A Collaborative Approach to the Analysis of the COVID-19 Response in Africa

TL;DR

This paper presents a collaborative capacity-building approach to applying machine learning for COVID-19 analysis in Africa, addressing challenges like limited data, skills, and infrastructure.

Contribution

It introduces a cross-border collaboration model to enhance machine learning capabilities for COVID-19 research in Africa.

Findings

Improved machine learning application in African COVID-19 research

Enhanced cross-border collaboration for capacity building

Potential for scalable solutions in resource-limited settings

Abstract

The COVID-19 crisis has emphasized the need for scientific methods such as machine learning to speed up the discovery of solutions to the pandemic. Harnessing machine learning techniques requires quality data, skilled personnel and advanced compute infrastructure. In Africa, however, machine learning competencies and compute infrastructures are limited. This paper demonstrates a cross-border collaborative capacity building approach to the application of machine learning techniques in discovering answers to COVID-19 questions.