Evaluating the number of unvaccinated people needed to exclude to prevent SARS-CoV-2 transmissions
Bibliographic record
Abstract
Abstract Background Vaccine mandates and vaccine passports (VMVP) for SARS-CoV-2 are thought to be a path out of the pandemic by increasing vaccination through coercion and excluding unvaccinated people from different settings because they are viewed as being at significant risk of transmitting SARS-CoV-2. While variants and waning efficacy are relevant, SARS-CoV-2 vaccines reduce the risk of infection, transmission, and severe illness/hospitalization in adults. Thus, higher vaccination levels are beneficial by reducing healthcare system pressures and societal fear. However, the benefits of excluding unvaccinated people are unknown. Methods A method to evaluate the benefits of excluding unvaccinated people to reduce transmissions is described, called the number needed to exclude (NNE). The NNE is analogous to the number needed to treat (NNT=1/ARR), except the absolute risk reduction (ARR) is the baseline transmission risk in the population for a setting (e.g., healthcare). The rationale for the NNE is that exclusion removes all unvaccinated people from a setting, such that the ARR is the baseline transmission risk for that type of setting, which depends on the secondary attack rate (SAR) typically observed in that type of setting and the baseline infection risk in the population. The NNE is the number of unvaccinated people who need to be excluded from a setting to prevent one transmission event from unvaccinated people in that type of setting. The NNE accounts for the transmissibility of the currently dominant Delta (B.1.617.2) variant to estimate the minimum NNE in six types of settings: households, social gatherings, casual close contacts, work/study places, healthcare, and travel/transportation. The NNE can account for future potentially dominant variants (e.g., Omicron, B.1.1.529). To assist societies and policymakers in their decision-making about VMVP, the NNEs were calculated using the current (mid-to-end November 2021) baseline infection risk in many countries. Findings The NNEs suggest that at least 1,000 unvaccinated people likely need to be excluded to prevent one SARS-CoV-2 transmission event in most types of settings for many jurisdictions, notably Australia, California, Canada, China, France, Israel, and others. The NNEs of almost every jurisdiction examined are well within the range of the NNTs of acetylsalicylic acid (ASA) in primary prevention of cardiovascular disease (CVD) (≥ 250 to 333). This is important since ASA is not recommended for primary prevention of CVD because the harms outweigh the benefits. Similarly, the harms of exclusion may outweigh the benefits. These findings depend on the accuracy of the model assumptions and the baseline infection risk estimates. Conclusions Vaccines are beneficial, but the high NNEs suggest that excluding unvaccinated people has negligible benefits for reducing transmissions in many jurisdictions across the globe. This is because unvaccinated people are likely not at significant risk – in absolute terms – of transmitting SARS-CoV-2 to others in most types of settings since current baseline transmission risks are negligible. Consideration of the harms of exclusion is urgently needed, including staffing shortages from losing unvaccinated healthcare workers, unemployment/unemployability, financial hardship for unvaccinated people, and the creation of a class of citizens who are not allowed to fully participate in many areas of society. Registration CRD42021292263 Funding This study received no grant from any funding agency, commercial, or not-for-profit sectors. It has also received no support of any kind from any individual or organization. BH is supported by a personal research grant from the University of Wroclaw within the “Excellence Initiative – Research University” framework and by a scholarship from the Polish Ministry of Education and Science. None of these institutions were involved in this research and did not fund it directly. Competing interests The authors have no competing interests to declare. Ethical approval Not applicable. All the work herein was performed using publicly available data. Data reporting The data used in this work are available at https://tinyurl.com/4m8mm4jh and https://decision-support-tools.com/ .
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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 | 0.020 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".