Finalists for ISDS's 2013 'Awards for Outstanding Research Articles in Biosurveillance'

ISDS is proud to announce the finalists for the 2013 'Awards for Outstanding Research Articles in Biosurveillance' (see below), which are awarded in recognition of exceptional research literature in the field. ISDS members now have the opportunity to vote for the winners, which will be announced on August 6, 2013.

ISDS congratulates all of the finalists on their exemplary work!

For more information on the awards and selection process, please click here.

Finalists in the 'Impact on field of biosurveillance' category:

Enki DG, Noufaily A, Garthwaite PH, et al. Automated Biosurveillance Data from England and Wales, 1991-2011. Emerg Infect Dis. 2013;19(1):35–42. doi:10.3201/eid1901.120493.

Abstract: Twenty years of data provide valuable insights for the design of large automated outbreak detection systems., Outbreak detection systems for use with very large multiple surveillance databases must be suited both to the data available and to the requirements of full automation. To inform the development of more effective outbreak detection algorithms, we analyzed 20 years of data (1991–2011) from a large laboratory surveillance database used for outbreak detection in England and Wales. The data relate to 3,303 distinct types of infectious pathogens, with a frequency range spanning 6 orders of magnitude. Several hundred organism types were reported each week. We describe the diversity of seasonal patterns, trends, artifacts, and extra-Poisson variability to which an effective multiple laboratory-based outbreak detection system must adjust. We provide empirical information to guide the selection of simple statistical models for automated surveillance of multiple organisms, in the light of the key requirements of such outbreak detection systems, namely, robustness, flexibility, and sensitivity.

Paterson BJ, Durrheim DN. The remarkable adaptability of syndromic surveillance to meet public health needs. Journal of Epidemiology and Global Health. 2013;3(1):41–47. doi:10.1016/j.jegh.2012.12.005.

Abstract: The goal of syndromic surveillance is the earlier detection of epidemics, allowing a timelier public health response than is possible using traditional surveillance methods. Syndromic surveillance application for public health purposes has changed over time and reflects a dynamic evolution from the collection, interpretation of data with dissemination of data to those who need to act, to a more holistic approach that incorporates response as a core component of the surveillance system. Recent infectious disease threats, such as severe acute respiratory syndrome (SARS), avian influenza (H5N1) and pandemic influenza (H1N1), have all highlighted the need for countries to be rapidly aware of the spread of infectious diseases within a region and across the globe. The International Health Regulations (IHR) obligation to report public health emergencies of international concern has raised the importance of early outbreak detection and response. The emphasis in syndromic surveillance is changing from automated, early alert and detection, to situational awareness and response. Published literature on syndromic surveillance reflects the changing nature of public health threats and responses. Syndromic surveillance has demonstrated a remarkable ability to adapt to rapidly shifting public health needs. This adaptability makes it a highly relevant public health tool.

Schirmer PL, Lucero-Obusan CA, Benoit SR, et al. Dengue Surveillance in Veterans Affairs Healthcare Facilities, 2007–2010. PLoS Negl Trop Dis. 2013;7(3):e2040. doi:10.1371/journal.pntd.0002040.

Abstract: Dengue is an important tropical disease seen throughout the world in tropical climate zones and is spread by Aedes mosquitoes. Most cases of dengue in the continental US are imported. In July 2009 through 2010, dengue virus was found to be circulating in Key West, Florida (FL). Dengue virus has been transmitted in Puerto Rico (PR) for many years. This study used electronic and manual surveillance systems to identify dengue cases in VA healthcare facilities and clinically compared dengue cases in Veterans presenting for care in PR as well as in FL. We found that FL dengue cases were similar to those in PR and that Centers for Disease Control and Prevention defined confirmed/probable cases were more likely to be hospitalized within our VA system, and have either lower platelet or white blood cell counts than suspected cases. During July 2009–2010, FL cases were more likely to be tested for dengue and have intensive care admissions, but had lower hospitalization rates and headache or eye pain symptoms compared to PR cases. No one method of capturing dengue cases was perfect. It is important to educate healthcare workers about this disease to help with direct patient care as well as surveillance.

