Analysis of COVID-19 burden, epidemiology and mitigation strategies in Muslim majority countries
Bibliographic record
Abstract
BACKGROUND: Muslim majority countries have experienced a considerable burden of COVID-19 infection. However, there has been a relative lack of research comparing COVID-19 outbreaks and responses between Muslim-majority countries. AIMS: This study aimed to analyse COVID-19 burden, epidemiology and mitigation strategies in Muslim-majority countries. METHODS: We use a mixed-methods approach to describe the course of the COVID-19 pandemic throughout the Islamic world, highlight the range of non-pharmaceutical interventions used and the speed with which they were implemented, and investigate reasons behind the differing responses between Muslim-majority countries. The number of cases and deaths per million population, and the mean time taken to implement a range of policies, were compared across the Islamic world. Cases per million population and the mean estimated doubling time for cases was compared between Muslim- majority countries on the basis of governance systems, rapidity of institution of mitigation strategies and conflict groups. We also evaluated pushback to implementation of measures within MMCs, especially from religious quarters. RESULTS: Non-democratic regimes had much shorter doubling time of cases compared to functional democratic Muslim- majority countries (mean 33.9 versus 66.5 days, P = 0.002) and a significantly greater proportion of countries appeared to have flattened the curve by 1 June 2020 (43.8% versus 12.5%, P < 0.03). The doubling time was also significantly greater among countries who implemented lockdown and mitigation measures early (66.7 versus 16.7 days, P < 0.003). CONCLUSION: Our analysis indicates wide diversity in the COVID-19 response across Muslim majority countries with clear indication that functional democracies were able to contain the epidemic significantly better than nondemocratic regimes. Future analysis should focus on determination of sub-national differentials and risks as well as targeting of 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".