COVID in crisis: The impact of COVID-19 in complex humanitarian emergencies
Bibliographic record
Abstract
Introduction: Two billion people are currently affected by complex humanitarian emergencies (CHEs) resulting from natural disasters and armed conflict. Many have been displaced into crowded camps with poor access to water, sanitation, and health care. Humanitarian response is challenging under these circumstances, raising concern about the impact of COVID-19 on crisis-affected populations. Methods: This article examines CHEs in the Democratic Republic of Congo, Bangladesh, and Yemen, where protracted crises have displaced millions of people. Through use of a conceptual model, we examine barriers and facilitators to an effective COVID-19 response in these complex settings, and explore the future impact of the pandemic on crisis-affected populations. Results: Younger populations, who tend to have less severe COVID-19 disease, and existing response mechanisms, including educational health messaging, may facilitate the COVID-19 response in some CHEs. However, pre-existing chronic illnesses and malnutrition, coupled with poor access to health care and limited water/sanitation infrastructure, may increase COVID-19 infection rates and mortality. Exacerbated health care shortages, food insecurity, interrupted immunizations, increased insecurity, and worsened poverty may have a particularly severe impact. Discussion: A wide-reaching global response, incorporating the voices of marginalized populations, is needed to effectively and equitably respond to this global pandemic. Given the potential future deployment of Canadian troops to CHEs, an understanding of the COVID-19 response and pandemic implications in CHEs is critical for Canadian Armed Forces members.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".