<b>State‐Level Zoonotic Disease Surveillance in the United States</b>

Scotch, Matthew; Rabinowitz, Peter; Brandt, Cynthia

doi:10.1111/j.1863-2378.2011.01401.x

Cited by 6 publications

(5 citation statements)

References 8 publications

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“…Expert opinion methods ( n = 5) [66, 68, 125, 132, 133] were conducted in order to evaluate surveillance systems in place ( n = 4), to identify risk factors and population strata at higher risk ( n = 1), or to obtain risk estimates for disease entry and exposure ( n = 1). Considering that this is a purely subjective method, caution is needed when using the outputs derived from these approaches.…”

Section: Resultsmentioning

confidence: 99%

“…A novel technique within the sub-category active surveillance (i.e. earliest publication included in the review in 2005) was the use of participatory surveillance (n = 7) [63][64][65][66][67][68][69]. The application of this method can result in enhanced collaboration and communication among different sectors and institutions to better understand the causes of success or failure (i.e.…”

Section: Active Surveillance Approachesmentioning

confidence: 99%

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Systematic review of surveillance systems and methods for early detection of exotic, new and re-emerging diseases in animal populations

et al. 2014

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In this globalized world, the spread of new, exotic and re-emerging diseases has become one of the most important threats to animal production and public health. This systematic review analyses conventional and novel early detection methods applied to surveillance. In all, 125 scientific documents were considered for this study. Exotic (n = 49) and re-emerging (n = 27) diseases constituted the most frequently represented health threats. In addition, the majority of studies were related to zoonoses (n = 66). The approaches found in the review could be divided in surveillance modalities, both active (n = 23) and passive (n = 5); and tools and methodologies that support surveillance activities (n = 57). Combinations of surveillance modalities and tools (n = 40) were also found. Risk-based approaches were very common (n = 60), especially in the papers describing tools and methodologies (n = 50). The main applications, benefits and limitations of each approach were extracted from the papers. This information will be very useful for informing the development of tools to facilitate the design of cost-effective surveillance strategies. Thus, the current literature review provides key information about the advantages, disadvantages, limitations and potential application of methodologies for the early detection of new, exotic and re-emerging diseases.

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Section: Resultsmentioning

confidence: 99%

Section: Active Surveillance Approachesmentioning

confidence: 99%

Systematic review of surveillance systems and methods for early detection of exotic, new and re-emerging diseases in animal populations

et al. 2014

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“… 80 Studies have shown the importance of having these systems interconnected and that there are many barriers to successful implementation. 127 , 128 Research has shown that although almost 90% of veterinarians report encountering a zoonotic disease, 70% believe they do not have access to a surveillance system, and <50% have reported a zoonotic disease to state or federal agencies. With the inability to share much of the data electronically, integrating into human platforms may overcome some of these barriers.…”

Section: Applications Of Veterinary Informaticsmentioning

confidence: 99%

Veterinary informatics: forging the future between veterinary medicine, human medicine, and One Health initiatives—a joint paper by the Association for Veterinary Informatics (AVI) and the CTSA One Health Alliance (COHA)

Lustgarten

Zehnder²,

Shipman

et al. 2020

JAMIA Open

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Objectives This manuscript reviews the current state of veterinary medical electronic health records and the ability to aggregate and analyze large datasets from multiple organizations and clinics. We also review analytical techniques as well as research efforts into veterinary informatics with a focus on applications relevant to human and animal medicine. Our goal is to provide references and context for these resources so that researchers can identify resources of interest and translational opportunities to advance the field. Methods and Results This review covers various methods of veterinary informatics including natural language processing and machine learning techniques in brief and various ongoing and future projects. After detailing techniques and sources of data, we describe some of the challenges and opportunities within veterinary informatics as well as providing reviews of common One Health techniques and specific applications that affect both humans and animals. Discussion Current limitations in the field of veterinary informatics include limited sources of training data for developing machine learning and artificial intelligence algorithms, siloed data between academic institutions, corporate institutions, and many small private practices, and inconsistent data formats that make many integration problems difficult. Despite those limitations, there have been significant advancements in the field in the last few years and continued development of a few, key, large data resources that are available for interested clinicians and researchers. These real-world use cases and applications show current and significant future potential as veterinary informatics grows in importance. Veterinary informatics can forge new possibilities within veterinary medicine and between veterinary medicine, human medicine, and One Health initiatives.

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“…However, little is known about the current analytical practices, software, and data use of state and local health practitioners regarding cluster detection and prediction. Zoonotic disease surveillance has been studied to identify challenges of integrating animal and human zoonotic disease data to assist in the development of biomedical informatics tools using the Delphi method 7 . Here, we elaborate on this type of work to facilitate a greater understanding of viral zoonotic disease surveillance tasks in local health departments including the use of bioinformatics resources.…”

Section: Background and Significancementioning

confidence: 99%

Identifying current and emerging resources and tools utilized for detection, prediction, and visualization of viral zoonotic disease clusters: a Delphi study

Beard

Scotch

2019

JAMIA Open

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Zoonotic disease surveillance presents a substantial problem in the management of public health. Globally, zoonoses have the potential to spread and negatively impact population health economic growth, and security. This research was conducted to investigate the current data sources, analytical methods, and limitations for cluster detection and prediction with particular interest in emerging bioinformatics tools and resources to inform the development of zoonotic surveillance spatial decision support systems. We recruited 10 local health personnel to participate in a Delphi study. Participants agreed cluster detection is a priority, though mathematical modeling methods and bioinformatics resources are not commonly used toward this endeavor. However, participants indicated a desire to utilize preventative measures. We identified many limitations for identifying clusters including software availability, appropriateness, training, and usage of emerging genetic data. Future decision support system development should focus on state health personnel priorities and tasks to better utilize emerging developments and available data.

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State‐Level Zoonotic Disease Surveillance in the United States

Cited by 6 publications

References 8 publications

Systematic review of surveillance systems and methods for early detection of exotic, new and re-emerging diseases in animal populations

Systematic review of surveillance systems and methods for early detection of exotic, new and re-emerging diseases in animal populations

Veterinary informatics: forging the future between veterinary medicine, human medicine, and One Health initiatives—a joint paper by the Association for Veterinary Informatics (AVI) and the CTSA One Health Alliance (COHA)

Identifying current and emerging resources and tools utilized for detection, prediction, and visualization of viral zoonotic disease clusters: a Delphi study

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