The Determinants of Under-Estimation of Covid-19 Cases Across Africa

17 Pages Posted: 8 Apr 2024

See all articles by Qing Han

Qing Han

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Laboratory for Industrial and Applied Mathematics (LIAM), York University

Ghislain Rutayisire

York University

Maxime Descartes Mbogning Fonkou

University Grenoble Alpes

Wisdom Avusuglo

Africa-Canada Artificial Intelligence and Data Innovation Consortium; Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Laboratory for Industrial and Applied Mathematics (LIAM), York University

Ali Ahmadi

Africa-Canada Artificial Intelligence and Data Innovation Consortium; K.N. Toosi University, Faculty of Computer Engineering

Ali Asgary

York University

James Orbinski

York University

Jianhong Wu

York University - Laboratory for Industrial and Applied Mathematics; Africa-Canada Artificial Intelligence and Data Innovation Consortium

Jude Dzevela Kong

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC); University of Toronto

Date Written: March 24, 2024

Abstract

Background: According to study on the under-estimation of COVID-19 cases in African countries, the average daily case reporting rate was only 5.37% in the initial phase of the outbreak when there was little or no control measures. In this work, we aimed to identify the determinants of the under-estimation and classify the African countries using the case reporting rates and the significant determinants.

Methods: We used the COVID-19 daily case reporting rate estimated in the previous paper for 54 African countries as the response variable and 33 variables from demographics, socioeconomic, religion, education, and public health categories as the predictors. We adopted a generalized additive model with cubic spline for continuous predictors and linear relationship for categorical predictors to identify the significant covariates. In addition, we performed Hierarchical Clustering on Principal Components (HCPC) analysis on the reporting rates and significant continuous covariates of all countries.

Results: 21 covariates were identified as significantly associated with COVID-19 case detection: total population, urban population, median age, life expectancy, GDP, democracy index, corruption, voice accountability, social media, internet filtering, air transport, human development index, literacy, Islam population, number of physicians, number of nurses, global health security, malaria incidence, diabetes incidence, lower respiratory and cardiovascular diseases prevalence. HCPC resulted in three major clusters for the 54 African countries: northern, southern and central essentially, with the northern having the best early case detection, followed by the southern and the central.

Conclusions: Overall, northern and southern Africa had better early COVID-19 case identification compared to the central. There are a number of demographics, socioeconomic, public health factors that exhibited significant association with the early case detection.

Note:

Funding Information: This research is funded by Canada’s International Development Research Centre (IDRC) (Grant No. 109981). JDK acknowledges support from NSERC Discovery Grant (Grant No. RGPIN-2022-04559), NSERC Discovery Launch Supplement (Grant No: DGECR-2022-00454) and New Frontier in Research Fund- Exploratory (Grant No. NFRFE-2021-00879).

Conflict of Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Keywords: under-estimation, COVID-19, Africa, determinants of under-estimation, generalized additive model, hierarchical clustering on principal component analysis

Suggested Citation

Han, Qing and Rutayisire, Ghislain and Mbogning Fonkou, Maxime Descartes and Avusuglo, Wisdom and Ahmadi, Ali and Asgary, Ali and Orbinski, James and Wu, Jianhong and Kong, Jude Dzevela, The Determinants of Under-Estimation of Covid-19 Cases Across Africa (March 24, 2024). Available at SSRN: https://ssrn.com/abstract=4771127 or http://dx.doi.org/10.2139/ssrn.4771127

Qing Han

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Laboratory for Industrial and Applied Mathematics (LIAM), York University ( email )

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Ghislain Rutayisire

York University ( email )

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Maxime Descartes Mbogning Fonkou

University Grenoble Alpes ( email )

151 Rue des Universités
Saint-Martin-d'Hères, 38400
France

Wisdom Avusuglo

Africa-Canada Artificial Intelligence and Data Innovation Consortium ( email )

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Laboratory for Industrial and Applied Mathematics (LIAM), York University ( email )

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Ali Ahmadi

Africa-Canada Artificial Intelligence and Data Innovation Consortium ( email )

K.N. Toosi University, Faculty of Computer Engineering ( email )

Tehran
Iran

Ali Asgary

York University ( email )

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

James Orbinski

York University

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Jianhong Wu

York University - Laboratory for Industrial and Applied Mathematics ( email )

Canada

Africa-Canada Artificial Intelligence and Data Innovation Consortium ( email )

Jude Dzevela Kong (Contact Author)

Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) ( email )

University of Toronto
Toronto, Ontario M5R 0A3
Canada

University of Toronto ( email )

105 St George Street
Toronto, Ontario M5S 3G8
Canada

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