Understanding the Community Risk Perceptions of the COVID-19 Outbreak in South Korea: Infodemiology Study.
Understanding the Community Risk Perceptions of the COVID-19 Outbreak in South Korea: Infodemiology Study.
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DOI：
10.2196/19788
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发表时间：
2020-09-29
影响因子：
7.4
通讯作者：
Su EC
中科院分区：
文献类型：
--
作者：
Husnayain A;Shim E;Fuad A;Su EC
韩国是在应对冠状病毒大流行方面表现最好的国家之一，采取了大规模驾车通过检测、使用口罩和广泛的社交距离。然而，了解风险认知模式也可促进有效的风险沟通，以尽量减少危机期间疾病传播的影响。我们试图利用互联网搜索数据探索韩国社区对COVID-19的健康风险认知模式。谷歌Trends （GT）和NAVER的相对搜索量（rsv）数据是用韩国语收集的，按照时间、性别、年龄、设备类型、位置等进行检索。将在线查询与2019年12月5日至2020年5月31日期间Kaggle开放获取数据集中报告的每日新增COVID-19病例和检测数量进行了比较。采用Spearman秩相关系数计算的时滞相关性来评估新发病例与网络搜索之间的相关性是否受到时间的影响。我们还利用COVID-19病例数、检测结果以及滞后期（1-3天）的GT和NAVER rsv构建了新发COVID-19病例的预测模型。采用反向消去和方差膨胀因子<5的单次和多次回归。在本地传播、批准冠状病毒检测试剂盒、实施冠状病毒免刷检测、口罩短缺、广泛开展社交距离运动等地方事件以及世界卫生组织宣布国际关注的突发公共卫生事件等国际事件期间，韩国与covid -19相关的查询数量有所增加。在线查询在女性（r=0.763-0.823， P<.001）、≤29岁（r=0.726-0.821， P<.001）、30-44岁（r=0.701-0.826， P<.001）和≥50岁（r=0.706-0.725， P<.001）年龄组中也更强。在空间分布上，受灾地区的互联网搜索数据较高。此外，与桌面搜索（r=0.705-0.717； P<.001）相比，移动搜索（r=0.704-0.804； P<.001）发现了更大的相关性，这表明在疫情期间搜索在线健康信息的行为发生了变化。这些与COVID-19相关的不同互联网搜索代表了社区对健康风险的看法。此外，作为冠状病毒检测数量众多的国家，结果显示，成年人认为冠状病毒检测相关信息比疾病相关知识更重要。与此同时，年轻人和老年人的看法不同。此外，NAVER rsv可能用于健康风险感知评估和疾病预测。与基于COVID-19病例的模型相比，添加NAVER提供的COVID-19相关搜索可以提高模型的性能，并有可能用于预测流行曲线。使用GT和NAVER rsv来探索社区健康风险认知模式可能有利于从几个角度（包括时间、人口特征和地点）进行风险沟通。
South Korea is among the best-performing countries in tackling the coronavirus pandemic by using mass drive-through testing, face mask use, and extensive social distancing. However, understanding the patterns of risk perception could also facilitate effective risk communication to minimize the impacts of disease spread during this crisis. We attempt to explore patterns of community health risk perceptions of COVID-19 in South Korea using internet search data. Google Trends (GT) and NAVER relative search volumes (RSVs) data were collected using COVID-19–related terms in the Korean language and were retrieved according to time, gender, age groups, types of device, and location. Online queries were compared to the number of daily new COVID-19 cases and tests reported in the Kaggle open-access data set for the time period of December 5, 2019, to May 31, 2020. Time-lag correlations calculated by Spearman rank correlation coefficients were employed to assess whether correlations between new COVID-19 cases and internet searches were affected by time. We also constructed a prediction model of new COVID-19 cases using the number of COVID-19 cases, tests, and GT and NAVER RSVs in lag periods (of 1-3 days). Single and multiple regressions were employed using backward elimination and a variance inflation factor of <5. The numbers of COVID-19–related queries in South Korea increased during local events including local transmission, approval of coronavirus test kits, implementation of coronavirus drive-through tests, a face mask shortage, and a widespread campaign for social distancing as well as during international events such as the announcement of a Public Health Emergency of International Concern by the World Health Organization. Online queries were also stronger in women (r=0.763-0.823; P<.001) and age groups ≤29 years (r=0.726-0.821; P<.001), 30-44 years (r=0.701-0.826; P<.001), and ≥50 years (r=0.706-0.725; P<.001). In terms of spatial distribution, internet search data were higher in affected areas. Moreover, greater correlations were found in mobile searches (r=0.704-0.804; P<.001) compared to those of desktop searches (r=0.705-0.717; P<.001), indicating changing behaviors in searching for online health information during the outbreak. These varied internet searches related to COVID-19 represented community health risk perceptions. In addition, as a country with a high number of coronavirus tests, results showed that adults perceived coronavirus test–related information as being more important than disease-related knowledge. Meanwhile, younger, and older age groups had different perceptions. Moreover, NAVER RSVs can potentially be used for health risk perception assessments and disease predictions. Adding COVID-19–related searches provided by NAVER could increase the performance of the model compared to that of the COVID-19 case–based model and potentially be used to predict epidemic curves. The use of both GT and NAVER RSVs to explore patterns of community health risk perceptions could be beneficial for targeting risk communication from several perspectives, including time, population characteristics, and location.