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Frontiers _ Big Data’s Role in Precision Public Health _ Public Health.htm
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Frontiers _ Big Data’s Role in Precision Public Health _ Public Health.htm
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<!DOCTYPE html>
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<title>Frontiers | Big Data’s Role in Precision Public Health | Public Health</title>
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<meta property="og:description" name="Description" content="Abstract: Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous sub-populations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.">
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<meta name="citation_volume" content="6">
<meta name="citation_journal_title" content="Frontiers in Public Health">
<meta name="citation_publisher" content="Frontiers">
<meta name="citation_journal_abbrev" content="Front. Public Health">
<meta name="citation_issn" content="2296-2565">
<meta name="citation_doi" content="10.3389/fpubh.2018.00068">
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<meta name="citation_language" content="English">
<meta name="citation_title" content="Big Data’s Role in Precision Public Health">
<meta name="citation_keywords" content="Precision public health; big data; Computational epidemiology; Infectious disease surveillance; precision population health">
<meta name="citation_abstract" content="Abstract: Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous sub-populations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.">
<meta name="description" content="Abstract: Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous sub-populations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.">
<meta name="citation_online_date" content="2018/02/20">
<meta name="citation_date" content="2018">
<meta name="citation_publication_date" content="2018/03/07">
<meta name="citation_pdf_url" content="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/pdf">
<meta name="citation_author" content="Dolley, Shawn">
<meta name="citation_author_institution" content="Cloudera, Inc., United States">
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<meta name="Keywords" content="Precision public health, big data, Computational epidemiology, Infectious disease surveillance, precision population health">
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<li><a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#h1">Abstract</a></li>
<li><a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#h2">Introduction</a></li>
<li><a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#h3">Methods</a></li>
<li><a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#h4">Precision Public Health</a></li>
<li><a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#h5">Big Data in Healthcare and Public Health</a></li>
<li><a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#h6">Big Data in Precision Public Health</a></li>
<li><a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#h7">Performing Disease Surveillance and Signal Detection</a></li>
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<h2>Review ARTICLE
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Front. Public Health, 07 March 2018
| <a href="https://doi.org/10.3389/fpubh.2018.00068">https://doi.org/10.3389/fpubh.2018.00068</a>
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<a id="h1" class="reset-hash-position"></a><h1>Big Data’s Role in Precision Public Health</h1>
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<a style="text-decoration:none;line-height:1.3em;" href="http://www.frontiersin.org/people/u/419625" class="user-id-419625"><img class="pr5" src="./Frontiers _ Big Data’s Role in Precision Public Health _ Public Health_files/24(5)" onerror="this.src='https://f96a1a95aaa960e01625-a34624e694c43cdf8b40aa048a644ca4.ssl.cf2.rackcdn.com/Design/Images/newprofile_default_profileimage_new.jpg'" alt="image">Shawn Dolley</a>*
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<li class="pl0">Cloudera, Inc., Palo Alto, CA, United States</li>
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<p>Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.</p>
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<a id="h2" class="reset-hash-position"></a><h2>Introduction</h2>
<p class="mb0">This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts. This article focuses on surveying current practice, with a breadth of examples. The article does not include a critical review of the methods included in the big data and precision public health published research. It is hoped this article may pave the way for future researchers to measure the strengths and weaknesses, robustness, and validity of individual studies, interventions and outcomes. With the breadth of practice defined here, such follow-on in-depth critical review could identify precision public health best practices in design, methods, implementation, and analysis.</p>
<a id="h3" class="reset-hash-position"></a><h2>Methods</h2>
<p class="mb15">The terms “big data” and “precision public health”—two relatively new disciplines—often do not appear in the nomenclature of contemporary public health interventions and studies. Searching for the terms “big data” or “precision public health” returns a small fraction of the actual activity. Based on the lack of existing reviews and the complexity in identifying the intersection of precision public health and big data, the rationale of this narrative review article is to find examples of the use of big data in implementations of precision public health published in peer-reviewed academic journals. The author (a) reviewed a large number of public health studies to look for precision and big data, as well as related and follow-on studies, (b) identified and searched for specific types of big data being applied to public health, and (c) searched for uses of data in precision public health to identify big vs. small data—always using the definition of these terms rather than relying on the presence of the terms “big data” or “precision public health.”</p>
<p class="mb15">Searches were performed using Google Scholar and Google. Examples of public health implementations—with and without big data—and precision public health implementations—with and without big data—only qualified for this article if they were published in peer-reviewed journals. In the presence of multiple qualifying examples, best attempts were made to limit examples to a single citation. In the presence of multiple examples, to reduce risk of bias and attempt to identify the most robust examples, the examples selected were those with the (a) most clearly identifiable public health use case, (b) clearest use of big data, (c) most “precision,” (d) in journals with the highest impact factor, that were (e) the most recent—and in that order of priority. Searches were concluded by July 20, 2017.</p>
<p class="mb15">Search terms used were as follows:</p>
<p class="mb0" style="margin-left:1.2em; text-indent:-1.2em;">1. For identifying implementations using big data volume, the term “public health” and each of the following: “big data,” “gene-wide,” “genome,” “genomic,” “germline,” “GWAS,” “imaging,” “molecular,” “multi-omic,” “pan-omic,” “phenome,” “PWAS,” “translational,” “video,” “whole exome,” and “whole genome.”</p>
<p class="mb0" style="margin-left:1.2em; text-indent:-1.2em;">2. For identifying implementations using big data variety, the term “public health” and each of the following: “big data,” “drone,” “Facebook,” “Instagram,” “IoT,” “internet of things,” “linked,” “linked data,” “patient-centered,” “patient generated,” “mobile,” “mobile phone,” “registry,” “registries,” “secondary use,” “semantic,” “sensors,” “social media,” “surveys,” “Twitter,” “UAV,” “unmanned aerial vehicle,” “variety,” and “wearable.”</p>
