Modeling COVID-19 Growing Trends to Reveal the Differences in the Effectiveness of Non-Pharmaceutical Interventions among Countries in the World

  1. You Chen
  2. Yubo Feng
  3. Chao Yan
  4. Xinmeng Zhang
  5. Cheng Gao

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Abstract

Objective

We hypothesize that COVID-19 case growth data reveals the efficacy of NPIs. In this study, we conduct a secondary analysis of COVID-19 case growth data to compare the differences in the effectiveness of NPIs among 16 representative countries in the world.

Methods

This study leverages publicly available data to learn patterns of dynamic changes in the reproduction rate for sixteen countries covering Asia, Europe, North America, South America, Australia, and Africa. Furthermore, we model the relationships between the cumulative number of cases and the dynamic reproduction rate to characterize the effectiveness of the NPIs. We learn four levels of NPIs according to their effects in the control of COVID-19 growth and categorize the 16 countries into the corresponding groups.

Results

The dynamic changes of the reproduction rate are learned via linear regression models for all of the studied countries, with the average adjusted R-squared at 0.96 and the 95% confidence interval as [0.94 0.98]. China, South Korea, Argentina, and Australia are at the first level of NPIs, which are the most effective. Japan and Egypt are at the second level of NPIs, and Italy, Germany, France, Netherlands, and Spain, are at the third level. The US and UK have the most inefficient NPIs, and they are at the fourth level of NPIs.

Conclusions

COVID-19 case growth data provides evidence to demonstrate the effectiveness of the NPIs. Understanding the differences in the efficacy of the NPIs among countries in the world can give guidance for emergent public health events.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.22.20075846: (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:
    There are several limitations to this study that we wish to highlight to guide future research in this area. First, the dynamic changes in the reproduction rate may be impacted by many reasons other than the NPIs. If countries have other factors that influence COVID-19 growth, then our approaches to measuring the effectiveness of the NPIs will be limited. Second, we use estimated peaks to categorize the effectiveness of the NPIs into four different levels. Although the estimated cumulative number of cases is consistent with the observed part (high adjusted R-Square), there is potentiality it will derive from the observed one in the future COVID-19 spread. Third, the study aims to quantify the overall effects of NPIs on the case growing trend and did not consider the impact of each NPI, such as school and university close, on the growing trend. Due to limited data information and a lack of approaches, we did not separate each NPIs and consider all of them as a whole in this study. Fourth, the patterns of COVID-19 growing are learned from reported case numbers, which may be underestimated given the shortages or unavailability of test kits in many countries. Findings learned from such data may be biased to the available number of test kits.

    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 10.1101/2020.04.22.20075846v1 on medRxiv