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Adedire O, Love NK, Hughes HE, Buchan I, Vivancos R, Elliot AJ. Early Detection and Monitoring of Gastrointestinal Infections Using Syndromic Surveillance: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:489. [PMID: 38673400 PMCID: PMC11050429 DOI: 10.3390/ijerph21040489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 04/08/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024]
Abstract
The underreporting of laboratory-reported cases of community-based gastrointestinal (GI) infections poses a challenge for epidemiologists understanding the burden and seasonal patterns of GI pathogens. Syndromic surveillance has the potential to overcome the limitations of laboratory reporting through real-time data and more representative population coverage. This systematic review summarizes the utility of syndromic surveillance for early detection and surveillance of GI infections. Relevant articles were identified using the following keyword combinations: 'early warning', 'detection', 'gastrointestinal activity', 'gastrointestinal infections', 'syndrome monitoring', 'real-time monitoring', 'syndromic surveillance'. In total, 1820 studies were identified, 126 duplicates were removed, and 1694 studies were reviewed. Data extraction focused on studies reporting the routine use and effectiveness of syndromic surveillance for GI infections using relevant GI symptoms. Eligible studies (n = 29) were included in the narrative synthesis. Syndromic surveillance for GI infections has been implemented and validated for routine use in ten countries, with emergency department attendances being the most common source. Evidence suggests that syndromic surveillance can be effective in the early detection and routine monitoring of GI infections; however, 24% of the included studies did not provide conclusive findings. Further investigation is necessary to comprehensively understand the strengths and limitations associated with each type of syndromic surveillance system.
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Affiliation(s)
- Olubusola Adedire
- Institute of Population Health, University of Liverpool, Liverpool L69 3GF, UK;
- Real-Time Syndromic Surveillance Team, Field Services, Health Protection Operations, UK Health Security Agency, Birmingham B2 4BH, UK; (H.E.H.); (A.J.E.)
- National Institute for Health Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool L69 7BE, UK; (N.K.L.); (R.V.)
| | - Nicola K. Love
- National Institute for Health Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool L69 7BE, UK; (N.K.L.); (R.V.)
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Wirral CH64 7TE, UK
| | - Helen E. Hughes
- Real-Time Syndromic Surveillance Team, Field Services, Health Protection Operations, UK Health Security Agency, Birmingham B2 4BH, UK; (H.E.H.); (A.J.E.)
- National Institute for Health Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool L69 7BE, UK; (N.K.L.); (R.V.)
| | - Iain Buchan
- Institute of Population Health, University of Liverpool, Liverpool L69 3GF, UK;
- National Institute for Health Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool L69 7BE, UK; (N.K.L.); (R.V.)
| | - Roberto Vivancos
- National Institute for Health Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool L69 7BE, UK; (N.K.L.); (R.V.)
- Field Services North-West, Health Protection Operations, UK Health Security Agency, Liverpool L3 1DS, UK
| | - Alex J. Elliot
- Real-Time Syndromic Surveillance Team, Field Services, Health Protection Operations, UK Health Security Agency, Birmingham B2 4BH, UK; (H.E.H.); (A.J.E.)
- National Institute for Health Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool L69 7BE, UK; (N.K.L.); (R.V.)
