Breaking down of healthcare system: Mathematical modelling for controlling the novel coronavirus (2019-nCoV) outbreak in Wuhan, China

  1. Wai-Kit Ming
  2. Jian Huang
  3. Casper J. P. Zhang

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Abstract

Background

A novel coronavirus pneumonia initially identified in Wuhan, China and provisionally named 2019-nCoV has surged in the public. In anticipation of substantial burdens on healthcare system following this human-to-human spread, we aim to scrutinise the currently available information and evaluate the burden of healthcare systems during this outbreak in Wuhan.

Methods and Findings

We applied a modified SIR model to project the actual number of infected cases and the specific burdens on isolation wards and intensive care units (ICU), given the scenarios of different diagnosis rates as well as different public health intervention efficacy. Our estimates suggest, assuming 50% diagnosis rate if no public health interventions were implemented, that the actual number of infected cases could be much higher than the reported, with estimated 88,075 cases (as of 31 st January, 2020), and projected burdens on isolation wards and ICU would be 34,786 and 9,346 respectively The estimated burdens on healthcare system could be largely reduced if at least 70% efficacy of public health intervention is achieved.

Conclusion

The health system burdens arising from the actual number of cases infected by the novel coronavirus appear to be considerable if no effective public health interventions were implemented. This calls for continuation of implemented anti-transmission measures (e.g., closure of schools and facilities, suspension of public transport, lockdown of city) and further effective large-scale interventions spanning all subgroups of populations (e.g., universal facemask wear) aiming at obtaining overall efficacy with at least 70% to ensure the functioning of and to avoid the breakdown of health system.

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    SciScore for 10.1101/2020.01.27.922443: (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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

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    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
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    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.01.27.922443v2 on bioRxiv
  3. Version published to 10.1101/2020.01.27.922443v1 on bioRxiv