Scientific Computing Program, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil.
Department of Mathematics, University of Exeter, Exeter, UK.
Stat Med. 2019 Sep 30;38(22):4363-4377. doi: 10.1002/sim.8303. Epub 2019 Jul 10.
One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistical problems, infrastructure difficulties, and so on. The ability to correct the available information as quickly as possible is crucial, in terms of decision making such as issuing warnings to the public and local authorities. A Bayesian hierarchical modelling approach is proposed as a flexible way of correcting the reporting delays and to quantify the associated uncertainty. Implementation of the model is fast due to the use of the integrated nested Laplace approximation. The approach is illustrated on dengue fever incidence data in Rio de Janeiro, and severe acute respiratory infection data in the state of Paraná, Brazil.
实时跟踪疫情的一个困难与报告延迟有关。报告延迟可能是由于实验室确认、后勤问题、基础设施困难等原因造成的。尽快纠正可用信息的能力对于做出决策（如向公众和地方当局发出警告）至关重要。提出了一种贝叶斯分层建模方法，作为一种灵活的校正报告延迟并量化相关不确定性的方法。由于使用了集成嵌套拉普拉斯逼近，该模型的实现速度很快。该方法应用于巴西里约热内卢登革热发病率数据和巴西南部帕拉那州严重急性呼吸道感染数据。