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Ondrikova N, Harris JP, Douglas A, Hughes HE, Iturriza-Gomara M, Vivancos R, Elliot AJ, Cunliffe NA, Clough HE. Predicting Norovirus in England Using Existing and Emerging Syndromic Data: Infodemiology Study. J Med Internet Res 2023; 25:e37540. [PMID: 37155231 DOI: 10.2196/37540] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 11/28/2022] [Accepted: 02/19/2023] [Indexed: 05/10/2023] Open
Abstract
BACKGROUND Norovirus is associated with approximately 18% of the global burden of gastroenteritis and affects all age groups. There is currently no licensed vaccine or available antiviral treatment. However, well-designed early warning systems and forecasting can guide nonpharmaceutical approaches to norovirus infection prevention and control. OBJECTIVE This study evaluates the predictive power of existing syndromic surveillance data and emerging data sources, such as internet searches and Wikipedia page views, to predict norovirus activity across a range of age groups across England. METHODS We used existing syndromic surveillance and emerging syndromic data to predict laboratory data indicating norovirus activity. Two methods are used to evaluate the predictive potential of syndromic variables. First, the Granger causality framework was used to assess whether individual variables precede changes in norovirus laboratory reports in a given region or an age group. Then, we used random forest modeling to estimate the importance of each variable in the context of others with two methods: (1) change in the mean square error and (2) node purity. Finally, these results were combined into a visualization indicating the most influential predictors for norovirus laboratory reports in a specific age group and region. RESULTS Our results suggest that syndromic surveillance data include valuable predictors for norovirus laboratory reports in England. However, Wikipedia page views are less likely to provide prediction improvements on top of Google Trends and Existing Syndromic Data. Predictors displayed varying relevance across age groups and regions. For example, the random forest modeling based on selected existing and emerging syndromic variables explained 60% variance in the ≥65 years age group, 42% in the East of England, but only 13% in the South West region. Emerging data sets highlighted relative search volumes, including "flu symptoms," "norovirus in pregnancy," and norovirus activity in specific years, such as "norovirus 2016." Symptoms of vomiting and gastroenteritis in multiple age groups were identified as important predictors within existing data sources. CONCLUSIONS Existing and emerging data sources can help predict norovirus activity in England in some age groups and geographic regions, particularly, predictors concerning vomiting, gastroenteritis, and norovirus in the vulnerable populations and historical terms such as stomach flu. However, syndromic predictors were less relevant in some age groups and regions likely due to contrasting public health practices between regions and health information-seeking behavior between age groups. Additionally, predictors relevant to one norovirus season may not contribute to other seasons. Data biases, such as low spatial granularity in Google Trends and especially in Wikipedia data, also play a role in the results. Moreover, internet searches can provide insight into mental models, that is, an individual's conceptual understanding of norovirus infection and transmission, which could be used in public health communication strategies.
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Affiliation(s)
- Nikola Ondrikova
- Institute of Infection, Ecological and Veterinary Sciences, University of Liverpool, Liverpool, United Kingdom
- National Institute for Health and Care Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, United Kingdom
- Institute for Risk and Uncertainty, University of Liverpool, Liverpool, United Kingdom
| | - John P Harris
- Field Service, Health Protection Operations, United Kingdom Health Security Agency, Liverpool, United Kingdom
| | - Amy Douglas
- Gastrointestinal Infections and Food Safety (One Health) Division, United Kingdom Health Security Agency, London, United Kingdom
| | - Helen E Hughes
- National Institute for Health and Care Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, United Kingdom
- Real-time Syndromic Surveillance Team, Health Protection Operations, United Kingdom Health Security Agency, Birmingham, United Kingdom
| | | | - Roberto Vivancos
- National Institute for Health and Care Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, United Kingdom
- Field Service, Health Protection Operations, United Kingdom Health Security Agency, Liverpool, United Kingdom
- National Institute for Health and Care Research Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool, Liverpool, United Kingdom
| | - Alex J Elliot
- National Institute for Health and Care Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, United Kingdom
- Real-time Syndromic Surveillance Team, Health Protection Operations, United Kingdom Health Security Agency, Birmingham, United Kingdom
| | - Nigel A Cunliffe
- Institute of Infection, Ecological and Veterinary Sciences, University of Liverpool, Liverpool, United Kingdom
- National Institute for Health and Care Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, United Kingdom
| | - Helen E Clough
- Institute of Infection, Ecological and Veterinary Sciences, University of Liverpool, Liverpool, United Kingdom
- National Institute for Health and Care Research Health Protection Research Unit in Gastrointestinal Infections, University of Liverpool, Liverpool, United Kingdom
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Morbey RA, Elliot AJ, Smith GE, Charlett A. Adapting Syndromic Surveillance Baselines After Public Health Interventions. Public Health Rep 2020; 135:737-745. [PMID: 33026959 DOI: 10.1177/0033354920959080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Public health surveillance requires historical baselines to identify unusual activity. However, these baselines require adjustment after public health interventions. We describe an example of such an adjustment after the introduction of rotavirus vaccine in England in July 2013. METHODS We retrospectively measured the magnitude of differences between baselines and observed counts (residuals) before and after the introduction of a public health intervention, the introduction of a rotavirus vaccine in July 2013. We considered gastroenteritis, diarrhea, and vomiting to be indicators for national syndromic surveillance, including telephone calls to a telehealth system, emergency department visits, and unscheduled consultations with general practitioners. The start of the preintervention period varied depending on the availability of surveillance data: June 2005 for telehealth, November 2009 for emergency departments, and July 2010 for general practitioner data. The postintervention period was July 2013 to the second quarter of 2016. We then determined whether baselines incorporating a step-change reduction or a change in seasonality resulted in more accurate models of activity. RESULTS Residuals in the unadjusted baseline models increased by 42%-198% from preintervention to postintervention. Increases in residuals for vomiting indicators were 19%-44% higher than for diarrhea. Both step-change and seasonality adjustments improved the surveillance models; we found the greatest reduction in residuals in seasonally adjusted models (4%-75%). CONCLUSION Our results demonstrated the importance of adjusting surveillance baselines after public health interventions, particularly accounting for changes in seasonality. Adjusted baselines produced more representative expected values than did unadjusted baselines, resulting in fewer false alarms and a greater likelihood of detecting public health threats.
