

Results

From results of numerical simulations, the multi-chain model performs well on data fitting and reasonably interprets the revival phenomena. The band of ±25% fluctuation of simulation results could contain most seemly unsteady increments.

Conclusion

The multi-chain model has better performance on data fitting in revival situations compared with the single-chain model. It is predicted by the three-chain model with data by Apr 21 that the epidemic curve of Iran would level off on round May 10, and the final cumulative confirmed cases would be around 88820. The upper bound of the 95% confidence interval would be around 96000.

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    SciScore for 10.1101/2020.04.22.20075630: (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 unknown parameters were estimated by running the fminsearch, a MATLAB function.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    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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  2. Version published to 10.1101/2020.04.22.20075630v2 on medRxiv
  3. Version published to 10.1101/2020.04.22.20075630v1 on medRxiv