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数字工具在虫媒病毒监测中的应用:范围综述。

Use of Digital Tools in Arbovirus Surveillance: Scoping Review.

机构信息

Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.

Instituto Brasileiro de Geografia e Estatística, Rio de Janeiro, Brazil.

出版信息

J Med Internet Res. 2024 Nov 18;26:e57476. doi: 10.2196/57476.


DOI:10.2196/57476
PMID:39556803
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11612576/
Abstract

BACKGROUND: The development of technology and information systems has led to important changes in public health surveillance. OBJECTIVE: This scoping review aimed to assess the available evidence and gather information about the use of digital tools for arbovirus (dengue virus [DENV], zika virus [ZIKV], and chikungunya virus [CHIKV]) surveillance. METHODS: The databases used were MEDLINE, SCIELO, LILACS, SCOPUS, Web of Science, and EMBASE. The inclusion criterion was defined as studies that described the use of digital tools in arbovirus surveillance. The exclusion criteria were defined as follows: letters, editorials, reviews, case reports, series of cases, descriptive epidemiological studies, laboratory and vaccine studies, economic evaluation studies, and studies that did not clearly describe the use of digital tools in surveillance. Results were evaluated in the following steps: monitoring of outbreaks or epidemics, tracking of cases, identification of rumors, decision-making by health agencies, communication (cases and bulletins), and dissemination of information to society). RESULTS: Of the 2227 studies retrieved based on screening by title, abstract, and full-text reading, 68 (3%) studies were included. The most frequent digital tools used in arbovirus surveillance were apps (n=24, 35%) and Twitter, currently called X (n=22, 32%). These were mostly used to support the traditional surveillance system, strengthening aspects such as information timeliness, acceptability, flexibility, monitoring of outbreaks or epidemics, detection and tracking of cases, and simplicity. The use of apps to disseminate information to society (P=.02), communicate (cases and bulletins; P=.01), and simplicity (P=.03) and the use of Twitter to identify rumors (P=.008) were statistically relevant in evaluating scores. This scoping review had some limitations related to the choice of DENV, ZIKV, and CHIKV as arboviruses, due to their clinical and epidemiological importance. CONCLUSIONS: In the contemporary scenario, it is no longer possible to ignore the use of web data or social media as a complementary strategy to health surveillance. However, it is important that efforts be combined to develop new methods that can ensure the quality of information and the adoption of systematic measures to maintain the integrity and reliability of digital tools' data, considering ethical aspects.

摘要

背景:技术和信息系统的发展给公共卫生监测带来了重要变化。

目的:本范围综述旨在评估现有证据,并收集有关数字工具在虫媒病毒(登革热病毒[DENV]、寨卡病毒[ZIKV]和基孔肯雅热病毒[CHIKV])监测中的使用信息。

方法:使用的数据库包括 MEDLINE、SCIELO、LILACS、SCOPUS、Web of Science 和 EMBASE。纳入标准定义为描述数字工具在虫媒病毒监测中使用的研究。排除标准定义如下:信件、社论、评论、病例报告、病例系列、描述性流行病学研究、实验室和疫苗研究、经济评估研究以及未明确描述数字工具在监测中使用的研究。结果在以下步骤中进行评估:暴发或流行监测、病例跟踪、谣言识别、卫生机构决策、(病例和公报)通信、以及向社会传播信息。

结果:根据标题、摘要和全文阅读筛选,共检索到 2227 篇研究,其中 68 篇(3%)研究被纳入。虫媒病毒监测中使用最频繁的数字工具是应用程序(n=24,35%)和 Twitter,现称为 X(n=22,32%)。这些主要用于支持传统监测系统,加强信息及时性、可接受性、灵活性、暴发或流行监测、病例检测和跟踪以及简单性等方面。使用应用程序向社会传播信息(P=.02)、通信(病例和公报;P=.01)和简单性(P=.03)以及使用 Twitter 识别谣言(P=.008)在评估分数方面具有统计学意义。本范围综述存在一些局限性,涉及将 DENV、ZIKV 和 CHIKV 作为虫媒病毒的选择,这是由于它们的临床和流行病学重要性。

结论:在当代背景下,不能再忽视网络数据或社交媒体作为卫生监测的补充策略。然而,重要的是要共同努力开发新方法,以确保信息质量,并采取系统措施来维护数字工具数据的完整性和可靠性,同时考虑伦理方面。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc75/11612576/07c17f6dd720/jmir_v26i1e57476_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc75/11612576/07c17f6dd720/jmir_v26i1e57476_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc75/11612576/07c17f6dd720/jmir_v26i1e57476_fig1.jpg

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Use of Digital Tools in Arbovirus Surveillance: Scoping Review.

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引用本文的文献

[1]
Early Warning of Infectious Disease Outbreaks Using Social Media and Digital Data: A Scoping Review.

Int J Environ Res Public Health. 2025-7-13

本文引用的文献

[1]
Epidemiology and Economic Burden of Chikungunya: A Systematic Literature Review.

Trop Med Infect Dis. 2023-5-31

[2]
Chikungunya fever.

Nat Rev Dis Primers. 2023-4-6

[3]
Role of artificial intelligence-internet of things (AI-IoT) based emerging technologies in the public health response to infectious diseases in Bangladesh.

Parasite Epidemiol Control. 2022-8

[4]
Detection of Potential Arbovirus Infections and Pregnancy Complications in Pregnant Women in Jamaica Using a Smartphone App (ZIKApp): Pilot Evaluation Study.

JMIR Form Res. 2022-7-27

[5]
An entropy-based method to control COVID-19 rumors in online social networks using opinion leaders.

Technol Soc. 2022-8

[6]
Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review.

Artif Intell Med. 2022-6

[7]
Infectious diseases prevention and control using an integrated health big data system in China.

BMC Infect Dis. 2022-4-6

[8]
The Early Warning and Response System (EWARS-TDR) for dengue outbreaks: can it also be applied to chikungunya and Zika outbreak warning?

BMC Infect Dis. 2022-3-7

[9]
EpiHacks, a Process for Technologists and Health Experts to Cocreate Optimal Solutions for Disease Prevention and Control: User-Centered Design Approach.

J Med Internet Res. 2021-12-15

[10]
Google Trends correlation and sensitivity for outbreaks of dengue and yellow fever in the state of São Paulo.

Einstein (Sao Paulo). 2021

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