Stoto MA. The Effectiveness of U.S. Public Health Surveillance Systems for Situational Awareness during the 2009 H1N1 Pandemic: A Retrospective Analysis. PLoS ONE. 2012;7(8):e40984. doi:10.1371/journal.pone.0040984.

Abstract: Background: The 2009 H1N1 outbreak provides an opportunity to learn about the strengths and weaknesses of current U.S. public health surveillance systems and to identify implications for measuring public health emergency preparedness. Methodology/Principal Findings: We adopted a "triangulation" approach in which multiple contemporary data sources, each with different expected biases, are compared to identify time patterns that are likely to reflect biases versus those that are more likely to be indicative of actual infection rates. This approach is grounded in the understanding that surveillance data are the result of a series of decisions made by patients, health care providers, and public health professionals about seeking and providing health care and about reporting cases to health authorities. Although limited by the lack of a gold standard, this analysis suggests that children and young adults are over-represented in many pH1N1 surveillance systems, especially in the spring wave. In addition, the nearly two-month delay between the Northeast and the South in the Fall peak in some surveillance data seems to at least partially reflect regional differences in concerns about pH1N1rather than real differences in pH1N1 infection rates. Conclusions/Significance: Although the extent of the biases suggested by this analysis cannot be known precisely, the analysis identifies underlying problems with surveillance systems – in particular their dependence on patient and provider behavior, which is influenced by a changing information environment – that could limit situational awareness in future public health emergencies. To improve situational awareness in future health emergencies, population-based surveillance systems such as telephone surveys of representative population samples and seroprevalence surveys in well-defined population cohorts are needed.

Finalists in the 'Scientific Achievement' category:

Conway M, Dowling JN, Chapman WW. Using chief complaints for syndromic surveillance: A review of chief complaint based classifiers in North America. Journal of Biomedical Informatics. 2013. doi:10.1016/j.jbi.2013.04.003.

Abstract: A major goal of Natural Language Processing in the public health informatics domain is the automatic extraction and encoding of data stored in free text patient records. This extracted data can then be utilized by computerized systems to perform syndromic surveillance. In particular, the chief complaint—a short string that describes a patient's symptoms—has come to be a vital resource for syndromic surveillance in the North American context due to its near ubiquity. This paper reviews fifteen systems in North America—at the city, county, state and federal level—that use chief complaints for syndromic surveillance.

Farrington CP, Whitaker HJ, Unkel S, Pebody R. Correlated infections: quantifying individual heterogeneity in the spread of infectious diseases. American Journal of Epidemiology. 2013;177(5):474–486.

Abstract: In this paper, we propose new methods for investigating the extent of heterogeneity in effective contact rates relevant to the transmission of infections. These methods exploit the correlations between ages at infection for different infections within individuals. The methods are developed for serological surveys, which provide accessible individual data on several infections, and are applied to a wide range of infections. We find that childhood infections are often highly correlated within individuals in early childhood, with the correlations persisting into adulthood only for infections sharing a transmission route. We discuss 2 applications of the methods: 1) to making inferences about routes of transmission when these are unknown or uncertain and 2) to estimating epidemiologic parameters such as the basic reproduction number and the critical immunization threshold. Two examples of such applications are presented: elucidating the transmission route of polyomaviruses BK and JC and estimating the basic reproduction number and critical immunization coverage of varicella-zoster infection in Belgium, Italy, Poland, and England and Wales. We speculate that childhood correlations stem from confounding of different transmission routes and represent heterogeneity in childhood circumstances, notably nursery-school attendance. In contrast, it is suggested that correlations in adulthood are route-specific.

Shaman J, Karspeck A. Forecasting seasonal outbreaks of influenza. PNAS. 2012;109(50):20425–20430. doi:10.1073/pnas.1208772109.

Abstract: Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, using a data assimilation technique commonly applied in numerical weather prediction. The availability of real-time, web-based estimates of local influenza infection rates makes this type of quantitative forecasting possible. Retrospective ensemble forecasts are generated on a weekly basis following assimilation of these web-based estimates for the 2003–2008 influenza seasons in New York City. The findings indicate that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak. In addition, confidence in those predictions can be inferred from the spread of the forecast ensemble. This work represents an initial step in the development of a statistically rigorous system for real-time forecast of seasonal influenza.

*This award was developed and is coordinated by the ISDS Research Committee.