Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
Int J Health Geogr. 2020 Mar 3;19(1):4. doi: 10.1186/s12942-020-00199-0.
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.
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.
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.
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.
及时准确地预测登革热病例数，对疾病控制计划有重大影响。泰国登革热控制的一个关键挑战是监测系统不同层面上报的系统性延迟。高效可靠的监测和通知系统对于监测健康结果趋势和及早发现空间和时间上存在差异的疾病暴发至关重要。
由于包括报告延迟在内的多种因素，泰国实时预测登革热病例趋势是一项具有挑战性的任务。我们提出了一种使用时空临近预报模型的决策支持方法，该模型在贝叶斯框架中考虑了报告延迟，并使用滑动窗口。通过 2010 年曼谷地区每周登革热监测数据进行案例研究，展示了所提出的临近预报方法。
整体实时估计准确率为 70.69%，在所有周和地区平均低季和高季的准确率分别为 59.05%和 79.59%。结果表明，该模型能够在监测系统中存在延迟报告的情况下，对真实病例数进行合理估计。使用滑动窗口，模型也可以产生与使用全部数据相似的估计精度。
统计和流行病学界面临的一个持续挑战是将数据转化为基于证据的知识，以促进个人和人群层面的健康改善和疾病控制的政策制定。提高传染病发病率的实时估计是一个重要的技术发展。这项工作的努力为实践中的临近预报提供了一个模板，以为登革热控制提供决策支持。
