Prediction and surveillance of influenza epidemics

Medical Journal of Australia

Volumn 194, Issue 4, 2011, Pages

  Boyle, Justin R ^a   Sparks, Ross S ^a   Keijzers, Gerben B ^b,d   Crilly, Julia L ^c   Lind, James F ^b,c   Ryan, Louise M ^a  

^a CSIRO   (Australia)

^b GOLD COAST UNIVERSITY HOSPITAL   (Australia)

^c GRIFFITH UNIVERSITY   (Australia)

^d BOND UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; AUSTRALIA; BRONCHOPNEUMONIA; DISEASE SURVEILLANCE; EMERGENCY WARD; EPIDEMIC; HOSPITAL ADMISSION; HUMAN; INFLUENZA; INFLUENZA A (H1N1); INTERNATIONAL CLASSIFICATION OF DISEASES; INTERNET; LOBAR PNEUMONIA; MONITORING; PREDICTION AND FORECASTING; PUBLIC HOSPITAL; VIRAL GASTROENTERITIS; VIRUS INFECTION; VIRUS PNEUMONIA; WINTER;

EID: 79952474392     PISSN: 0025729X     EISSN: 13265377     Source Type: Journal    
DOI: 10.5694/j.1326-5377.2011.tb02940.x     Document Type: Article

References (25)

1. Burkom HS, Murphy SP, Shmueli G. Automated time series forecasting for biosurveillance. Stat Med 2007; 26: 4202-4218. (Pubitemid 47399348)
2. Buckeridge DL. Outbreak detection through automated surveillance: a review of the determinants of detection. J Biomed Inform 2007; 40: 370-379. (Pubitemid 47187786)
3. Elbert Y, Burkom HS. Development and evaluation of a data-adaptive alerting algorithm for univariate temporal biosurveillance data. Stat Med 2009; 28: 3226-3248.
4. Murphy SP, Burkom H. Recombinant temporal aberration detection algorithms for enhanced biosurveillance. J Am Med Inform Assoc 2008; 15: 77-86. (Pubitemid 350216490)
5. Berger M, Shiau R, Weintraub JM. Review of syndromic surveillance: implications for waterborne disease detection. J Epidemiol Community Health 2006; 60: 543-550. (Pubitemid 43806094)
6. Watkins RE, Eagleson S, Veenendaal B, et al. Disease surveillance using a hidden Markov model. BMC Med Inform Decis Mak 2009; 9: 39.
7. Watkins RE, Eagleson S, Veenendaal B, et al. Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia. BMC Med Inform Decis Mak 2008; 8: 37.
8. Fricker RD Jr, Hegler BL, Dunfee DA. Comparing syndromic surveillance detection methods: EARS versus a CUSUM-based methodology. Stat Med 2008; 27: 3407-3429.
9. Cakici B, Hebing K, Grunewald M, et al. CASE: a framework for computer supported outbreak detection. BMC Med Inform Decis Mak 2010; 10: 14.
10. Zheng W, Aitken R, Muscatello DJ, et al. Potential for early warning of viral influenza activity in the community by monitoring clinical diagnoses of influenza in hospital emergency departments. BMC Public Health 2007; 7: 250.
11. Schindeler SK, Muscatello DJ, Ferson MJ, et al. Evaluation of alternative respiratory syndromes for specific syndromic surveillance of influenza and respiratory syncytial virus: a time series analysis. BMC Infect Dis 2009; 9: 190.
12. Hope K, Durrheim DN, Muscatello D, et al. Identifying pneumonia outbreaks of public health importance: can emergency department data assist in earlier identification? Aust N Z J Public Health 2008; 32: 361-363.
13. Hope K, Merritt T, Durrheim DN. Evaluating the utility of emergency department syndromic surveillance for a regional public health service. Commun Dis Intell 2010; 34: 310-318.
14. Muscatello DJ, Churches T, Kaldor J, et al. An automated, broad-based, near real-time public health surveillance system using presentations to hospital emergency departments in New South Wales, Australia. BMC Public Health 2005; 5: 141.
15. Churches T, Conaty SJ, Gilmour RE, Muscatello D. Reflections on public health surveillance of pandemic (H1N1) 2009 influenza in NSW. NSW Public Health Bull 2010; 21(1-2): 19-25.
16. Sparks RS, Keighley T, Muscatello D. Early warning CUSUM plans for surveillance of negative binomial daily disease counts. J Appl Stat 2010; 37: 1911-1929.
17. Hoot N, Aronsky D. An early warning system for overcrowding in the emergency department, AMIA Annu Symp Proc 2006: 339-343.
18. Hoot NR, Zhou C, Jones I, et al. Measuring and forecasting emergency department crowding in real time. Ann Emerg Med 2007; 49: 747-755. (Pubitemid 46755440)
19. McCarthy ML, Zeger SL, Ding R, et al. The challenge of predicting demand for emergency department services. Acad Emerg Med 2008; 15: 337-346.
20. Schweigler LM, Desmond JS, McCarthy ML. Forecasting models of emergency department crowding. Acad Emerg Med 2009; 16: 301-308.
21. Champion R, Kinsman LD, Lee GA, et al. Forecasting emergency department presentations. Aust Health Rev 2007; 31: 83-90.
22. Jones SS, Thomas A, Evans RS, et al. Forecasting daily patient volumes in the emergency department. Acad Emerg Med 2008; 15: 159-170.
23. Ginsberg J, Mohebbi MH, Patel RS, et al. Detecting influenza epidemics using search engine query data. Nature 2009; 457: 1012-1014.
24. Eysenbach G. Infodemiology: tracking flu-related searches on the web for syndromic surveillance. AMIA Annu Symp Proc 2006: 244-248.
25. Wilson N, Mason K, Tobias M, et al. Interpreting Google flu trends data for pandemic H1N1 influenza: the New Zealand experience. Euro Surveill 2009; 14 (44). pii: 19386.