Impact of social distancing measures on the daily number of new COVID-19 cases in Côte d’Ivoire: a retrospective cohort study
Bibliographic record
Abstract
Abstract Introduction Côte d’Ivoire is facing a second wave of the novel coronavirus disease 2019 (COVID-19). While social distancing measures (SDM) may be an option to address this wave, SDM may be devastating, especially if they have a minimal impact on the spread of COVID-19, given the other measures in place. Methods We conducted a cohort study involving cases that had occurred as at June 30, 2020. We used data from the Government’s situation reports. We established three study periods, which correspond to the implementation and easing of SDM, including a 10-day delay for test results: (1) the SDM (March 11 - May 24), (2) the no SDM (May 25 - June 21), and (3) the pseudo SDM (June 22 - July 10) periods. We compared the incidence rate during these periods using Poisson regression, with sex, age, and the average daily number of tests as covariates. Results As at July 10, there were 12,052 cases. The incidence rate was 100% higher during period 2 compared to period 1 (incidence rate ratio = 2.05, 95% confidence interval: 1.75-2.41) and 25% lower during period 3 compared to period 2 (0.75 [0.66-0.86]). Conclusions The easing and subsequent reinforcement of SDM had a significant impact on the spread of COVID-19 in Côte d’Ivoire. The other mitigation measures either did not compensate for the easing of the SDM during the no SDM period or were not fully effective throughout the study periods; they should be strengthened before the SDM are reimplemented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".