Modeling the impact of COVID-19 vaccination in Lebanon: A call to speed-up vaccine roll out
Bibliographic record
Abstract
Abstract Background Amidst a very difficult economic and political situation, and after a large first SARS-CoV-2 wave near the end of 2020, Lebanon launched its vaccination campaign on 14 February 2021. To date, only 6.7% of the population have received at least one dose of the vaccine, raising serious concerns over the speed of vaccine roll-out and its impact in the event of a future surge. Objective Using mathematical modeling, we assessed the short-term impact (by end of 2021) of various vaccine roll-out scenarios on SARS-CoV-2 epidemic course in Lebanon. Results At current immunity levels in the population, estimated by the model at 40% on 15 April 2021, a large epidemic wave is predicted if all social distancing restrictions are gradually eased and variants of concern are introduced. Reaching 80% vaccine coverage by end of 2021 will flatten the epidemic curve and will result in a 37% and 34% decrease in the peak daily numbers of severe/critical disease cases and deaths, respectively; while reaching intermediate coverage of 40% will result in only 10-11% decrease in each. Reaching 80% coverage by end of 2021 will avert 3 times more hospitalizations and deaths over the course of this year compared with 40% coverage. Impact of vaccination was substantially enhanced with rapid scale-up. Reaching 80% vaccine coverage by August would prevent twice as many severe/critical disease cases and deaths than if it were reached by December. Finally, a longer duration over which restrictions are eased resulted in a more favorable impact of vaccination. Conclusion For vaccination to have an impact on the predicted epidemic course and associated disease burden in Lebanon, vaccination has to be rapid and reach high coverage (at least 70%), while sustaining social distancing measures during roll-out. At current vaccination pace, this is unlikely to be achieved. Concerted efforts need to be put to overcome local challenges and substantially scale up vaccination to avoid a surge that the country, with its multiple crises and limited health-care capacity, is largely unprepared for.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".