Taking flight with Precision Global Health: a scoping review on avian influenza
Review Article

与全球精准医疗共同起航：禽流感范围界定检索

Nefti-Eboni Bempong1, Rafael Ruiz De Castañeda1, Damien Dietrich1,2,3, Isabelle Bolon1, Antoine Flahault1

1Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland; 2eHealth and Telemedicine Division, Geneva University Hospitals, Geneva, Switzerland; 3Radiology and Medical Informatics Department, Faculty of Medicine, University of Geneva, Geneva, Switzerland

Contributions: (I) Conception and design: A Flahault, R Ruiz De Castañeda; (II) Administrative support: I Bolon, NE Bempong; (III) Provision of study materials or patients: A Flahault, NE Bempong, R Ruiz De Castañeda, D Dietrich; (IV) Collection and assembly of data: NE Bempong, R Ruiz De Castañeda, D Dietrich; (V) Data analysis and interpretation: NE Bempong, R Ruiz De Castañeda, D Dietrich, I Bolon; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

Correspondence to: Nefti-Eboni Bempong. Institute of Global Health, Faculty of Medicine, University of Geneva, Campus Biotech, Chemin des Mines 9, 1202, Geneva, Switzerland. Email: nefti-eboni.bempong@unige.ch.

摘要：禽流感是由甲型流感病毒引起的禽类感染，由于活禽市场环境拥挤和职业暴露，甲型流感病毒已跨越物种壁垒传染给人类。因预计病死率高达60%，故绘制可用于改善禽流感疾病监测和健康结果的现有数字技术地图至关重要。本次范围界定审查旨在确定哪些数字技术可以改善禽流感的预防、检测和控制，并可作为增强卫生系统的基础。在 PubMed 和 Web of Science 上检索关于数字技术使应用于禽流感的使用情况的研究，检索时间范围从 2009 年（一月）到 2017 年（七月）。数据被提取总结为图表、使用 EndNote 软件管理的引文以及通过使用图文方式对数字技术领域进行分组。范围界定检索确定了 111 项相关研究，并揭示了涉及诊断工具的数据建模（n=72）和新技术（n=15），它们是应对禽流感最常用的技术。数据建模领域的很大一部分受到计算机辅助数学建模 (n=42) 的影响，包括数学建模 (n=8)、仿真建模 (n=14) 和时空建模 (n=20)，主要应用于根据迁徙模式和传播动态评估疫情分布。本次综述的一个主要挑战是家禽市场的生物安全措施不佳。数字技术显示出改善禽流感检测、控制和预防的潜力，特别是通过使用气象数据集的数据建模。当然，为了最大限度地发挥这些数字技术的作用，在亚洲等受灾严重的地区更好地实施生物安全措施是非常必要的。

关键词：禽流感； 高致病性禽流感（HPAI）； 人畜共患病； 数字技术；移动健康；遥感技术； 造型；新技术； 大数据与疾病监测

Received: 30 May 2018; Accepted: 01 June 2018; Published: 30 June 2018.

doi: 10.21037/jphe.2018.06.01

介绍

Avian Influenza也称为“禽流感(Bird Flu)“, 是由将动物种群与人类联系起来的主要病毒之一的病毒引致，预计病死率为 60% [1]。禽流感病毒的遗传物质为单链 RNA[2]。这些病毒可以根据它们引发疾病的严重程度进一步细分为两组，即高致病性禽流感 (HPAI) 和低致病性禽流感 (LPAI)（同上）。每种病毒都包含一个H和一个N抗原，已知H5和H7毒株会引发HPAI。有假设认为HPAI 是 LPAI引发的由有缺陷的聚合酶复合体导致的自发性突变[3]。虽然后者被认为是最易被观察到的病变机制，但研究也发现了其他的替代路径，包括核苷酸替换和与其他基因重组而导致的HPAI 出现（同上）。