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Affiliation(s)
- Roger Antony Morbey
- 371011 Real-time Syndromic Surveillance Team, Field Service, National Infection Service, Public Health England, Birmingham, UK
| | - Alex James Elliot
- 371011 Real-time Syndromic Surveillance Team, Field Service, National Infection Service, Public Health England, Birmingham, UK
| | - Gillian Elizabeth Smith
- 371011 Real-time Syndromic Surveillance Team, Field Service, National Infection Service, Public Health England, Birmingham, UK
| | - Andre Charlett
- 371011 Statistics, Modelling and Economics Department, National Infection Service, Public Health England, London, UK
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Emberland KE, Wensaas KA, Litleskare S, Rortveit G. Consultations for gastroenteritis in general practice and out-of-hours services in Norway 2006-15. Fam Pract 2019; 36:614-620. [PMID: 30689824 PMCID: PMC6781938 DOI: 10.1093/fampra/cmy133] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Most of the patients with gastroenteritis seeking health care services are managed in primary care; yet, little is known about these consultations. Syndromic-based surveillance of gastrointestinal infections is used in several countries, including Norway. AIM To investigate the extent of, and explore characteristics associated with, consultations for gastroenteritis in primary care and to compare consultations in daytime general practice and out-of-hours (OOH) services in Norway. DESIGN AND SETTING Registry-based study using reimbursement claims data from all consultations in general practice and OOH services in Norway over the 10-year period, 2006-15. METHODS The main outcome variable was whether the consultation took place in general practice or OOH services. Possible associations with patient age and sex, time and use of point-of-care C-reactive protein (CRP) testing and sickness certificate issuing were investigated. RESULTS Gastroenteritis consultations (n = 1 281 048) represented 0.9% of all consultations in primary care (n = 140 199 637), of which 84.4% were conducted in general practice and 15.6% in OOH services. Young children and young adults dominated among the patients. Point-of-care CRP testing was used in 36.1% of the consultations. Sickness certificates were issued in 43.6% of consultations with patients in working age. Age-specific time variations in consultation frequencies peaking in winter months were observed. CONCLUSIONS The proportion of gastroenteritis consultations was higher in the OOH services when compared with daytime general practice. Young children and young adults dominated among the patients. The seasonal variation in consultation frequency is similar to that shown for gastroenteritis caused by norovirus.
