School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, United States of America.
Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
PLoS Comput Biol. 2020 Aug 17;16(8):e1008117. doi: 10.1371/journal.pcbi.1008117. eCollection 2020 Aug.
Understanding the behavior of emerging disease outbreaks in, or ahead of, real-time could help healthcare officials better design interventions to mitigate impacts on affected populations. Most healthcare-based disease surveillance systems, however, have significant inherent reporting delays due to data collection, aggregation, and distribution processes. Recent work has shown that machine learning methods leveraging a combination of traditionally collected epidemiological information and novel Internet-based data sources, such as disease-related Internet search activity, can produce meaningful "nowcasts" of disease incidence ahead of healthcare-based estimates, with most successful case studies focusing on endemic and seasonal diseases such as influenza and dengue. Here, we apply similar computational methods to emerging outbreaks in geographic regions where no historical presence of the disease of interest has been observed. By combining limited available historical epidemiological data available with disease-related Internet search activity, we retrospectively estimate disease activity in five recent outbreaks weeks ahead of traditional surveillance methods. We find that the proposed computational methods frequently provide useful real-time incidence estimates that can help fill temporal data gaps resulting from surveillance reporting delays. However, the proposed methods are limited by issues of sample bias and skew in search query volumes, perhaps as a result of media coverage.
了解新发疾病在实时或提前爆发的情况，有助于卫生保健官员更好地设计干预措施，减轻对受影响人群的影响。然而，大多数基于医疗保健的疾病监测系统由于数据收集、汇总和分发过程，存在着显著的固有报告延迟。最近的研究表明，利用传统收集的流行病学信息和新的基于互联网的数据源（如与疾病相关的互联网搜索活动）相结合的机器学习方法，可以在基于医疗保健的估计之前对疾病发病率进行有意义的“实时预测”，大多数成功的案例研究都集中在流感和登革热等地方性和季节性疾病上。在这里，我们将类似的计算方法应用于地理区域的新发疫情，这些地区以前没有观察到所关注疾病的存在。通过将有限的可用历史流行病学数据与与疾病相关的互联网搜索活动相结合，我们回溯性地估计了五个近期疫情爆发前几周的疾病活动。我们发现，所提出的计算方法经常提供有用的实时发病率估计，可以帮助填补由于监测报告延迟而导致的时间数据空白。然而，所提出的方法受到样本偏差和搜索查询量偏斜的限制，这可能是由于媒体报道的结果。