禽流感的天然宿主为水鸟，主要通过感染家禽和水禽，以及其他鸟类进行传播。该病毒可以通过粪/口途径传播，也可以通过陆地鸟类的呼吸途径传播[2]。家禽的禽流感爆发尤其令人担忧，因为它有可能从 LPAI 演变为 HPAI，并且 HPAI 造成的家禽死亡与经济损失和贸易限制相关[4]。在动物中，HPAI可能产生鸡群突然出现的高死亡率，与头部和面部水肿、头部皮下出血以及产卵停止等临床症状相关（同上）。然而，最严重的公共卫生问题是禽流感病毒可以传播给人类。疾病或病毒从动物传染给人类（反之亦然）的过程称为人畜共患病[1]。人类感染H5N1型禽流感的首例病例可追溯到 1997年，中国香港共发生了18例确诊病例和6例死亡病例，突显了禽流感大流行的可能性[5]。禽流感也曾发生人传人情况，2015年中国大陆报告了超过200例的H7N9型禽流感病例[6]。动物到人类的传播路径通常是通过受污染的环境或中间宿主而传播，例如猪的屠宰过程中直接接触可能会发生传播[6]。病毒在亚洲具备跨越物种传播的条件，在某些地区，家禽、猪和人类共同生活在狭小的环境中，并且会通过活禽市场发生职业暴露[3]。

为了减少禽流感传播，人们已经采取了多种预防和控制措施。降低动物种群感染风险对降低人类感染风险至关重要[1]。包括世界卫生组织（WHO）、联合国粮食与农业组织（FAO） 和 世界动物卫生组织（OIE） 在内的三方合作制定了相应的指南，用于最常见的禽流感预防和控制[7]，指南包括：鸟类种群的疫苗接种、OIE 陆生动物卫生法[8]等法律和生物安全措施。生物安全措施，是指可用于防止禽流感传入易感家禽的物理和/或程序措施[2]。现有的预防和控制策略可以通过数字技术来加强，数字技术可以定义为数字资源，用于从人群中收集新的个人或环境数据（人与动物），数字技术包括但不限于：移动医疗、大数据和遥感技术。例如，时空模型可用于更精确地估计疫情分布，通过卫星遥感技术可以深入了解人类社会及行为模式等。因此，数字技术在加强禽流感的预防和控制工作上展示了巨大的潜力。

研究目的和研究问题

本次范围界定综述的目的是检索关注数字技术和禽流感防控的现有研究，并进一步探索他们在加强禽流感监测的可能。本次范围界定综述旨在回答以下问题：目前有哪些数字技术被用于改进和增强禽流感的检测、控制和预防工作？

研究方法

范围检索旨在“形成知识综述，解决探索性研究问题。通过系统地检索、选择及综合现有研究成果来绘制与特定领域或相关研究中的关键概念、证据类型及差异”[9]。本研究旨在绘制用于应对禽流感的现有数字技术的应用地图。

检索策略

检索策略由三位作者共同制定，包括数字技术和禽流感相关的广泛术语，文本和 MeSH 术语的组合（见附件）。

关键词

用于识别使用数字技术与禽流感防控相关的文献。与疾病相关的关键词（禽流感）, 使用来自国家医学图书馆MeSH数据库的MeSH词组及其附属目录同义词进行识别，而与数字技术相关的关键词则通过初步文献筛选的关键词进行识别。再结合疾病相关关键词和数字技术的相关词组，在高级搜索设置中进行筛选（详见附件表S1），例如以下关键词包含但不限于：[(Avian Influenza) 或 (HPAI) 或 (H5N1)] 和 [(Technology) 或 (Big data) 或 (Social media) 或 (mHealth)]，用于筛选相关文献。此外，还检索了已筛选出研究的参考文献以收集更多相关资料。

数据库

为了确保对文献进行更全面地检索，综述中包括了两个数据库，即 PubMed 和 Web of Science。利用滚雪球方法和手动检索首先识别出的文献，再从已标记文献数据库中检索出其他的相关文献。

