Enhancing integrated epidemic response mechanisms in humanitarian emergencies: a scoping review and qualitative study
Bibliographic record
Abstract
OBJECTIVES: Epidemics pose significant challenges for fragile health systems, particularly in humanitarian emergencies. Recent responses to epidemics such as cholera in Yemen and Ebola virus disease in the Democratic Republic of the Congo have highlighted the lack of effective and integrated coordination. We review existing global models for addressing large-scale epidemics in humanitarian emergencies, identify gaps and inefficiencies, and propose operational recommendations to enhance response mechanisms. DESIGN: A two-pronged approach was used to identify and critically assess current response coordination frameworks. Using the Arksey and O'Malley framework, a scoping review was undertaken, which was complemented by key informant interviews with humanitarian emergency response experts. The interviews focused on identifying the existing challenges and potential strategies to improve epidemic response in humanitarian contexts. PARTICIPANTS: The scoping review included 51 documents (13 peer-reviewed articles and 38 grey literature documents). We conducted in-depth interviews with 28 respondents representing 17 different agencies and donors. INTERVENTIONS: We focused on two major response architectures: the Incident Management System (IMS) and the cluster system. IMS is an important coordination and response instrument increasingly being used to respond to infectious disease threats. PRIMARY AND SECONDARY OUTCOME MEASURES: Outcome measures of interest included the gaps in the current mechanisms to address infectious disease threats in complex humanitarian emergencies. RESULTS: Unlike the cluster system model, which relies on consensus decision-making, IMS has a command-and-control approach, ensuring rapid decision-making. However, it can also lead to vertical responses that neglect the cross-sectoral and complex needs of affected communities. In addition, we found that the absence of context-specific response coordination mechanisms, with clear roles and responsibilities for involved stakeholders, was a common shortcoming. Fragmented response efforts that sidelined national and local stakeholders and a lack of reliable funding were also identified as important weaknesses. CONCLUSIONS: We recommend the integration of coordination mechanisms into a sufficiently flexible framework that can be adapted to local contexts, while empowering national and local actors and ensuring the continuity of essential humanitarian services. We propose a paradigm shift towards mechanisms that respect humanitarian principles, effectively addressing the epidemic threats while remaining focused on deploying community-centric response efforts.
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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 | 0.045 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".