Africa’s response to the COVID-19 pandemic: A review of the nature of the virus, impacts and implications for preparedness
Bibliographic record
Abstract
Background: COVID-19 continues to wreak havoc in different countries across the world, claiming thousands of lives, increasing morbidity and disrupting lifestyles. The global scientific community is in urgent need of relevant evidence, to understand the challenges and knowledge gaps, as well as the opportunities to contain the spread of the virus. Considering the unique socio-economic, demographic, political, ecological and climatic contexts in Africa, the responses which may prove to be successful in other regions may not be appropriate on the continent. This paper aims to provide insight for scientists, policy makers and international agencies to contain the virus and to mitigate its impact at all levels. Methods: The Affiliates of the African Academy of Sciences (AAS), came together to synthesize the current evidence, identify the challenges and opportunities to enhance the understanding of the disease. We assess the potential impact of this pandemic and the unique challenges of the disease on African nations. We examine the state of Africa’s preparedness and make recommendations for steps needed to win the war against this pandemic and combat potential resurgence. Results: We identified gaps and opportunities among cross-cutting issues which is recommended to be addressed or harnessed in this pandemic. Factors such as the nature of the virus and the opportunities for drug targeting, point of care diagnostics, health surveillance systems, food security, mental health, xenophobia and gender-based violence, shelter for the homeless, water and sanitation, telecommunications challenges, domestic regional coordination and financing. Conclusion: Based on our synthesis of the current evidence, while there are plans for preparedness in several African countries, there are significant limitations. Multi-sectoral efforts from the science, education, medical, technological, communication, business and industry sectors as well as local communities is required in order to win this fight.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".