研究的选择、纳入和排除标准

本次综述检索了所有与利用数字技术改善禽流感防控相关结果的研究。本研究也检索了同行评审文献（包括原始的定量和定性研究），以及在 PubMed 和 Web of Science 中编入索引的社论、观点和信件。文献须在 2009 年（一月）和 2017 年（七月）之间以英文、西班牙文、法文或德文发表。对相关研究的地理位置、人种或研究设计没有设限。本次研究排除了重复研究、上述指定出版语言以外的研究，以及以兽医为重点、反对或与公共卫生无关的文献，以及没有明确关于数字技术的研究。

数据的收集和整理

两位作者根据研究标题和摘要与本研究的相关性独立制定收录和排除标准。之后两位作者对筛选出的文献列表合并去重，讨论收录或排除的基本原则，然后从两位审稿人先前制作的两个列表中选择纳入本次综述的选文列表。此外，第三位作者也参与了文献筛选过程，并仔细检索了最终入选的清单。之后获取全文文章并将符合条件的研究总归纳到到描述性总结表中，关注以下关键信息：作者、发表日期、发布期刊、区域、作者单位、数字技术/设备、功能、研究设计、数据源、目标人群、健康指标和挑战。并请留意，数字技术是根据以下指定的研究领域进行分组，按出现频率创建（见表1）。引文使用 EndNote 软件进行管理。

表1
表1 数字技术领域。[1], Stuart J, Barker A. (2013). Undefined by data: A survey of Big Data definitions. Available online: https://arxiv.org/pdf/1309.5821.pdf,last accessed 21/08/2017. [2], World Health Organisation. (2011). mHealth: new horizons for health through mobile technologies. Available online: http://www.who.int/goe/publications/goe_mhealth_web.pdf, last accessed 21/08/2017. [3], Squires H, Tappenden P (2011). Mathematical modelling and its application to social care. National Institute for Health Research: Methods Review. Available online: http:// eprints.lse.ac.uk/41192/1/SSCR_Methods_Review_7_web_2.pdf, last accessed 11/06/2018. [4], Oxford University Press, 2001. Oxford Dictionaries. Available online: https://en.oxforddictionaries.com/definition/novel, last accessed 21/08/2017. [5], Graham S. (1999). Remote sensing. Available online: https://earthobservatory.nasa.gov/Features/RemoteSensing/, last accessed 21/08/2017.
Full table

数据合成

使用文字叙述和图形的组合来合成数据，以对调查结果进行总结性描述。此外，本研究创建了作者国籍的相关性网络，可视化界定学术界中的数字创新研究中心。在作者的国籍关系网络中，每个圆圈的半径反映了每个国家/地区的出版物数量，边缘的颜色取决于它们来自哪个大洲，国家之间的联系代表国家之间的不同合作（见图1）。该图通过在第一作者和其他每个作者之间添加一条连线绘制出的。

图1
图1 本研究检索文献的作者从属网络 (n=111)

结果

主要发现

通过标题和摘要筛共选出1,753篇文章，其中694篇被确认为相关研究，191篇因重复研究被排除，392篇不符合纳入标准，因此最终筛选出 111 项研究纳入综述（见图2）。 综述中纳入的研究确认了用于应对禽流感防控的数字技术或设备。检索的 111 项研究主要围绕五个数字技术领域，即大数据、移动医疗、数据建模、新技术和遥感技术（见表1）。大多数研究发表于 2016 年，占总出版研究数的21%。亚洲的相关研究最多（57%），而南美洲则没有符合纳入标准的已发表文献。

图2
图2 研究的筛选流程

使用作者单位隶属网络对通过以国家/地区划分的现有研究成果进行了可视化（见图1）。最多相关研究的是美国，与中国和比利时有着密切联系。其他主要贡献者位于欧洲，比如比利时、法国、意大利和英国。许多文献引用了比利时国家科学研究基金和布鲁塞尔大学的生物控制和空间生态学部门，以及粮农组织、意大利动物卫生服务机构的EMPRES野生动物部门资源，这可能解释了比利时和意大利作出的的较大贡献 。所选文章中的许多作者所在的国家/地区都隶属于亚洲国家的机构，例如越南、印度、韩国、孟加拉国、日本和柬埔寨，而这些国家正是受禽流感影响较为严重的国家。

