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The determinants of COVID-19 case reporting across Africa.
Han, Qing; Rutayisire, Ghislain; Mbogning Fonkou, Maxime Descartes; Avusuglo, Wisdom Stallone; Ahmadi, Ali; Asgary, Ali; Orbinski, James; Wu, Jianhong; Kong, Jude Dzevela.
Afiliación
  • Han Q; Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, ON, Canada.
  • Rutayisire G; Department of Mathematics and Statistics, York University, Toronto, ON, Canada.
  • Mbogning Fonkou MD; Department of Mathematics and Statistics, York University, Toronto, ON, Canada.
  • Avusuglo WS; Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, ON, Canada.
  • Ahmadi A; Department of Mathematics and Statistics, York University, Toronto, ON, Canada.
  • Asgary A; Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, ON, Canada.
  • Orbinski J; Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran.
  • Wu J; Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Toronto, ON, Canada.
  • Kong JD; Disaster and Emergency Management, School of Administrative Studies, York University, Toronto, ON, Canada.
Front Public Health ; 12: 1406363, 2024.
Article en En | MEDLINE | ID: mdl-38993699
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: COVID-19 Límite: Humans País/Región como asunto: Africa Idioma: En Revista: Front Public Health Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: COVID-19 Límite: Humans País/Región como asunto: Africa Idioma: En Revista: Front Public Health Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Suiza