Ineffectiveness of international travel restrictions to contain spread of the SARS-CoV-2 Omicron BA.1 variant: a continent-wide laboratory-based observational study from Africa
Bibliographic record
Abstract
Background: In mid-November 2021, the SARS-CoV-2 Omicron BA.1 variant was detected in Southern Africa, prompting international travel restrictions of unclear effectiveness that exacted a substantial economic toll. Methods: Amidst the BA.1 wave, we tested 13,294 COVID-19 patients in 24 African countries between mid-2021 to early 2022 for BA.1 and Delta variants using real-time reverse transcription-PCR tests. The diagnostic precision of the assays was evaluated by high-throughput sequencing in four countries. The observed BA.1 spread was compared to mobility-based mathematical simulations. Findings: By November-December 2021, BA.1 had replaced the Delta variant in all African sub-regions following a South-North gradient, with a median Rt of 2.4 up to 30 days before BA.1 became predominant. PCR-based South-North spread was in agreement with phylogeographic reconstructions relying on 939 SARS-CoV-2 genomes from GISAID. PCR-based reconstructions of country-level BA.1 predominance correlated significantly in time with the emergence of BA.1 genomic sequences on GISAID (p=0.0035, cor=0.70). First BA.1 detections in affluent settings beyond Africa were predicted adequately in time by mobility-based mathematical simulations (p<0.0001). BA.1-infected inbound travelers departing from five continents were identified in five Western countries and one Northern African country by late November/early December 2021, highlighting fast global BA.1 spread aided by international travel. Interpretation: Unilateral travel bans were poorly effective because by the time they came into effect, BA.1 was already widespread in Africa and beyond. PCR-based variant typing combined with mobility-based mathematical modelling can inform rapidly and cost-efficiently on Rt, spread to inform non-pharmaceutical interventions.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".