数据建模

数据建模占综述中研究的65%，涵盖从计算机辅助数学建模到时空建模（见图1，图3）。数学建模包括基于蒙特卡罗模拟、贝叶斯概率和物种分布模型的研究，主要用于估计禽流感爆发分布、预测宿主一病毒相互作用，以及更准确地研究通过各种场景（含活禽市场）的传播和控制动态。此外，通过物种生态位建模和使用气象数据集来预测和绘制疾病发生概率高的区域，模型产生了更多的生态焦点。

图3
图3 按数字技术领域划分的出版物数量：2009-2017年

新技术

综述中发现新技术占总文献筛选结果的13%，包括特定病例的诊断设备 (67%) 纳米技术 (26%) 和可穿戴设备 (7%)。新技术主要用于监测目的，而其中一项值得关注的研究是利用纳米技术进行治疗。

大数据

本次综述选出了十项 (10%) 专注于大数据领域的研究，这些研究可以进一步细分为社交媒体分析 (40%)、基于网络的监控平台 (40%) 和在线学习资源 (20%)。社交媒体平台被用来以衡量用户参与度和健康传播活动来捕捉和告知用户的行为变化。大数据平台也被用于以网络监控的形式收集信息，同时也支持在线学习。

遥感技术

少数研究探索了遥感技术(7%)，在卫星遥测和卫星图像的参数下，捕捉候鸟种群如何与其处的环境相互作用，并通过地球卫星观测识别受污染的水体。

移动医疗

一小部分研究致力于移动医疗领域，占本次检索结果的5%。移动医疗主要用作监控系统的一部分，通过短信（SMS）或呼叫功能发出报告，以及GPS技术来追踪医护人员和病例。同时，手机等移动设备也被用于诊断目的。

本次检索中认定的数字技术主要用于疫情监控 (83%)，部分专门用于诊断(16%)，而较少应用于治疗 (1%)。 在监控职能中，数据建模仍占据主导地位，而诊断目的主要依赖新技术和移动医疗（见表2）。虽然大数据相关研究数仅排在本次检索中的第三位（9%），但它在多数据源结合方面显示出巨大的潜力。

表2
表2 Digital technology domain by function
Full table

讨论

本次综述确定了用于应对禽流感的数字技术，其中数据建模占65%，在计算机辅助数学建模参数下，时空建模结合 GPS 和 GIS 功能使用得最多。就禽流感而言，数字技术在通过使用气象数据源跟踪迁徙路线和识别水库来预测潜在爆发热点方面特别有用 [55]。大多数技术用于监测功能，很少用于诊断或治疗目的。尽管禽流感主要影响亚洲国家，但在除中国外的更多北方地区观察到较高的研究产出，中国的研究产出排名第二（见图1）。

在本次综述中数据建模是在最多研究中应用的数字技术，模型能够预测疾病热点的重要变量，几项研究报告指出家禽市场密度和人口密度是所述模型中最重要的预测变量 [28,35,64,80]。时空建模技术还与全球导航卫星系统功能（例如 GIS 和 GPS（24%））相结合，通过绘制鸟类种群的迁徙路线图来确定传播途径和爆发热点[31,71]。数据建模中包含的许多研究都以生态为重点，对与爆发相关的迁移模式进行建模 [28,29,38,40,41,57,64,79,80]。One Health的方法也被纳入模型，通过对物种跨越建模和评估人类感染风险而被注意 [31,57,67,81]。

新技术主要用于诊断目的，包括诊断设备，如具备数十秒内检测目标分子能力的数字微流控设备[99]、RNAi抗病毒载体技术[100]和便携式侧向流装置 [104]。可穿戴传感信号是针对特定情况设定的，可对家禽进行持续监控，并在检测到鸡的异常状态时通过互联网向管理人员发出警报[110]。一项值得注意的研究被归类到治疗领域，该研究通过在鸡身上使用纳米平台的新型疫苗，研究表明与未接种疫苗的鸡相比，接种疫苗的鸡的IgG反应增加[98]。

