Abstract
Following the confirmation of the first two cases of pandemic influenza on 27 April 2009 in the United Kingdom (UK), syndromic surveillance data from the Health Protection Agency (HPA)/QSurveillance and HPA/NHS Direct systems were used to monitor the possible spread of pandemic influenza at local level during the first phase of the outbreak. During the early weeks, syndromic indicators sensitive to influenza activity monitored through the two schemes remained low and the majority of cases were travel-related. The first evidence of community spread was seen in the West Midlands region following a school-based outbreak in central Birmingham. During the first phase several Primary Care Trusts had periods of exceptional influenza activity two to three weeks ahead of the rest of the region. Community transmission in London began slightly later than in the West Midlands but the rates of influenza-like illness recorded by general practitioners (GPs) were ultimately higher. Influenza activity in the West Midlands and London regions peaked a week before the remainder of the UK. Data from the HPA/NHS Direct and HPA/QSurveillance systems were mapped at local level and used alongside laboratory data and local intelligence to assist in the identification of hotspots, to direct limited public health resources and to monitor the progression of the outbreak. This work has demonstrated the utility of local syndromic surveillance data in the detection of increased transmission and in the epidemiological investigation of the pandemic and has prompted future spatio-temporal work.
| Original language | English |
|---|---|
| Journal | Eurosurveillance |
| Volume | 16 |
| Issue number | 3 |
| Publication status | Published - Jan 2011 |
Bibliographical note
Funding Information:The authors are grateful to the generous technical help provided by Emmanuelle Rollet-Labelle, Charles Joly Beauparlant, and Antoine Bodein throughout the study. The authors thank the Bioimagerie du Petit Animal, the Cytometry, and the Microscopy platforms (CHU de Qu?bec). The authors also thank the High-Throughput Genomics and Bioinformatic Analysis Core at the Huntsman Cancer Institute, University of Utah.
Funding Information:
This work was supported by a foundation grant from the Canadian Institutes of Health Research (CIHR) (E.B.). E.B. is recipient of a new investigator award from the CIHR and the Fonds de Recherche en Santé du Québec (FRQS); and is a Canadian National Transplant Research Program researcher. P.R.F. is a recipient of a tier 1 Canada Research Chair on Systemic Autoimmune Rheumatic Diseases. N.T. and I.M. are recipients of fellowships from The Arthritis Society and from FRQS. R.A.C. is supported by a grant from the National Institutes of Health (NIH), National Institute on Aging (K01AG059892). M.T.R. is supported by grants from the NIH, National Heart, Lung, and Blood Institute (HL142804 and HL130541) and the National Institute on Aging (AG048022 and AG059877). M.T.R. was also supported, in part, by Merit Review Award Number I01 CX001696 from the US Department of Veterans Affairs Clinical Sciences R&D Service. This material is, in part, the result of work supported with resources and the use of facilities at the George E. Wahlen Veterans Affairs Medical Center. K.R.M. supported by the NIH, National Institute of Diabetes and Digestive and Kidney Diseases (K01DK111515), and is an American Society of Hematology Scholar. J.R. is a recipient of an operating grant from the CIHR (PJT-159652). D.S. was the recipient of a studentship from FRQS and Merit Fellowships from the Department of Microbiology and Immunology. C.L. is supported by the Lupus Research Alliance (519414).
