Husnayain Atina, Shim Eunha, Fuad Anis, Su Emily Chia-Yu
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
Department of Biostatistics, Epidemiology, and Population Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia.
J Med Internet Res. 2020 Sep 29;22(9):e19788. doi: 10.2196/19788.
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.
韩国是通过大规模免下车检测、佩戴口罩和广泛的社交距离措施来应对新冠疫情表现最佳的国家之一。然而，了解风险认知模式也有助于进行有效的风险沟通，以尽量减少此次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新冠疫情的健康风险认知模式。
使用韩语中与新冠疫情相关的词汇收集谷歌趋势（GT）和NAVER相对搜索量（RSV）数据，并根据时间、性别、年龄组、设备类型和地点进行检索。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新冠病例数和检测数进行比较。采用斯皮尔曼等级相关系数计算的时间滞后相关性来评估新冠新增病例与互联网搜索之间的相关性是否受时间影响。我们还使用滞后1至3天的新冠病例数、检测数以及GT和NAVER RSV构建了新冠新增病例预测模型。采用向后剔除和方差膨胀因子<5的方法进行单因素和多因素回归。
在韩国，与新冠疫情相关的查询数量在包括本地传播、新冠病毒检测试剂盒获批、实施新冠病毒免下车检测、口罩短缺以及广泛的社交距离宣传活动等本地事件期间增加，在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也增加。女性（r=0.763-0.823；P<0.001）以及年龄≤29岁（r=0.726-0.821；P<0.001）、30-44岁（r=0.701-0.826；P<0.001）和≥50岁（r=0.706-0.725；P<0.001）的年龄组在线查询也更为强烈。在空间分布方面，受影响地区的互联网搜索数据更高。此外，与桌面搜索（r=0.705-0.717；P<0.001）相比，移动搜索的相关性更高（r=0.704-0.804；P<0.001），这表明疫情期间在线健康信息搜索行为的变化。这些与新冠疫情相关的不同互联网搜索代表了社区健康风险认知。此外，作为一个新冠病毒检测数量众多的国家，结果显示成年人认为与新冠病毒检测相关的信息比疾病相关知识更重要。同时，年轻和老年群体有不同的认知。此外，NAVER RSV有可能用于健康风险认知评估和疾病预测。与基于新冠病例的模型相比，添加NAVER提供的与新冠疫情相关的搜索可以提高模型的性能，并有可能用于预测疫情曲线。
使用GT和NAVER RSV来探索社区健康风险认知模式可能有助于从时间、人群特征和地点等多个角度进行有针对性的风险沟通。