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Affiliation(s)
- Knut Erik Emberland
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.,Research Unit for General Practice, NORCE Norwegian Research Centre, Bergen, Norway
| | - Knut-Arne Wensaas
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.,Research Unit for General Practice, NORCE Norwegian Research Centre, Bergen, Norway
| | - Sverre Litleskare
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.,Research Unit for General Practice, NORCE Norwegian Research Centre, Bergen, Norway
| | - Guri Rortveit
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.,Research Unit for General Practice, NORCE Norwegian Research Centre, Bergen, Norway
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Morbey R, Hughes H, Smith G, Challen K, Hughes TC, Elliot AJ. Potential added value of the new emergency care dataset to ED-based public health surveillance in England: an initial concept analysis. Emerg Med J 2019; 36:459-464. [PMID: 31253597 DOI: 10.1136/emermed-2018-208323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 06/04/2019] [Accepted: 06/13/2019] [Indexed: 11/04/2022]
Abstract
INTRODUCTION For the London Olympic and Paralympic Games in 2012, a sentinel ED syndromic surveillance system was established to enhance public health surveillance by obtaining data from a selected network of EDs, focusing on London. In 2017, a new national standard Emergency Care Dataset was introduced, which enabled Public Health England (PHE) to initiate the expansion of their sentinel system to national coverage. Prior to this initiative, we estimated the added value, and potential additional resource use, of an expansion of the sentinel surveillance system. METHODS The detection capabilities of the sentinel and national systems were compared using the aberration detection methods currently used by PHE. Different scenarios were used to measure the impact on health at a local, subnational and national level, including improvements to sensitivity and timeliness, along with changes in specificity. RESULTS The biggest added value was found to be for detecting local impacts, with an increase in sensitivity of over 80%. There were also improvements found at a national level with outbreaks being detected earlier and smaller impacts being detectable. However, the increased number of local sites will also increase the number of false alarms likely to be generated. CONCLUSION We have quantified the added value of national ED syndromic surveillance systems, showing how they will enable detection of more localised events. Furthermore, national systems add value in enabling timelier public health interventions. Finally, we have highlighted areas where extra resource may be required to manage improvements in detection coverage.
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Affiliation(s)
- Roger Morbey
- Real-time Syndromic Surveillance, Public Health England, Birmingham, UK
| | - Helen Hughes
- Real-time Syndromic Surveillance, Public Health England, Birmingham, UK
| | - Gillian Smith
- Real-time Syndromic Surveillance, Public Health England, Birmingham, UK
| | - Kirsty Challen
- Lancashire Teaching Hospitals NHS Foundation Trust, Chorley, Lancashire, UK
| | | | - Alex J Elliot
- Real-time Syndromic Surveillance, Public Health England, Birmingham, UK
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Colón-González FJ, Lake IR, Morbey RA, Elliot AJ, Pebody R, Smith GE. A methodological framework for the evaluation of syndromic surveillance systems: a case study of England. BMC Public Health 2018; 18:544. [PMID: 29699520 PMCID: PMC5921418 DOI: 10.1186/s12889-018-5422-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Accepted: 04/09/2018] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Syndromic surveillance complements traditional public health surveillance by collecting and analysing health indicators in near real time. The rationale of syndromic surveillance is that it may detect health threats faster than traditional surveillance systems permitting more timely, and hence potentially more effective public health action. The effectiveness of syndromic surveillance largely relies on the methods used to detect aberrations. Very few studies have evaluated the performance of syndromic surveillance systems and consequently little is known about the types of events that such systems can and cannot detect. METHODS We introduce a framework for the evaluation of syndromic surveillance systems that can be used in any setting based upon the use of simulated scenarios. For a range of scenarios this allows the time and probability of detection to be determined and uncertainty is fully incorporated. In addition, we demonstrate how such a framework can model the benefits of increases in the number of centres reporting syndromic data and also determine the minimum size of outbreaks that can or cannot be detected. Here, we demonstrate its utility using simulations of national influenza outbreaks and localised outbreaks of cryptosporidiosis. RESULTS Influenza outbreaks are consistently detected with larger outbreaks being detected in a more timely manner. Small cryptosporidiosis outbreaks (<1000 symptomatic individuals) are unlikely to be detected. We also demonstrate the advantages of having multiple syndromic data streams (e.g. emergency attendance data, telephone helpline data, general practice consultation data) as different streams are able to detect different outbreak types with different efficacy (e.g. emergency attendance data are useful for the detection of pandemic influenza but not for outbreaks of cryptosporidiosis). We also highlight that for any one disease, the utility of data streams may vary geographically, and that the detection ability of syndromic surveillance varies seasonally (e.g. an influenza outbreak starting in July is detected sooner than one starting later in the year). We argue that our framework constitutes a useful tool for public health emergency preparedness in multiple settings. CONCLUSIONS The proposed framework allows the exhaustive evaluation of any syndromic surveillance system and constitutes a useful tool for emergency preparedness and response.
