Emerging and Re-emerging Infectious Diseases in the WHO Eastern Mediterranean Region, 2001-2018
Bibliographic record
Abstract
BACKGROUND: Countries in the World Health Organization (WHO) Eastern Mediterranean Region (EMR) are predisposed to highly contagious, severe and fatal, emerging infectious diseases (EIDs), and re-emerging infectious diseases (RIDs). This paper reviews the epidemiological situation of EIDs and RIDs of global concern in the EMR between 2001 and 2018. METHODS: To do a narrative review, a complete list of studies in the field was we prepared following a systematic search approach. Studies that were purposively reviewed were identified to summarize the epidemiological situation of each targeted disease. A comprehensive search of all published studies on EIDs and RIDs between 2001 and 2018 was carried out through search engines including Medline, Web of Science, Scopus, Google Scholar, and ScienceDirect. RESULTS: Leishmaniasis, hepatitis A virus (HAV) and hepatitis E virus (HEV) are reported from all countries in the region. Chikungunya, Crimean Congo hemorrhagic fever (CCHF), dengue fever, and H5N1 have been increasing in number, frequency, and expanding in their geographic distribution. Middle East respiratory syndrome (MERS), which was reported in this region in 2012 is still a public health concern. There are challenges to control cholera, diphtheria, leishmaniasis, measles, and poliomyelitis in some of the countries. Moreover, Alkhurma hemorrhagic fever (AHF), and Rift Valley fever (RVF) are limited to some countries in the region. Also, there is little information about the real situation of the plague, Q fever, and tularemia. CONCLUSION: EIDs and RIDs are prevalent in most countries in the region and could further spread within the region. It is crucial to improve regional capacities and capabilities in preventing and responding to disease outbreaks with adequate resources and expertise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".