Analysis of the Number of Tests, the Positivity Rate, and Their Dependency Structure during COVID-19 Pandemic

  1. Babak Jamshidi
  2. Hakim Bekrizadeh
  3. Shahriar Jamshidi Zargaran
  4. Mansour Rezaei

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Abstract

Background

Applying recent advances in medical instruments, information technology, and unprecedented data sharing into COVID-19 research revolutionized medical sciences, and causes some unprecedented analyses, discussions, and models.

Methods

Modeling of this dependency is done using four classes of copulas: Clayton, Frank, Gumbel, and FGM. The estimation of the parameters of the copulas is obtained using the maximum likelihood method. To evaluate the goodness of fit of the copulas, we calculate AIC. All computations are conducted on Matlab R2015b, R 4.0.3, Maple 2018a, and EasyFit 5.6, and the plots are created on software Matlab R2015b and R 4.0.3.

Results

As time passes, the number of tests increases, and the positivity rate becomes lower. The epidemic peaks are occasions that violate the stated general rule –due to the early growth of the number of tests. If we divide data of each country into peaks and otherwise, about both of them, the rising number of tests is accompanied by decreasing the positivity rate.

Conclusion

The positivity rate can be considered a representative of the level of the spreading. Approaching zero positivity rate is a good criterion to scale the success of a health care system in fighting against an epidemic. We expect that if the number of tests is great enough, the positivity rate does not depend on the number of tests. Accordingly, the number and accuracy of tests can play a vital role in the quality level of epidemic data.

Key messages

  • -

    In a country, increasing the positivity rate is more representative than increasing the number of tests to warn about an epidemic peak.

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    Approaching zero positivity rate is a good criterion to scale the success of a health care system in fighting against an epidemic.

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    Except for the first half of the epidemic peaks, in a country, the higher number of tests is associated with a lower positivity rate.

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    In countries with high test per million, there is no significant dependency between the number of tests and positivity rate.

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    1. ScreenIT

      SciScore for 10.1101/2021.04.20.21255796: (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:
      The first limitation is the low quality of data for some countries because of the restricted facilities, the low number of tests, and non-organized data collection program. Also, some interpolation and moving average methods were applied to find some missing data regarding the countries of interest and calculating the correlation for the countries with poor data. Out of the twelve countries, Iran, South Africa, Nigeria, Bolivia, and Guatemala have been restricted by the number of tests. The data of Italy, the UAE, and South Korea showed no significant correlation. The lack of dependency is a good criterion to show that there is no shortage of facilities. The highest quality and most significant correlations belong to the USA, India, the UK, and Australia. The present approach using copulas is promising since it allows to take into account a wide range of correlation, frequently observed in medical. In fact, the classical multivariate models cannot reproduce all type of correlations. Moreover, the standard models are limited, especially because the choice of the marginal distributions is restricted. The crucial step in the modeling process is the choice of the copula function, which best fits the data. Further work is needed to choose the best copulas able to reproduce the dependence structure of bivariate medical variables. In clinical trials or medical studies, sample size is often an important consideration and is relatively small. The copula-based methodology overcomes thi...

      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 10.1101/2021.04.20.21255796v1 on medRxiv