The Impact of Armed Conflicts on the Prevalence, Transmission, and Management of Infectious Diseases: A Systematic Review
Bibliographic record
Abstract
Armed conflicts persist despite global peace efforts, driven by cultural, religious, and ethnic divisions. These conflicts significantly impact public health by exacerbating the spread of infectious diseases and disrupting essential healthcare systems. This study aimed to assess the complex relationship between conflict and the prevalence, transmission, and management of infectious diseases in the affected populations. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to conduct a comprehensive search across five databases (EBSCO, PubMed, Web of Science, Cochrane, and Google Scholar) focusing on publications from 2020 to 2024. The search centered on topics such as infectious diseases, epidemics, war, health infrastructure, and public health systems. Papers published in English were screened using the Rayyan™ tool (Rayyan Systems Inc., Cambridge, MA) to ensure relevance to infectious diseases and conflicts. Data analysis was carried out using RevMan software (The Cochrane Collaboration, London, UK), while the Newcastle-Ottawa Scale (NOS) was employed to evaluate the quality of the included studies. The results reveal that conflicts significantly disrupt healthcare systems, leading to an increased prevalence of diseases such as tuberculosis, cholera, and soil-transmitted helminthiasis (STH). Effective interventions, including improved water, sanitation, and hygiene (WASH) conditions, targeted vaccination campaigns, and strengthened healthcare infrastructure, were critical in mitigating outbreaks. Despite methodological variations, the studies highlighted the multifaceted impact of conflict on public health. Conflict creates complex interdependencies between environmental, social, and health factors, worsening disease prevalence and management. In order to improve WASH conditions, prevent diseases, guarantee medication supply, and improve healthcare, coordination of efforts is essential. Future studies should examine community resiliency, socioeconomic determinants, and intervention evaluation.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".