Understanding Economic and Health Factors Impacting the Spread of COVID-19 Disease

  1. Aleksandr Farseev
  2. Yu-Yi Chu-Farseeva
  3. Qi Yang
  4. Daron Benjamin Loo

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Abstract

Background

The rapid spread of the Coronavirus 2019 disease (COVID-19) had drastically impacted life all over the world. While some economies are actively recovering from this pestilence, others are experiencing fast and consistent disease spread, compelling governments to impose social distancing measures that have put a halt on routines, especially in densely populated areas.

Objective

Aiming at bringing more light on key economic and population health factors affecting the disease spread, this initial study utilizes a quantitative statistical analysis based on the most recent publicly available COVID-19 datasets.

Methods

We have applied Pearson Correlation Analysis and Clustering Analysis (X-Means Clustering) techniques on the data obtained by combining multiple datasets related to country economics, medical system & health, and COVID-19 - related statistics. The resulting dataset consisted of COVID-19 Case and Mortality Rates, Economic Statistics, and Population Public Health Statistics for 165 countries reported between 22 January 2020 and 28 March 2020. The correlation analysis was conducted with the significance level α of 0.05. The clustering analysis was guided by the value of Bayesian Information Criterion (BIC) with the bin value b = 1.0 and the cutoff factor c = 0.5, and have provided a stable split into four country-level clusters.

Results

The study showed and explained multiple significant relationships between the COVID-19 data and other country-level statistics. We also identified and statistically profiled four major country-level clusters with relation to different aspects of COVID-19 development and country-level economic and health indicators. Specifically, this study identified potential COVID-19 under-reporting traits, as well as various economic factors that impact COVID-19 Diagnosis, Reporting, and Treatment. Based on the country clusters, we also described the four disease development scenarios, which are tightly knit to country-level economic and population health factors. Finally, we highlighted the potential limitation of reporting and measuring COVID-19 and provided recommendations on further in-depth quantitative research.

Conclusions

In this study, we first identified possible COVID-19 reporting issues and biases across different countries and regions. Second, we identified crucial factors affecting the speed of COVID-19 disease spread and provided recommendations on choosing and operating economic and health system factors when analyzing COVID-19 progression. Particularly, we discovered that the political system and compliance with international disease control norms are crucial for effective COVID-19 pandemic cessation. However, the role of some widely-adopted measures, such as GHS Health Index, might have been overestimated in lieu of multiple biases and underreporting challenges. Third, we benchmarked our findings against the widely-adopted Global Health Security (GHS) model and found that the latter might be redundant when measuring and forecasting COVID-19 spread, while its individual components could potentially serve as stronger COVID-19 indicators. Fourth, we discovered four clusters of countries characterized by different COVID-19 development scenarios, highlighting the differences of the disease reporting and progression in different economic and health system settings. Finally, we provided recommendations on sophisticated measures and research approaches to be implemented for effective outbreak measurements, evaluation and forecasting. We have supported the latter recommendations by a preliminary regression analysis based on the our-collected dataset. We believe that our work would encourage further in-depth quantitative research along the direction as well as would be of support to public policy development when addressing the COVID-19 crisis worldwide.

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  1. Version published to 10.1101/2020.04.10.20058222v3 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.04.10.20058222: (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

    Software and Algorithms
    SentencesResources
    Djibouti, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Ghana, Guinea, Guinea-Bissau, Haiti, India, Indonesia, Kenya, Liberia, Madagascar, Mali, Mauritania, Mozambique, Namibia, Nepal, Niger, Nigeria, Pakistan, Philippines, Rwanda, Senegal, Somalia, Sri Lanka, Sudan, Tanzania, Timor-Leste, Togo, Uganda, Zambia, Zimbabwe. Country Cluster 2: Albania, Algeria, Andorra, Antigua And Barbuda, Argentina, Armenia, Azerbaijan, Bahrain, Barbados, Belarus, Belize, Bolivia, Bosnia And Herzegovina, Brazil, Brunei, Bulgaria, Cabo Verde, Chile, Colombia, Costa Rica, Cuba, Cyprus, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Fiji, Georgia, Grenada, Guatemala, Guyana, Holy See, Honduras, Iran, Iraq, Jamaica, Jordan, Kazakhstan, South Korea, Kuwait, Kyrgyzstan, Laos, Lebanon, Libya, Liechtenstein, Malaysia, Maldives, Mauritius, Mexico, Moldova, Mongolia, Montenegro, Morocco, Nicaragua, Oman, Panama, Papua New Guinea, Paraguay, Peru, Qatar, Romania
    Fiji
    suggested: (Fiji, RRID:SCR_002285)

    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.

    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.

    About SciScore

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  3. Version published to 10.1101/2020.04.10.20058222v2 on medRxiv
  4. Version published to 10.1101/2020.04.10.20058222v1 on medRxiv