RT Journal Article
SR Electronic
T1 Bigger and Better? Representativeness of the Influenza A surveillance using one consolidated clinical microbiology laboratory data set as compared to the Belgian Sentinel Network of Laboratories
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 343236
DO 10.1101/343236
A1 Sigi Van den Wijngaert
A1 Nathalie Bossuyt
A1 Bridget Ferns
A1 Laurent Busson
A1 Gabriela Serrano
A1 Magali Wautier
A1 Isabelle Thomas
A1 Matthew Byott
A1 Yves Dupont
A1 Eleni Nastouli
A1 Marie Hallin
A1 Zisis Kozlakidis
A1 Olivier Vandenberg
YR 2018
UL http://biorxiv.org/content/early/2018/06/10/343236.abstract
AB Infectious diseases remain a serious public health concern globally, while the need for reliable and representative surveillance systems remains as acute as ever. The public health surveillance of infectious diseases uses reported positive results from sentinel clinical laboratories or laboratory networks, to survey the presence of specific microbial agents known to constitute a threat to public health in a given population. This monitoring activity is commonly based on a representative fraction of the microbiology laboratories nationally reporting to a single central reference point. However in recent years a number of clinical microbiology laboratories (CML) have undergone a process of consolidation involving a shift towards laboratory amalgamation and closer real-time informational linkage. This report aims to investigate whether such merging activities might have a potential impact on infectious diseases surveillance. Influenza data was used from Belgian public health surveillance 2014-2017, to evaluate whether national infection trends could be estimated equally as effectively from only just one centralised CML serving the wider Brussels area (LHUB-ULB). The overall comparison reveals that there is a close correlation and representativeness of the LHUB-ULB data to the national and international data for the same time periods, both on epidemiological and molecular grounds. Notably, the effectiveness of the LHUB-ULB surveillance remains partially subject to local regional variations. These results illustrate that centralised CML-derived data are not only credible but also advantageous to use for future surveillance and prediction purposes, especially for automatic detection systems that might include multiple layers of information and timely implementation of control strategies.