A Global Scale Estimate of Novel Coronavirus (COVID-19) Cases Using Extreme Value Distributions

  1. M. Aadhityaa
  2. K. S. Kasiviswanathan
  3. Idhayachandhiran Ilampooranan
  4. B. Soundharajan
  5. M. Balamurugan
  6. Jianxun He

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Abstract

The COVID-19 pandemic has created a global crisis and the governments are fighting rigorously to control the spread by imposing intervention measures and increasing the medical facilities. In order to tackle the crisis effectively we need to know the trajectories of number of the people infected (i.e. confirmed cases). Such information is crucial to government agencies for developing effective preparedness plans and strategies. We used a statistical modeling approach – extreme value distributions (EVDs) for projecting the future confirmed cases on a global scale. Using the 69 days data (from January 22, 2020 to March 30, 2020), the EVDs model predicted the number of confirmed cases from March 31, 2020 to April 9, 2020 (validation period) with an absolute percentage error < 15 % and then projected the number of confirmed cases until the end of June 2020. Also, we have quantified the uncertainty in the future projections due to the delay in reporting of the confirmed cases on a global scale. Based on the projections, we found that total confirmed cases would reach around 11.4 million globally by the end of June 2020.The USA may have 2.9 million number of confirmed cases followed by Spain-1.52 million and Italy-1.28 million.

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    SciScore for 10.1101/2020.04.17.20069500: (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:
    Assumptions, limitations, and quantification of uncertainty: The uncertainties associated with the epidemic modeling studies are due to various reasons such as (i) availability, length, and correctness of the data [31], (ii) model parameter values: either assumed or estimated or adopted from previous modeling studies (assumption of incubation period and reproduction number) [32, 33] (iii) assumptions or limitations of the model being used. For example, the SEIR model assumes all the population is susceptible to infection. Dynamics transmission model’s assumption that symptomatic individuals are more (50%) susceptible to infection than asymptomatic individuals [32]. Assumptions while conceptualizing the non-pharmaceutical interventions such as duration of stay at home during isolation, percent contact reduction in workplaces, impact of non-pharmaceutical interventions are constant with time and same across all countries etc. [32]. As mentioned earlier, the uncertainty in the projected confirmed cases were quantified only based on the Rd, and the fold increase estimated from each data point for a fixed lag length will have inherent variability (Fig. 8). This is mainly because the data is highly random and during the initial phase of pandemic the effect of delay will be less and gradually increase with time. Although the mean estimate as reported in the previous section is a good choice to quantify the delay effect in the projection, ignoring the uncertainty might under predict ...

    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.
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    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.04.17.20069500v1 on medRxiv