Documenting the development, adoption and pre-ebola implementation of Liberia’s integrated disease surveillance and response (IDSR) strategy
Bibliographic record
Abstract
BACKGROUND: In the immediate aftermath of a 14-year civil conflict that disrupted the health system, Liberia adopted the internationally recommended integrated disease surveillance and response (IDSR) strategy in 2004. Despite this, Liberia was among the three West African countries ravaged by the worst Ebola epidemic in history from 2014 to 2016. This paper describes successes, failures, strengths, and weaknesses in the development, adoption, and implementation of IDSR following the civil war and up until the outbreak of Ebola, from 2004 to early 2014. METHODS: We reviewed 112 official Government documents and peer-reviewed articles and conducted 29 in-depth interviews with key informants from December 2021 to March 2022 to gain perspectives on IDSR in the post-conflict and pre-Ebola era in Liberia. We assessed the core and supportive functions of IDSR, such as notification of priority diseases, confirmation, reporting, analysis, investigation, response, feedback, monitoring, staff training, supervision, communication, and financial resources. Data were triangulated and presented via emerging themes and in-depth accounts to describe the context of IDSR introduction and implementation, and the barriers surrounding it. RESULTS: Despite the adoption of the IDSR framework, Liberia failed to secure the resources-human, logistical, and financial-to support effective implementation over the 10-year period. Documents and interview reports demonstrate numerous challenges prior to Ebola: the surveillance system lacked key components of IDSR including laboratory testing capacity, disease reporting, risk communication, community engagement, and staff supervision systems. Insufficient financial support and an abundance of vertical programs further impeded progress. In-depth accounts by donors and key governmental informants demonstrate that although the system had a role in detecting Ebola in Liberia, it could not respond effectively to control the disease. CONCLUSION: Our findings suggest that post-war, Liberia's health system intended to prioritize epidemic preparedness and response with the adoption of IDSR. However, insufficient investment and systems development meant IDSR was not well implemented, leaving the country vulnerable to the devastating impact of the Ebola epidemic.
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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 | 0.105 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".