时间序列方法和包围模型在巴西国家一级登革热的即时预报
Time Series Methods and Ensemble Models to Nowcast Dengue at the State Level in Brazil
摘要：
预测传染病可以通过建议公共卫生干预和个人预防措施来帮助减少其影响。新的数据流，如互联网和社交媒体数据，最近被报道有利于传染病预测。作为巴西登革热的案例研究，我们在七年内每周将27个州的多个传统和非传统异构数据流（卫星图像，互联网，天气和临床监测数据）结合起来。对于每个州，我们根据几个时间序列模型对登革热进行了预测，这些模型在复杂性和外生数据的包含方面各不相同。表现最好的模型因州而异，这促使我们考虑集成方法，以自动组合这些模型，从而在州一级获得更好的结果。模型的比较表明，预测往往改善了外生数据的增加，虽然类似的性能可以通过只包括一个外生数据流（天气数据或新的卫星数据），而不是将它们结合起来。我们的研究结果表明，巴西可以在州一级以高准确性和信心进行即时预测，为每个数据流的效用提供信息，并揭示预测性能的潜在地理贡献者。我们的工作可以扩展到巴西其他空间层面、病媒传播疾病国家，这样传染病的传播就可以得到更有效的遏制。
英文摘要：
Predicting an infectious disease can help reduce its impact by advising public health interventions and personal preventive measures. Novel data streams, such as Internet and social media data, have recently been reported to benefit infectious disease prediction. As a case study of dengue in Brazil, we have combined multiple traditional and non-traditional, heterogeneous data streams (satellite imagery, Internet, weather, and clinical surveillance data) across its 27 states on a weekly basis over seven years. For each state, we nowcast dengue based on several time series models, which vary in complexity and inclusion of exogenous data. The top-performing model varies by state, motivating our consideration of ensemble approaches to automatically combine these models for better outcomes at the state level. Model comparisons suggest that predictions often improve with the addition of exogenous data, although similar performance can be attained by including only one exogenous data stream (either weather data or the novel satellite data) rather than combining all of them. Our results demonstrate that Brazil can be nowcasted at the state level with high accuracy and confidence, inform the utility of each individual data stream, and reveal potential geographic contributors to predictive performance. Our work can be extended to other spatial levels of Brazil, vector-borne diseases, and countries, so that the spread of infectious disease can be more effectively curbed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
