ArticleTransboundary and emerging diseases2022
Ecology of Middle East respiratory syndrome coronavirus, 2012-2020: A machine learning modelling analysis.
Article in Transboundary and emerging diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Oro-faecal transmission of SARS-CoV-2: A systematic review of studies employing viral culture from gastrointestinal and other potential oro-faecal sources and evidence for transmission to humans.Epidemiology and infection · 2024Pooled it
- Geospatial epidemiological approaches to the prevention and control of vector-borne diseases: A systematic review.Infectious diseases & immunity · 2026Review
- Epidemiological characteristics of ventilator-associated pneumonia in neurosurgery: A 10-year surveillance study in a Chinese tertiary hospital.Infectious medicine · 2024Article
- Ecology of Middle East respiratory syndrome coronavirus, 2012-2020: A machine learning modelling analysis.Transboundary and emerging diseases · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 6 institutions in 3 countries.
Funding
Abstract
The ongoing enzootic circulation of the Middle East respiratory syndrome coronavirus (MERS-CoV) in the Middle East and North Africa is increasingly raising the concern about the possibility of its recombination with other human-adapted coronaviruses, particularly the pandemic SARS-CoV-2. We aim to provide an updated picture about ecological niches of MERS-CoV and associated socio-environmental drivers. Based on 356 confirmed MERS cases with animal contact reported to the WHO and 63 records of animal infections collected from the literature as of 30 May 2020, we assessed ecological niches of MERS-CoV using an ensemble model integrating three machine learning algorithms. With a high predictive accuracy (area under receiver operating characteristic curve = 91.66% in test data), the ensemble model estimated that ecologically suitable areas span over the Middle East, South Asia and the whole North Africa, much wider than the range of reported locally infected MERS cases and test-positive animal samples. Ecological suitability for MERS-CoV was significantly associated with high levels of bareland coverage (relative contribution = 30.06%), population density (7.28%), average temperature (6.48%) and camel density (6.20%). Future surveillance and intervention programs should target the high-risk populations and regions informed by updated quantitative analyses.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.