大数据领域研究主要由社交媒体分析组成，其中包括对 Twitter 等平台的内容挖掘，以及分别参考百度指数和微博的特定国家的搜索引擎和博客 [11,15]。 这些社交媒体平台旨在通过健康交流和增加用户参与来影响行为改变。综述中的一个突出主题是通过基于网络的讨论，使用数据收集，展示参与方法和协作精神。例如，在线数据平台 CaribVnet 和 f-FLUA2H 都分别从普通人群和疾病专家处收集了有关禽流感的信息 [12,14]。 然而，这些平台普遍存在的一个困难是数据质量，这可能因一般人群的成员而异。值得注意的是，大数据也是通过在线学习工具使用和生成的。例如，为专注于禽流感的兽医量身定制的数字学习工具取得了巨大成功，90.2%的参与者认为在线课程有用且方便，97%的参与者希望在他们的职业生涯中应用学到的信息[18]。

遥感和移动医疗领域代表了本次综述中的一部分发现。通过卫星图像的利用，遥感技术在捕捉迁徙模式和潜在热点方面显示出巨大潜力，通过地球卫星观测识别出更多受污染的水体，这些水体充当了禽流感病毒的环境宿主[113,115,116]。此外，遥感技术能够通过迁徙模式记录家禽市场链 [114]。移动医疗主要用于诊断目的，将移动电话设备与成像技术联结起来以形成使用点传感平台 [118]，也有将移动设备与荧光技术相结合，用于基于智能手机的荧光诊断系统 [21,22]。

在一些选定的研究[12,79] 中提到目前禽流感防控主要的一个挑战是生物安全措施不佳。例如，加勒比地区和亚洲国家的家禽以自由放养方式为主，在这些国家和地区中，活禽交易市场是很常见的，由于目标是产生销售利润，导致环境卫生的标准清洁通常缺乏严格的监控。此外，考虑与禽流感相关的经济影响也很重要，主要是指活禽/鸟类交易市场和贸易动态。随着对家禽的需求增加，家禽密度和贸易活动也增多了，从而增加了病毒传播的可能性[119]。而嵌在经济影响因素里的是与更多文化习俗相关的潜在人为因素，例如农历新年的庆祝活动。最近的一项研究发现，农历新年期间禽肉消费量从 4.3 倍增加到9.6倍，加剧了需求增加、家禽密度增加以及病毒传播风险增加的循环[6]。

需要注意的是，本综述也存在研究方法的局限性，主要为仅检索了两个数据库（PubMed 和 Web of Science），因此无法涵盖所有相关研究，同时存在发表偏倚。此外，在整个文献收录过程中，因主要关注季节性或流行性感冒，而非禽流感，在整个研究筛选过程中排除了大量文献，这可能是由于在检索关键词中包含了“H2N2”（见附件表S1）。因包含检索关键词导致了部分非标准文献被收录，尤其是综述类文章，从整体上讨论了流感（包括禽流感），尽管大多数结果都集中在大流行性流感上，但也检索出并纳入了有关禽流感和人畜共患病的研究类别里。

结论

数字技术显示出了在改善禽流感检测、控制和预防工作上的潜力。范围界定审查绘制了用于抗击禽流感的现有数字技术，并发现了五个主要的数字领域，包括：移动医疗、大数据、数据建模、遥感和新技术。结果表明数据建模是最常使用的技术，主要用于监测目的。就相关研究产出数量而言，数字技术创新的主要中心包括美国、比利时和中国，这可能是由于资金和高疾病流行率的原因。值得关注的是，尽管通过将计算机辅助模拟与气象和遥感数据集相结合，建模方法已经取得了进步，但仍需要更多创新的方法来发挥其他现有技术的潜力。找到将这些技术结合起来以改进禽流感的治疗和诊断程序的方法仍然至关重要。

