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Publications

Journal Article Epublish

PubMed: 38993699 DOI: 10.3389/fpubh.2024.1406363

The determinants of COVID-19 case reporting across Africa.

Front Public Health 12:1406363

Authors: Qing Han, Ghislain Rutayisire, Maxime Descartes Mbogning Fonkou, Wisdom Stallone Avusuglo, Ali Ahmadi, Ali Asgary, James Orbinski, Jianhong Wu, Jude Dzevela Kong

Affiliations

  • Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, ON, Canada. Department of Mathematics and Statistics, York University, Toronto, ON, Canada.
  • Department of Mathematics and Statistics, York University, Toronto, ON, Canada.
  • Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, ON, Canada.
  • Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran.
  • Disaster and Emergency Management, School of Administrative Studies, York University, Toronto, ON, Canada.
  • Dahdaleh Institute for Global Health Research, York University, Toronto, ON, Canada.
  • Artificial Intelligence & Mathematical Modeling Lab (AIMM Lab), Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
  • Department of Mathematics, Bahen Centre for Information Technology, University of Toronto, Toronto, ON, Canada.
  • Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP), Toronto, ON, Canada.

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 case reporting 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 34 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.

Conclusion

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.

MeSH

Incidence, Public Health, Covid-19, Demography