Lessons learnt in the response to COVID-19 in Mozambique: enabling readiness for the next pandemic
Bibliographic record
Abstract
Introduction: The coronavirus disease 2019 (COVID-19) has led to a dramatic loss of human lives worldwide and caused economic and social disruptions. The risk of another pandemic occurring is ever-present requiring countries to document factors that influenced the response to COVID-19 to guide the response to future pandemics. This study documents lessons learnt from Mozambique's COVID-19 response, considering the perspectives of various stakeholders and examining different components of the response. Methods: We used a qualitative phenomenology research design and collected data using in-depth interviews. We used purposive sampling by selecting institutions with relevant experience and knowledge to inform the study objectives. We also used snowballing techniques by asking respondents for other potential informants. We interviewed 19 individuals indicated by the representatives of the institutions selected for the study. The institutions were mostly based in Maputo city, the country's capital. Participants were asked about their role in the organization; responsibility in vaccine distribution and delivery in Mozambique; their opinion on what worked well in the country's response to COVID-19, and what could be improved as preparation to future pandemics. Data was coded using a computer-assisted qualitative data analysis software Maxqda 2020 and analyzed using a deductive thematic approach. A validation meeting was held, in which research participants were asked to check the accuracy of the results and interpretations. Results: Key drivers of the COVID-19 response were strong leadership; a clear plan and strategies; a functional coordination mechanism; the use of evidence to make decisions; a careful consideration of priority groups; investments in the supply chain and surveillance systems; the utilization of pre-existing vaccination structures; and partnership between the government and several stakeholders. There is room for improvement including the development of a clear budget, a communication plan, creation of an emergency fund, accountability in the use of funds, decentralization of surveillance infrastructure and representation of vulnerable, marginalized, and hard-to-reach populations in the design and implementation of pandemic response. Conclusion: The lessons learned from the COVID-19 response in Mozambique, which could be considered when preparing for an effective and equitable response to future pandemics, are in essence the following: there should be government leadership, a response plan, adequate resources, use of data to inform decisions, constant vigilance, a prompt response, involvement of all stakeholders and documentation of actions for continuous learning. These lessons could improve pandemic preparedness nationally and globally.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".