Assessing 3 Outbreak Detection Algorithms in an Electronic Syndromic Surveillance System in a Resource-Limited Setting

Alsentzer, Emily; Ballard, Sarah-Blythe; Neyra, Joan; Vera, Delphis M.; Osorio, Victor B.; Quispe, Jose; Blazes, David L.; Loayza, Luis

doi:10.3201/eid2609.191315

Cited by 2 publications

(2 citation statements)

References 9 publications

(4 reference statements)

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“…Last, the system requires daily operation, and there is a need to ensure adequate staffing when surveilling for several weeks (Lewis et al, 2011;White et al, 2017;White et al, 2018). (Kool et al, 2012) workers, and data on a suspicious outbreak, event of public health concern, or rumor warning surveillance and has contributed to efforts to meet national surveillance-related capacity development obligations (Alsentzer et al, 2020;Kool et al, 2012;Paterson et al, 2012). Challenges with the system include issues with timeliness and completeness of reporting, data quality, and system stability.…”

Section: Cn-101681490-amentioning

confidence: 99%

Syndromic Surveillance 2.0: Emerging Global Surveillance Strategies for Infectious Disease Epidemics

2023

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This report documents research and analysis conducted as part of a project entitled Syndromic Surveillance 2.0: Identifying Emerging Epidemics in the 21st Century, sponsored by the U.S. Army Office of the Surgeon General. The purpose of the project was to identify how new technologies and approaches (economic indicators, social media scanning, and others) can be harnessed to gain early insights into emerging global epidemics to monitor health and safety threats to American soldiers across the globe and identify the epidemic's potential for widespread infection. The project also recommended promising strategies the Army could adopt to better monitor such biologic threats. This research was conducted within RAND Arroyo Center's Personnel, Training, and Health Program. RAND Arroyo Center, part of the RAND Corporation, is a federally funded research and development center (FFRDC) sponsored by the United States Army.RAND operates under a "Federal-Wide Assurance" (FWA00003425) and complies with the Code of Federal Regulations for the Protection of Human Subjects Under United States Law (45 CFR 46), also known as "the Common Rule," as well as with the implementation guidance set forth in DoD Instruction 3216.02. As applicable, this compliance includes reviews and approvals by RAND's Institutional Review Board (the Human Subjects Protection Committee) and by the U.S. Army. The views of sources utilized in this study are solely their own and do not represent the official policy or position of DoD or the U.S. Government. RecommendationsRecommendation 1. The Army should track academic and private enterprise efforts to detect diseases during the outbreak phase of epidemic surveillance. Because there is so much activity in this domain-activity that we assume will grow in the context of the coronavirus disease 2019 pandemic-the Army should continue to monitor progress but not necessarily invest in additional methods above and beyond its current investments in GEIS, DSRi, DMSS, and ESSENCE.Recommendation 2. The Army should establish more routine training to aid general medical officers in identifying and obtaining credible data and analyzing and interpreting the data. Our interviews with defense agency and CCMD representatives revealed that, although numerous systems exist to help CCMDs and services track possible disease outbreaks, there appears to be uneven awareness among some military medical personnel regarding what systems and information they can-or should-use for this purpose.Recommendation 3. The Army should consider investing in surveillance efforts that detect the possible emergence of an epidemic for use during the emergence phase of epidemic surveillance. Detecting viruses in animal populations is crucial to understanding the risk to humans at the human-animal interface, where most spillover events occur. The Army Veterinary Corps might be exceptionally well positioned to create or complement existing global veterinary public health surveillance efforts in support of the operational force. The Army might also be ab...

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Section: Cn-101681490-amentioning

confidence: 99%

Syndromic Surveillance 2.0: Emerging Global Surveillance Strategies for Infectious Disease Epidemics

2023

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“…The main differences among statistical methods used lie in how the expected value is calculated and how the statistical significance of the difference between the expected and observed value is determined [4] . Several aberration detection methods have been proposed including statistical process control, change-point detection, and spatial statistics methods [5] , [6] , [7] , [8] , [9] . Most research has been conducted on datasets drawn from large populations.…”

Section: Introductionmentioning

confidence: 99%

Comparison of statistical methods for the early detection of disease outbreaks in small population settings

Craig,

Leong,

Donoghoe

et al. 2023

IJID Regions

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Assessing 3 Outbreak Detection Algorithms in an Electronic Syndromic Surveillance System in a Resource-Limited Setting

Cited by 2 publications

References 9 publications

Syndromic Surveillance 2.0: Emerging Global Surveillance Strategies for Infectious Disease Epidemics

Syndromic Surveillance 2.0: Emerging Global Surveillance Strategies for Infectious Disease Epidemics

Comparison of statistical methods for the early detection of disease outbreaks in small population settings

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