<p class="mb0" style="margin-left:1.2em; text-indent:-1.2em;">3. For identifying implementations using big data velocity, the term “public health” and each of the following: “big data,” “continuous,” “monitor,” “real-time,” “sensor,” “streams,” “streaming,” “velocity,” and “video.”</p>
<p class="mb0" style="margin-left:1.2em; text-indent:-1.2em;">4. For identifying public health implementations—including programs, trials, innovations and experiments—using big data, the term “big data” and each of the following: “adverse drug event,” “ADE,” “adverse event,” “cohort,” “epidemic,” “epidemiology,” “health intervention,” “health risk,” “heterogeneous,” “homogeneous,” “human movement,” “outcomes,” “pandemic,” “pharmaco-epidemiology,” “population health,” “precision public health,” “prevention,” “public health,” “signal detection,” “surveillance,” “targeted intervention,” “tracking,” “vaccine,” “vector,” and “virus.”</p>
<p class="mb15">Google Scholar also provides lists of more recent studies which have cited the current study. These lists were reviewed to identify if more recent studies existed that provided better examples of pertinent characteristics.</p>
<p class="mb0">This method has a number of limitations. Google Scholar has limitations, including relying on the end user to discriminate which studies returned are from peer-reviewed journals. No review protocol exists independent of this review article. No study selection or summary measures were collected, and no meta-analysis was performed. No study characteristics were collected. No assessment of the validity of included studies was performed beyond their inclusion in peer-reviewed academic journals. No assessment of cumulative level bias risk was performed. No additional analysis methods were used. The selection of studies included was not independently reviewed. The scope of this narrative review precludes enumerating additional limitations. Limitations aside, the result of these methods is a collection of studies or programs where big data and precision public health—as these terms are defined in this article—are being used together. Through implementing these methods, this review article is the first to identify the scope and scale of big data’s role in precision public health, highlight classes of innovation, and identify the risks of using big data in this field.</p>
<a id="h4" class="reset-hash-position"></a><h2>Precision Public Health</h2>
<p class="mb15">“Precision public health is a new field driven by technological advances that enable more precise descriptions and analyzes of individuals and population groups, with a view to improving the overall health of populations” (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B1">1</a>). The term was coined in Australia by Dr. Tarun Weeramanthri in 2013, and first found in print in 2014 (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B2">2</a>). Dr. Muin Khoury and Dr. Sandro Galea describe precision public health as “improving the ability to prevent disease, promote health, and reduce health disparities in populations by applying emerging methods and technologies for measuring disease, pathogens, exposures, behaviors, and susceptibility in populations; and developing policies and targeted implementation programs to improve health” (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B3">3</a>). Precision public health leverages big data and its enabling technologies to achieve a previously impossible level of targeting or speed (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B4">4</a>). The Bill & Melinda Gates Foundation adds that precision public health “requires robust primary surveillance data, rapid application of sophisticated analytics to track the geographical distribution of disease, and the capacity to act on such information” (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B5">5</a>). Precision public health works because “more-accurate methods for measuring disease, pathogens, exposures, behaviors, and susceptibility could allow better assessment of population health and development of policies and targeted programs for preventing disease” (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B4">4</a>). Arnett & Claas add “Precision public health is characterized by discovering, validating, and optimizing care strategies for well-characterized population strata” (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B6">6</a>). As for the size of the strata, Colijn et al. state “precision approaches must act at the right scale, which will often be intermediate—between “one size fits all” medicine and fully individualized therapies” (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B7">7</a>).</p>
<p class="mb0">The prominence of the term “precision” in the new practices of precision medicine and precision public health will invariably raise questions about their similarity. While precision medicine requires genetic, lifestyle, and environmental data to meet goals of more customized and potentially individualized clinical treatments, precision public health is about increased accuracy and granularity in defining public cohorts and delivering target interventions of many types (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B4">4</a>–<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B6">6</a>). Precision medicine and precision public health are independent.</p>
<a id="h5" class="reset-hash-position"></a><h2>Big Data in Healthcare and Public Health</h2>
<p class="mb15">Big data has recently become a ubiquitous approach to driving insights, innovation and new interventions across economic sectors (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B8">8</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B9">9</a>). The United States National Institute of Standards and Technology defines big data as follows: “Big Data consists of extensive datasets—primarily in the characteristics of volume, variety, velocity, and/or variability—that require a scalable architecture for efficient storage, manipulation, and analysis,” (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B10">10</a>). Decreases in costs of technology enabled the big data phenomenon to emerge (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B11">11</a>). Data of “such a high volume, velocity and variety to require specific technology and analytical methods for its transformation into value” has a symbiotic relationship with the technology innovation on which it relies; the term big data often conflates the actual physical data with the unique technologies required to use it (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B12">12</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B13">13</a>).</p>
