Innovation in observation: a vision for early outbreak detection

Article English OPEN
Fefferman, Nina ; Naumova, Elena (2011)

The emergence of new infections and resurgence of old ones — health threats stemming from environmental contamination or purposeful acts of bioterrorism — call for a worldwide effort in improving early outbreak detection, with the goal to ameliorate current and future risks. In some cases, the problem of outbreak detection is logistically straightforward and mathematically easy: a single case of a disease of great concern can constitute an outbreak. However, for the vast majority of maladies, a simple analytical solution does not exist. Furthermore, each step in developing reliable, sensitive, effective surveillance systems demonstrates enormous complexities in the transmission, manifestation, detection and control of emerging health threats. In this communication we explore potential future innovations in early outbreak detection systems which can overcome the pitfalls of current surveillance. We think that modern advances in assembling data, techniques for collating and processing information, and technology that enables integrated analysis will facilitate a new paradigm in outbreak definition and detection. We anticipate that moving forward in this direction will provide the highly desired sensitivity and specificity in early detection required to meet the emerging challenges of global disease surveillance. (Accepted: 20 May 2010) Citation: Emerging Health Threats Journal 2010, 3:e6. doi: 10.3134/ehtj.10.006
  • References (41)
    41 references, page 1 of 5

    1 Burr T, Michalak S, Picard R. Mathematical and statistical estimation approaches in epidemiology. In: Castillo-Chavez C (ed). Mathematical and Statistical Estimation Approaches in Epidemiology. Springer: The Netherlands, 2009, pp 163-87.

    2 Tokars JI, Burkom H, Xing J, English R, Bloom S, Cox K, et al. Enhancing time-series detection algorithms for automated biosurveillance. Emerg Infect Dis 2009;15:533-9.

    3 Thompson WW, Shay DK, Weintraub E, Brammer L, Cox N, Anderson LJ, et al. Mortality associated with influenza and respiratory syncytial virus in the United States. JAMA 2003;289:179-86.

    4 Wong W-K, Moore A, Cooper G, Wagner MM. Rule-based anomaly pattern detection for detecting disease outbreaks. Proc Eighteenth Natl Conf Artif Intell (AAAI-02), 2002, pp 217-23.

    5 Fricker Jr RD, Hegler BL, Dunfee DA. Comparing syndromic surveillance detection methods: EARS' versus a CUSUM-based methodology. Stat Med 2008;27:3407-29.

    6 Woodall WH. The use of control charts in health-care and publichealth surveillance. J Qual Technol 2006;38:1-16.

    7 Woodall WH, Marshall MDJ Jr, Joner MD, Fraker SE, Abdel-Salam ASG. On the use and evaluation of prospective scan methods for health-related surveillance. J R Stat Soc Ser A (Stat Soc) 2008;171:223- 37.

    8 Murphy SP, Burkom H. Recombinant temporal aberration detection algorithms for enhanced biosurveillance. J Am Med Inform Assoc 2008;15:77-86.

    9 Reis BY, Kohane IS, Mandl KD. An epidemiological network model for disease outbreak detection. PLoS Med 2007;4:e210.

    10 Sintchenko V, Gallego B, Chung G, Coiera E. Towards bioinformatics assisted infectious disease control. BMC Bioinformatics 2009;10(Suppl 2): S10.

  • Related Research Results (1)
  • Metrics
    No metrics available
Share - Bookmark