An environmental scan of one health preparedness and response: the case of the Covid-19 pandemic in Rwanda
Bibliographic record
Abstract
BACKGROUND: Over the past decade, 70% of new and re-emerging infectious disease outbreaks in East Africa have originated from the Congo Basin where Rwanda is located. To respond to these increasing risks of disastrous outbreaks, the government began integrating One Health (OH) into its infectious disease response systems in 2011 to strengthen its preparedness and contain outbreaks. The strong performance of Rwanda in responding to the on-going COVID-19 pandemic makes it an excellent example to understand how the structure and principles of OH were applied during this unprecedented situation. METHODS: A rapid environmental scan of published and grey literature was conducted between August and December 2020, to assess Rwanda's OH structure and its response to the COVID-19 pandemic. In total, 132 documents including official government documents, published research, newspaper articles, and policies were analysed using thematic analysis. RESULTS: Rwanda's OH structure consists of multidisciplinary teams from sectors responsible for human, animal, and environmental health. The country has developed OH strategic plans and policies outlining its response to zoonotic infections, integrated OH into university curricula to develop a OH workforce, developed multidisciplinary rapid response teams, and created decentralized laboratories in the animal and human health sectors to strengthen surveillance. To address COVID-19, the country created a preparedness and response plan before its onset, and a multisectoral joint task force was set up to coordinate the response to the pandemic. By leveraging its OH structure, Rwanda was able to rapidly implement a OH-informed response to COVID-19. CONCLUSION: Rwanda's integration of OH into its response systems to infectious diseases and to COVID-19 demonstrates the importance of applying OH principles into the governance of infectious diseases at all levels. Rwanda exemplifies how preparedness and response to outbreaks and pandemics can be strengthened through multisectoral collaboration mechanisms. We do expect limitations in our findings due to the rapid nature of our environmental scan meant to inform the COVID-19 policy response and would encourage a full situational analysis of OH in Rwanda's Coronavirus response.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.026 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".