Utilize State Transition Matrix Model to Predict the Novel Corona Virus Infection Peak and Patient Distribution

  1. Ke Wu
  2. Junhua Zheng
  3. Jian Chen

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Abstract

Background

Since December 2019, a pneumonia caused by the 2019 novel coronavirus (2019-nCoV) has broken out in Wuhan, Hubei province, China. The continuous rising of infected cases has imposed overwhelming pressure on public health decision and medical resource allocation in China. We managed to forecast the infection peak time in Hubei province and the severe and critical case distribution.

Methods

We used data resource according to cases reported by the National Health Commission of the People’s Republic of China (Jan 25, 2019, to Feb 28, 2020) as the training set to deduce the arrival of the peak infection time and the number of severe and critical cases in Wuhan on subsequent days. Medical observation, discharge, infected, non-Severe, infected and severe, cure and death data were collected and analyzed. Using this state transition matrix model, we will be able predict when the inflection peak time (the maximum open infection cases) in Hubei Province will occur. Also, we can use this model to predict the patient distribution (severe, non-severe) to better allocate medical resource. Under relative pessimistic scenario, the inflection peak time is April 6-April 14. The numbers of critically ill and critically ill patients will lie between 8300-9800 and 2200-2700, respectively.

Results

In very optimistic scenarios (daily NCC decay rate of −10%), the peak time of open inflection cases will arrive around February 23-February 26. At the same time, there will be a peak in the numbers of severely ill and critically ill patients, between 6800-7200 and 1800-2000, respectively. In a relative optimistic scenario (daily NCC decay rate of −5%), the inflection case peak time will arrive around February 28-March 2. The numbers of critically ill and critically ill patients will lie between 7100-7800 and 1900-2200, respectively. In a relatively pessimistic scenario (daily NCC decay rate of −1%), the inflection peak time does not arrive around the end of March. Estimated time is April 6-April 14. The numbers of critically ill and critically ill patients will lie between 8300-9800 and 2200-2700, respectively. We are using the diagnosis rate, mortality rate, cure rate as the 2/8 data. There should be room for improvement, if these metrics continue to improve. In that case, the peak time will arrive earlier than our estimation. Also, the severe and critical case ratios are likely to decline as the virus becomes less toxic and medical conditions improve. If that happens, the peak numbers will be lower than predicted above.

Conclusion

We can infer that we are still not close to the end of this outbreak and the number of critically ill patients is still climbing. Assisting critical care resources in Hubei province requires the government to consider further tilt, and it is vital to make reasonable management of doctors and medical assistance systems to curb the transmission trend.

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  1. ScreenIT

    SciScore for 10.1101/2020.02.16.20023614: (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:
    Nevertheless, there are still limitations. Primarily, critical or severe cases will be affected by the gradually rational allocation of treatment teams and medical resources, the strengthening of medical power or virus toxicity decreasing. As a result, the peak time might arrive earlier than our estimates. Secondly, the impact of seasonal factors has not been considered and supposing 2019-nCoV is affected by the season, our prediction results may be unreliable. Hence, if these indicators continue to change, the model could have opportunities for improvement. In addition, due to the characteristics of severe and critical cases, there may be a huge impact on factors such as age and complex underlying diseases. Therefore, with the increasing awareness of the severity of the disease and the strengthening of prevention and control in various sectors of the society, various indicators may be reduced. At the same time, because of the instability of the previous indicators, these dynamic changes may affect the predictive efficacy of the model.

    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 10.1101/2020.02.16.20023614 on medRxiv