COVID-19 vaccination coverage and linkages with public willingness to receive vaccination prior to vaccine roll-out: Evidence from Rwanda
Bibliographic record
Abstract
Abstract The rapid development of multiple SARS-CoV-2 vaccines within one year of the virus’s emergence is unprecedented and redefines the timeline for vaccine approval and rollout. Consequently, over 13 billion COVID-19 vaccine doses have been administered worldwide, accounting for ∼70% of the global population. Despite this steadfast scientific achievement, many inequalities exist in vaccine distribution and procurement, particularly in low- and middle-income countries such as those in Africa. This stems from the cost of COVID-19 vaccines, storage and cold-chain challenges, distribution to remote areas, proper personnel training, and so on. In addition to logistical challenges, many developed nations rapidly procured available vaccines, administering second and third doses and leaving many developing nations without the first dose. In this paper, we explore the level of reception to COVID-19 vaccines prior to their availability in Rwanda using a survey-based approach. While several countries reported spikes in vaccine hesitancy generally coinciding with new information, new policies, or newly reported vaccine risks, Rwanda functions as an exemplar for controlling disease burden and educating locals regarding the benefits of vaccination. We show that, even before COVID-19 vaccines were available, many Rwandans (97%) recognized the importance of COVID-19 vaccination and (93%) were willing to receive a COVID-19 vaccine following vaccine availability. Our results underscore the level of preparedness in Rwanda, which rivals and outcompetes many developed nations in terms of vaccination rate (nearing 80% in Rwanda), vaccine acceptance, and local knowledge relating to vaccination. Furthermore, in addition to the whole-of-government coordination as well as tailored delivery approach, previously developed practices relating to vaccination and communication surrounding the Ebola Virus Disease may have compounded the COVID-19 vaccine program in Rwanda, prior to its implementation.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".