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Affiliation(s)
- Felipe J. Colón-González
- School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ UK
- NIHR Health Protection Research Unit for Emergency Preparedness and Response, London, UK
| | - Iain R. Lake
- School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ UK
- NIHR Health Protection Research Unit for Emergency Preparedness and Response, London, UK
| | - Roger A. Morbey
- Real-time Syndromic Surveillance Team, National Infection Service, Public Health England, Birmingham, B3 2PW UK
- NIHR Health Protection Research Unit for Emergency Preparedness and Response, London, UK
| | - Alex J. Elliot
- Real-time Syndromic Surveillance Team, National Infection Service, Public Health England, Birmingham, B3 2PW UK
- NIHR Health Protection Research Unit for Emergency Preparedness and Response, London, UK
| | - Richard Pebody
- Respiratory Diseases Department, National Infection Service, Public Health England, London, NW9 5EQ UK
| | - Gillian E. Smith
- Real-time Syndromic Surveillance Team, National Infection Service, Public Health England, Birmingham, B3 2PW UK
- NIHR Health Protection Research Unit for Emergency Preparedness and Response, London, UK
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Bjelkmar P, Hansen A, Schönning C, Bergström J, Löfdahl M, Lebbad M, Wallensten A, Allestam G, Stenmark S, Lindh J. Early outbreak detection by linking health advice line calls to water distribution areas retrospectively demonstrated in a large waterborne outbreak of cryptosporidiosis in Sweden. BMC Public Health 2017; 17:328. [PMID: 28420373 PMCID: PMC5395832 DOI: 10.1186/s12889-017-4233-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 04/06/2017] [Indexed: 11/25/2022] Open
Abstract
Background In the winter and spring of 2011 a large outbreak of cryptosporidiosis occurred in Skellefteå municipality, Sweden. This study summarizes the outbreak investigation in terms of outbreak size, duration, clinical characteristics, possible source(s) and the potential for earlier detection using calls to a health advice line. Methods The investigation included two epidemiological questionnaires and microbial analysis of samples from patients, water and other environmental sources. In addition, a retrospective study based on phone calls to a health advice line was performed by comparing patterns of phone calls between different water distribution areas. Results Our analyses showed that approximately 18,500 individuals were affected by a waterborne outbreak of cryptosporidiosis in Skellefteå in 2011. This makes it the second largest outbreak of cryptosporidiosis in Europe to date. Cryptosporidium hominis oocysts of subtype IbA10G2 were found in patient and sewage samples, but not in raw water or in drinking water, and the initial contamination source could not be determined. The outbreak went unnoticed to authorities for several months. The analysis of the calls to the health advice line provides strong indications early in the outbreak that it was linked to a particular water treatment plant. Conclusions We conclude that an earlier detection of the outbreak by linking calls to a health advice line to water distribution areas could have limited the outbreak substantially. Electronic supplementary material The online version of this article (doi:10.1186/s12889-017-4233-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Pär Bjelkmar
- Department of Monitoring and Evaluation, Public Health Agency of Sweden, 171 83, Solna, Sweden.
| | - Anette Hansen
- Department of Microbiology, Public Health Agency of Sweden, Solna, Sweden
| | - Caroline Schönning
- Department of Monitoring and Evaluation, Public Health Agency of Sweden, 171 83, Solna, Sweden
| | - Jakob Bergström
- Department of Monitoring and Evaluation, Public Health Agency of Sweden, 171 83, Solna, Sweden
| | - Margareta Löfdahl
- Department of Monitoring and Evaluation, Public Health Agency of Sweden, 171 83, Solna, Sweden
| | - Marianne Lebbad
- Department of Microbiology, Public Health Agency of Sweden, Solna, Sweden
| | - Anders Wallensten
- Department of Monitoring and Evaluation, Public Health Agency of Sweden, 171 83, Solna, Sweden.,Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Görel Allestam
- Department of Monitoring and Evaluation, Public Health Agency of Sweden, 171 83, Solna, Sweden
| | - Stephan Stenmark
- Department of Clinical Microbiology, Umeå University, Umeå, Sweden
| | - Johan Lindh
- Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
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