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OBJECTIVE: We describe the Bangkok Dusit Medical Services Surveillance System(BDMS-SS) and use of surveillance efforts for influenza as an example of surveillancecapability in near real-time among a network of 20 hospitals in the Bangkok DusitMedical Services group (BDMS).
MATERIALS AND METHODS: The BDMS has a comprehensive network oflaboratory, epidemiologic, and early warning surveillance systems which represents thelargest body of information from private hospitals across Thailand. Hospitals andclinical laboratories have deployed automatic reporting mechanisms since 2010 andhave effectively improved timeliness of laboratory data reporting. In April 2017, thecapacity of near real-time influenza surveillance in BDMS was found to have ademonstrated and sustainable capability.
RESULTS: : From October 2010 to April 2017, the real-time laboratory basedsurveillance system automatically uploaded test results and associated data which were24 hours available to affiliated physicians, infectious nurses, local and national publichealth users. A total of 482,789 subjects were tested and 86,177 (17.84%) cases ofinfluenza were identified. Of those positive cases, 40,552 (47.0%) were influenza typeB, 31,412 (36.4%) were influenza A unspecified subtype, 6,181 (7.2%) were influenzaA H1N1, 4,001 (4.6%) were influenza A H3N2, 3,835 (4.4%) were influenza Aseasonal and 196 (0.4%) were respiratory syncytial virus.
CONCLUSION: This system was the first near real-time influenza surveillance systemin Thailand. This illustrates a high level of awareness and willingness among the BDMShospital network to report emerging infectious diseases, and highlights the robust andsensitive nature of BDMS’s surveillance system. It demonstrates the flexibility of thesurveillance systems in BDMS to adapt to major communicable diseases. BDMS canmore actively collaborate with national counterparts and use its expertise to strengthenglobal and regional surveillance capacity in Southeast Asia, in order to secureadvances for a world safe and secure from infectious disease
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2. Jian SW, Chen CM, Lee CY, et al. Real-Time Surveillance ofInfectious Diseases: Taiwan’s Experience. Health Secur2017;15(2):144-53.
3. Ghosh TS, Vogt RL. Active influenza surveillance at the locallevel: a model for local health agencies. Am J Public Health2008;98(2):213-5.
4. Baumbach J, Mueller M, Smelser C, et al. Enhancement ofinfluenza surveillance with aggregate rapid influenza testresults: New Mexico, 2003-2007. Am J Public Health 2009;99Suppl 2:S372-7.
5. Dalton CB, Carlson SJ, Butler MT, et al. Building influenzasurveillance pyramids in near real time, Australia. Emerg InfectDis 2013;19(11):1863-5.
6. Voldstedlund M, Haahr M, Emborg HD, et al. Real-timesurveillance of laboratory confirmed influenza based on theDanish microbiology database (MiBa). Stud Health TechnolInform 2013;192:978.
7. Huart M, Bedubourg G, Abat C, et al. Implementation and InitialAnalysis of a Laboratory-Based Weekly BiosurveillanceSystem, Provence-Alpes-Cote d’Azur, France. Emerg InfectDis 2017;23(4):582-9.
8. Yang JR, Teng HJ, Liu MT, et al. Taiwan’s Public HealthNational Laboratory System: Success in Influenza Diagnosis andSurveillance. Health Secur 2017;15(2):154-64.
9. Maman I, Badziklou K, Landoh ED, et al. Implementation ofinfluenza-like illness sentinel surveillance in Togo. BMCPublic Health 2014;14:981.
10. Zhao H, Green H, Lackenby A, et al. A new laboratory-basedsurveillance system (Respiratory DataMart System) forinfluenza and other respiratory viruses in England: results and experience from 2009 to 2012. Euro Surveill 2014;19(3).