Tweet
Transbound Emerg Dis
. 2022 Apr 2.
doi: 10.1111/tbed.14548. Online ahead of print.
Ecology of middle east respiratory syndrome coronavirus, 2012-2020: A machine learning modeling analysis
An-Ran Zhang 1 2 3 , Xin-Lou Li 4 , Tao Wang 2 , Kun Liu 5 , Ming-Jin Liu 3 , Wen-Hui Zhang 2 , Guo-Ping Zhao 6 , Jin-Jin Chen 2 , Xiao-Ai Zhang 2 , Dong Miao 2 , Wei Ma 1 , Li-Qun Fang 2 , Yang Yang 3 , Wei Liu 2
Affiliations
- PMID: 35366384
- DOI: 10.1111/tbed.14548
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 May 30, 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. This article is protected by copyright. All rights reserved.
Keywords: MERS-CoV; Middle East respiratory syndrome; machine learning; predicted map; risk factors.