1. Introduction

OJS

Open Journal of Statistics

2161-718X

Scientific Research Publishing

10.4236/ojs.2021.112014

OJS-107620

Articles

Physics&Mathematics

Predictors of the Aggregate of COVID-19 Cases and Its Case-Fatality: A Global Investigation Involving 120 Countries

Sarah

Al-Gahtani

¹Mohamed

Shoukri

²^*Maha

Al-Eid

Department of Biostatistics, Epidemiology and Scientific Computing, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia

Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, University of Western Ontario, London Ontario, Canada

Department of Internal Medicine, King Fahd Medical City, Riyadh, Saudi Arabia

08032021

11022592772, February 20216, March 2021 9, March 2021

2014

This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/

Objective : Since the identification of COVID-19 in December 2019 as a pandemic, over 4500 research papers were published with the term “COVID-19” contained in its title. Many of these reports on the COVID-19 pandemic suggested that the coronavirus was associated with more serious chronic diseases and mortality particularly in patients with chronic diseases regardless of country and age. Therefore, there is a need to understand how common comorbidities and other factors are associated with the risk of death due to COVID-19 infection. Our investigation aims at exploring this relationship. Specifically, our analysis aimed to explore the relationship between the total number of COVID-19 cases and mortality associated with COVID-19 infection accounting for other risk factors. Methods: Due to the presence of over dispersion, the Negative Binomial Regression is used to model the aggregate number of COVID-19 cases. Case-fatality associated with this infection is modeled as an outcome variable using machine learning predictive multivariable regression. The data we used are the COVID-19 cases and associated deaths from the start of the pandemic up to December 02-2020, the day Pfizer was granted approval for their new COVID-19 vaccine. Results: Our analysis found significant regional variation in case fatality. Moreover, the aggregate number of cases had several risk factors including chronic kidney disease, population density and the percentage of gross domestic product spent on healthcare. The Conclusions: There are important regional variations in COVID-19 case fatality. We identified three factors to be significantly correlated with case fatality .

Intraclass Correlation Coefficient Hierarchical Data Structure Negative Binomial Regression Data Splitting Mixed Effects Linear Regression Model

1. Introduction

Coronavirus disease 2019 (COVID-19) is now a considered a pandemic by the World Health Organization. The main objective of this study is to report on the association between regional case fatality of COVID-19, kidney diseases mortality, diabetes, population density and the Gross Domestic Product (GPD). This cross-sectional historical data was constructed by combining data from several sources [1] - [6].

The above sited data sources were accessed on December 2-2020. We included in the study the cumulative number of COVID-19 cases and the associated death counts by country as of December 2-2020. We excluded countries that had cumulative count less than 10,000 cases. The data base has 120 countries, and we divided them into regions according to the classification given in data source number 2, resulting in 15 regions. This classification is shown in Table 1. This table has in the first column the names of the countries, the second column is the name of the region they belong to, within brackets, the number of countries in that region, and the third column is a code given to each region.

Basically, we have two outcome variables of interest: 1) The aggregate number of cases per country over the period ending December 2-2020 (AC). 2) The COVID-19 Case Fatality (CF). This is calculated as:

AC = Count of COVID-19 cases analyzed at the regional level.

CF = Count of deaths attributed to COVID-19/(Count of COVID-19 cases) × 100,000.

This paper has three objectives. Firstly, we quantify the degree between regions variation in both AC and CF. The second objective is to identify the important factors associated with AC and CF. We use machine learning algorithm to build a regression models with the entire data set divided into learning set and validation test to quantify the predictive accuracy of the constructed models.

2. Selected Risk Factors2.1. Chronic Kidney Diseases

Chronic Kidney Disease (CKD) is an important contributor to morbidity and mortality from noncommunicable diseases, and this disease should be actively addressed to meet the UN’s Sustainable Development Goal target to reduce premature mortality from non-communicable diseases by a third by 2030 [2]. We extracted the CKD prevalence and the associated mortality from the countries listed in table. The rationale was that there are many published articles

Table 1 Countries and the corresponding Regional classification as given in https://doi.org/10.1016/s0140-6736(20)30045-3

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