TY - JOUR
T1 - Unraveling the drivers of MERS-CoV transmission
AU - Cauchemez, Simon
AU - Nouvellet, Pierre
AU - Cori, Anne
AU - Jombart, Thibaut
AU - Garske, Tini
AU - Clapham, Hannah
AU - Moore, Sean
AU - Mills, Harriet Linden
AU - Salje, Henrik
AU - Collins, Caitlin
AU - Rodriquez-Barraquer, Isabel
AU - Rileyd, Steven
AU - Truelove, Shaun
AU - Algarni, Homoud
AU - Alhakeem, Rafat
AU - AlHarbi, Khalid
AU - Turkistani, Abdulhafiz
AU - Aguas, Ricardo J.
AU - Cummings, Derek A.T.
AU - Van Kerkhove, Maria D.
AU - Donnelly, Christl A.
AU - Lessler, Justin
AU - Fraser, Christophe
AU - Al-Barrak, Ali
AU - Ferguson, Neil M.
N1 - Funding Information:
We acknowledge funding from the Medical Research Council, the National Institute for Health Research Health Protection Research Unit Programme, the Laboratory of Excellence Integrative Biology of Emerging Infectious Diseases, the European Union Seventh Framework Programme (FP7/2007-2013) under Grant 278433-PREDEMICS, the National Institute of General Medical Sciences Models of Infectious Disease Agent Study Initiative, the Bill and Melinda Gates Foundation, and the AXA Research Fund.
PY - 2016/8/9
Y1 - 2016/8/9
N2 - With more than 1,700 laboratory-confirmed infections, Middle East respiratory syndrome coronavirus (MERS-CoV) remains a significant threat for public health. However, the lack of detailed data on modes of transmission from the animal reservoir and between humans means that the drivers of MERS-CoV epidemics remain poorly characterized. Here, we develop a statistical framework to provide a comprehensive analysis of the transmission patterns underlying the 681 MERS-CoV cases detected in the Kingdom of Saudi Arabia (KSA) between January 2013 and July 2014. We assess how infections from the animal reservoir, the different levels of mixing, and heterogeneities in transmission have contributed to the buildup of MERS-CoV epidemics in KSA. We estimate that 12% [95% credible interval (CI): 9%, 15%] of cases were infected from the reservoir, the rest via human-to-human transmission in clusters (60%; CI: 57%, 63%), within (23%; CI: 20%, 27%), or between (5%; CI: 2%, 8%) regions. The reproduction number at the start of a cluster was 0.45 (CI: 0.33, 0.58) on average, but with large SD (0.53; CI: 0.35, 0.78). It was >1 in 12% (CI: 6%, 18%) of clusters but fell by approximately one-half (47% CI: 34%, 63%) its original value after 10 cases on average. The ongoing exposure of humans to MERS-CoV from the reservoir is of major concern, given the continued risk of substantial outbreaks in health care systems. The approach we present allows the study of infectious disease transmission when data linking cases to each other remain limited and uncertain.
AB - With more than 1,700 laboratory-confirmed infections, Middle East respiratory syndrome coronavirus (MERS-CoV) remains a significant threat for public health. However, the lack of detailed data on modes of transmission from the animal reservoir and between humans means that the drivers of MERS-CoV epidemics remain poorly characterized. Here, we develop a statistical framework to provide a comprehensive analysis of the transmission patterns underlying the 681 MERS-CoV cases detected in the Kingdom of Saudi Arabia (KSA) between January 2013 and July 2014. We assess how infections from the animal reservoir, the different levels of mixing, and heterogeneities in transmission have contributed to the buildup of MERS-CoV epidemics in KSA. We estimate that 12% [95% credible interval (CI): 9%, 15%] of cases were infected from the reservoir, the rest via human-to-human transmission in clusters (60%; CI: 57%, 63%), within (23%; CI: 20%, 27%), or between (5%; CI: 2%, 8%) regions. The reproduction number at the start of a cluster was 0.45 (CI: 0.33, 0.58) on average, but with large SD (0.53; CI: 0.35, 0.78). It was >1 in 12% (CI: 6%, 18%) of clusters but fell by approximately one-half (47% CI: 34%, 63%) its original value after 10 cases on average. The ongoing exposure of humans to MERS-CoV from the reservoir is of major concern, given the continued risk of substantial outbreaks in health care systems. The approach we present allows the study of infectious disease transmission when data linking cases to each other remain limited and uncertain.
KW - Animal reservoir
KW - Epidemic dynamics
KW - Mathematical modeling
KW - Outbreaks
KW - Zoonotic virus
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U2 - 10.1073/pnas.1519235113
DO - 10.1073/pnas.1519235113
M3 - Article
C2 - 27457935
AN - SCOPUS:84982975972
SN - 0027-8424
VL - 113
SP - 9081
EP - 9086
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 32
ER -