Eliminating malaria in conflict zones: public health strategies developed in the Sri Lanka Civil War
Bibliographic record
Abstract
Despite the 26-year long civil war, Sri Lanka was declared malaria-free by WHO in 2016. This achievement was the result of nearly 30 years of elimination efforts following the last significant resurgence of malaria cases in Sri Lanka. The resurgence occurred in 1986-1987, when about 600 000 cases of malaria were detected. Obstacles to these efforts included a lack of healthcare workers in conflict zones, a disruption of vector control efforts, gaps in the medication supply chain, and rising malaria cases among the displaced population.This article seeks to describe the four strategies deployed in Sri Lanka to mitigate the aforementioned obstacles to ultimately achieve malaria elimination. The first approach was the support for disease elimination by the government of Sri Lanka and the Liberation Tamil Tigers of Elam. The second strategy was the balance of centralised leadership of the federal government and the decentralised programme operation at the regional level. The third strategy was the engagement of non-governmental stakeholders to fill in gaps left by the conflict to continue the elimination efforts. The last strategy is the ongoing efforts by the government, military and non-profit organisations to prevent the reintroduction of malaria.The lessons learnt from Sri Lanka have important implications for malaria-endemic nations that are in conflict such as Ethiopia, Afghanistan, Yemen and Somalia. To accomplish the World Health Assembly goal of reducing the global incidence and mortality of malaria by 90% by 2030, significant efforts are required to lessen the disease burden in conflict zones. In addition to the direct impacts of conflict on population health, conflicts may lead to increased risk of spread of malaria, both within a country and consequently, abroad.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".