One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistic 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 (INLA). The approach is illustrated on dengue fever incidence data in Rio de Janeiro, and Severe Acute Respiratory Illness (SARI) data in Paran\'a state, Brazil.

翻译：实时追踪流行病的一个困难与报告延误有关,报告延误的原因可能在于实验室确认、后勤问题、基础设施困难等等。在决策方面,如向公众和地方当局发出警告,尽快纠正现有信息的能力至关重要。建议采用巴耶斯等级建模方法,作为纠正报告延误和量化相关不确定性的灵活方式。由于采用了综合巢巢状拉普尔近似(INLA),模型的实施速度很快。该方法在里约热内卢登革热发生率数据和巴西帕拉纳州严重急性呼吸系统疾病数据上作了说明。