<p class="mb15">In patient-specific healthcare, big data technology has helped enable greater scales of volume, variety and velocity (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B14">14</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B15">15</a>). Usable data <i>volume</i> has significantly increased in areas such as genomics (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B16">16</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B17">17</a>), molecular research (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B18">18</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B19">19</a>), medical image mining (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B20">20</a>), and population health (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B21">21</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B22">22</a>). Enabling a <i>variety</i> of data to be integrated, for a more complete view of patient or population, has occurred in areas including air quality (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B23">23</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B24">24</a>), wearables (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B25">25</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B26">26</a>), patient generated content <i>via</i> the web (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B27">27</a>), patient or physician movement (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B28">28</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B29">29</a>), medical studies (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B30">30</a>), and critical care (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B31">31</a>). Big data enabling increased <i>velocity</i> in healthcare was one of the earliest uses, in areas such as clinical prediction (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B32">32</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B33">33</a>), and diagnostics (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B15">15</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B33">33</a>). Current examples and future vision for use of big data exists in multiple and varying pathologies, including cancer (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B34">34</a>), cardiology (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B35">35</a>), epilepsy (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B36">36</a>), family medicine (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B37">37</a>), gastroenterology (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B38">38</a>), nursing (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B39">39</a>), pediatric ophthalmology (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B40">40</a>), psychiatry (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B41">41</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B42">42</a>), and women’s health (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B43">43</a>) as examples.</p>
<p class="mb0">Barrett et al. state succinctly: “Big data can play a key role in both research and intervention activities and accelerate progress in disease prevention and population health” (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B44">44</a>). Big data shows utility across the entire spectrum of public health disciplines. This capability ranges from “monitoring population health in real-time” to building “definitive extents and databases on the occurrence of many diseases” (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B45">45</a>). Public health subject areas that include examples of the use of big data include community health (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B46">46</a>), environmental health science (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B24">24</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B47">47</a>), epidemiology (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B48">48</a>), infectious disease (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B45">45</a>), maternal and child health (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B49">49</a>), occupational health and safety (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B50">50</a>), and nutrition (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B51">51</a>). There is optimism and evidence for big data’s value in public health, both in research and in intervention (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B52">52</a>).</p>
<a id="h6" class="reset-hash-position"></a><h2>Big Data in Precision Public Health</h2>
<p class="mb0">Today, use of big data has been shown to improve precision in select disciplines of public health. These areas include performing disease surveillance and signal detection (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B53">53</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B54">54</a>), predicting risk (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B55">55</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B56">56</a>), targeting interventions (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B6">6</a>), and understanding disease (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B57">57</a>). Research and proofs-of-concept with this data for these applications have been performed around the world. With the pace of technology innovation, and the speed at which precision health practitioners have embraced big data, there will likely be more public health disciplines, practices, approaches, and interventions implemented in the future or that are beyond the scope of this article (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B58">58</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B59">59</a>).</p>
<a id="h7" class="reset-hash-position"></a><h2>Performing Disease Surveillance and Signal Detection</h2>
<p class="mb15">Disease surveillance and signal detection are among the most commonly cited and revolutionary of the big data use cases in precision public health (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B45">45</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B60">60</a>–<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B62">62</a>). Precision signal detection or disease surveillance using big data has shown efficacy in air pollution (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B23">23</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B24">24</a>), antibiotic resistance (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B63">63</a>), cholera (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B64">64</a>), dengue (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B65">65</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B66">66</a>), drowning (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B67">67</a>), drug safety (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B68">68</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B69">69</a>), electromagnetic field exposure (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B70">70</a>), Influenza A H1N1 (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B71">71</a>), Lyme disease (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B72">72</a>), monitoring food intake (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B73">73</a>), and whooping cough (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B74">74</a>).</p>
<p class="mb15">Disease surveillance often includes tracking affected individuals, i.e., human carriers, patients, or victims (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B75">75</a>). Stoddard et al. stated in 2009: “Human movement is a critical, understudied behavioral component underlying the transmission dynamics of many vector-borne pathogens” (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B76">76</a>). In the effort to track disease spread by human vectors, a premium is placed on information that is more recent and granular (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B77">77</a>, <a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B78">78</a>). Thus, access to huge volumes of streaming real-time data generated by humans seems at once an ideal signal repository for identifying and tracking affected individuals, and definitionally big data (<a href="https://www.frontiersin.org/articles/10.3389/fpubh.2018.00068/full#B78">78</a>).</p>