BACKGROUND The ability to produce timely and accurate estimation of dengue cases can significantly impact disease control programs. A key challenge for dengue control in Thailand is the systematic delay in reporting at different levels in the surveillance system. Efficient and reliable surveillance and notification systems are vital to monitor health outcome trends and early detection of disease outbreaks which vary in space and time. METHODS Predicting the trend in dengue cases in real-time is a challenging task in Thailand due to a combination of factors including reporting delays. We present decision support using a spatiotemporal nowcasting model which accounts for reporting delays in a Bayesian framework with sliding windows. A case study is presented to demonstrate the proposed nowcasting method using weekly dengue surveillance data in Bangkok at district level in 2010. RESULTS The overall real-time estimation accuracy was 70.69% with 59.05% and 79.59% accuracy during low and high seasons averaged across all weeks and districts. The results suggest the model was able to give a reasonable estimate of the true numbers of cases in the presence of delayed reports in the surveillance system. With sliding windows, models could also produce similar accuracy to estimation with the whole data. CONCLUSIONS A persistent challenge for the statistical and epidemiological communities is to transform data into evidence-based knowledge that facilitates policy making about health improvements and disease control at the individual and population levels. Improving real-time estimation of infectious disease incidence is an important technical development. The effort in this work provides a template for nowcasting in practice to inform decision making for dengue control.

中文翻译：

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贝叶斯时空建模，带有滑动窗口，可纠正泰国实时​​登革热监视的报告延迟。

背景技术产生及时和准确的登革热病例估计的能力可以显着影响疾病控制程序。在泰国，登革热控制面临的主要挑战是在监控系统中不同级别的报告出现系统性延迟。高效，可靠的监视和通知系统对于监视健康结果趋势以及及早发现因空间和时间而异的疾病暴发至关重要。方法由于包括报告延迟在内的多种因素，实时预测登革热病例的趋势在泰国是一项艰巨的任务。我们提出使用时空临近预报模型的决策支持，该模型考虑了具有滑动窗口的贝叶斯框架中报告延迟的情况。提出了一个案例研究，以使用2010年曼谷在区域级别的每周登革热监测数据来证明拟议的临近预报方法。结果总体实时估算准确度为70.69％，在淡季和旺季期间，所有地区的平均平均值准确度为59.05％和79.59％。周和地区。结果表明，在监视系统中存在延迟报告的情况下，该模型能够对病例的真实数量做出合理的估计。使用滑动窗口，模型还可以产生与估计整个数据相似的准确性。结论对于统计和流行病学界来说，持续存在的挑战是将数据转换为基于证据的知识，以促进有关个人和人群健康改善和疾病控制的政策制定。改进对传染病发病率的实时估计是一项重要的技术发展。这项工作的努力为实践中的临近预报提供了一个模板，可为登革热控制的决策提供依据。