Supplementary

表S1

检索策略索引

Domain related search terms Search strategy syntax
Digital technology “Digital” OR “Technology” OR “Precision medicine” OR “Biosensor” OR “Sensors” OR “Bio-surveillance” OR “Intelligent surveillance” OR “Participatory surveillance” OR “Genomic epidemiology” OR “Genomic sequencing” OR “Pathogen genomics” OR “Big data” OR “Data storage” OR “Data science” OR “Information processing” OR “Blockchain” OR “Social media” OR “Twitter” OR “Facebook” OR “Instagram” OR “Flicker” OR “YouTube” OR “Wikipedia” OR “Telemedicine” OR “Robotics” OR “Machine learning” OR “Modelling” OR “Mathematical modelling” OR “Spatiotemporal modelling” OR “Mapping” OR “mHealth” OR “Mobilephone” OR “Smartphone” OR “Cellphone” OR “Phone” OR “Cell phone technology” OR “Mobile data” OR “Mobile application” OR “Devices” OR “Connected device” OR “Internet” OR “Web-based” OR “Internet-based” OR “Web-database” OR “Cloud” OR “Cloud-based” OR “eHealth” OR “E-learning” OR “Game-based learning” OR “Augmented reality” OR “Massive Online Open Courses” OR “MOOC” OR “Virtual learning” OR “Virtual reality” OR “Online learning” OR “Gaming technology” OR “Serious game” OR “Crowd sourcing” OR “Citizen Science” OR “Connected device” OR “Remote-sensing technology” OR “Satellite” OR “GPS” OR “Global Positioning System” OR “Geographic Information System” OR “Drones” OR “GIS” OR “Spatial” OR “Participatory” OR “Sensor” OR “App” OR “Artificial intelligence” OR “Tracking” OR “Mapping” OR “Biogeography” OR “Biomarkers” OR “Disease mapping”
Avian Influenza “Influenza in Birds” OR “Influenza, Avian” OR “Fowl Plague” OR “Fowl Plague Virus” OR “Avian Flu” OR “Avian Influenza” OR “Influenza A Virus” OR “Influenza Viruses Type A” OR “Orthomyxovirus Type A” OR “Orthomyxovirus Type A, Avian” OR “Avian Orthomyxovirus Type A” OR “Pestis galli Myxovirus” OR “Myxovirus pestis galli” OR “A (H5N1)” OR “A (H7N9)” OR “A (H9N2)” OR “A (H1N1)” OR “A (H2N2)”OR “Bird Flu”

Acknowledgments

We would like to acknowledge the Institute of Global Health, Faculty of Medicine at the University of Geneva who supported this work. We would also like to acknowledge and thank Sharada Prasanna Mohanty for his contribution through the production of the authors’ affiliation network.

Funding: None.

Footnote

Provenance and Peer Review: This article was commissioned by the Guest Editor (Mohamed H. Ahmed, Heitham Awadalla and Ahmed O. Almobarak) for the series “The Role of Sudanese Diaspora and NGO in Health System in Sudan” published in Journal of Public Health and Emergency. The article has undergone external peer review.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/jphe.2018.06.01). The series “Precision Infectious Disease Epidemiology” was commissioned by the editorial office without any funding or sponsorship. AF serves as an unpaid editorial board member of Journal of Public Health and Emergency from Apr 2018 to Mar 2020 and served as the unpaid Guest Editor of the series. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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译者介绍
钟韵
毕业于中央民族大学（985,211）生物科学，硕士2012年毕业于香港大学李嘉诚医学院，公共卫生硕士（MPH），研究方向为健康经济政策与流行病学。毕业后在香港从事医疗市场和医疗投资工作。2020年参与哈佛大学公共卫生学院Global Health Intensive Program项目,与全球公共卫生领域专家与青年学者共同参与新冠肺炎疫情全球控制和全球健康应对政策讨论。（更新时间：2021/9/10）

（本译文仅供学术交流，实际内容请以英文原文为准。）

doi: 10.21037/jphe.2018.06.01
Cite this article as: Bempong NE, Ruiz De Castañeda R, Dietrich D, Bolon I, Flahault A. Taking flight with Precision Global Health: a scoping review on avian influenza. J Public Health Emerg 2018;2:21.

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