Israel’s rapid rollout of vaccinations for COVID-19
Bibliographic record
Abstract
As of the end of 2020, the State of Israel, with a population of 9.3 million, had administered more COVID-19 vaccine doses than all countries aside from China, the US, and the UK. Moreover, Israel had administered almost 11.0 doses per 100 population, while the next highest rates were 3.5 (in Bahrain) and 1.4 (in the United Kingdom). All other countries had administered less than 1 dose per 100 population.While Israel's rollout of COVID-19 vaccinations was not problem-free, its initial phase had clearly been rapid and effective. A large number of factors contributed to this early success, and they can be divided into three major groups.The first group of factors consists of long-standing characteristics of Israel which are extrinsic to health care. They include: Israel's small size (in terms of both area and population), a relatively young population, relatively warm weather in December 2020, a centralized national system of government, and well-developed infrastructure for implementing prompt responses to large-scale national emergencies.The second group of factors are also long-standing, but they are health-system specific. They include: the organizational, IT and logistical capacities of Israel's community-based health care providers, the availability of a cadre of well-trained, salaried, community-based nurses who are directly employed by those providers, a tradition of effective cooperation between government, health plans, hospitals, and emergency care providers - particularly during national emergencies; and support tools and decisionmaking frameworks to support vaccination campaigns.The third group consists of factors that are more recent and are specific to the COVID-19 vaccination effort. They include: the mobilization of special government funding for vaccine purchase and distribution, timely contracting for a large amount of vaccines relative to Israel's population, the use of simple, clear and easily implementable criteria for determining who had priority for receiving vaccines in the early phases of the distribution process, a creative technical response that addressed the demanding cold storage requirements of the Pfizer-BioNTech COVID-19 vaccine, and well-tailored outreach efforts to encourage Israelis to sign up for vaccinations and then show up to get vaccinated.While many of these facilitating factors are not unique to Israel, part of what made the Israeli rollout successful was its combination of facilitating factors (as opposed to each factor being unique separately) and the synergies it created among them. Moreover, some high-income countries (including the US, the UK, and Canada) are lacking several of these facilitating factors, apparently contributing to the slower pace of the rollout in those countries.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".