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Title: Applying data science advances in disease surveillance and control
Applying data science advances in disease surveillance and control Dr. David S. Ebert and Dr. Aaron Wendelboe explain how a cohesive, multidisciplinary, and multi-tiered approach can support a more predictive model in disease surveillance and control. Public health disease surveillance is being conducted in countless settings, including healthcare, vertebrate and invertebrate animals, wastewater, air quality, transportation, and commercial activities, but, attaining the goal of early disease detection has been somewhat elusive. For instance, one of the few key shortcoming of public health preparedness efforts is the insufficient collaboration between multidisciplinary experts, such as data scientists, computer engineers, anthropologists, social scientists, and systems engineers. To address these gaps in knowledge and preparedness, we are responding in a multi-tiered approach with a One Health perspective that will be economically feasible and sustainable. The authors have also engaged a broad set of stakeholders, created broad multidisciplinary teams, are combining relevant data sources in innovative ways that will serve as early indicators, are using advanced technologies for early diagnosis, and advancing analytic methods to maintain high specificity for true event identification.  more » « less
Award ID(s):
2200299
PAR ID:
10435533
Author(s) / Creator(s):
Date Published:
Journal Name:
Open Access Government
Volume:
39
Issue:
1
ISSN:
2516-3817
Page Range / eLocation ID:
152 to 153
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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