Probabilistic prediction of disease outbreaks with application to operational warning systems for infectious diseases in Brazil

减轻传染病影响的一种有用的干预和预防方法是使用早期预警系统，特别是为了对可能的流行病暴发发出警报。目前在巴西，登革热、寨卡病毒、基孔肯雅热和严重呼吸综合征(SRAG)等疾病都有这一机制。理想情况下，这些警报系统将使用当前感染该疾病的人数的计数来确定是否应该发出警报。这些警报意味着，分配资源等程序可以以适合正在发生的疾病暴发的严重程度的方式进行。然而，在给定的一天中，这种疾病的实际病例数量几乎总是未知的。这是由两个主要问题造成的：疾病数量报告不足和疾病数量报告延迟。延迟报告是指只有一部分疾病计数在发生后立即可用，而其余计数最终可用，但在发生后太长时间(有时是几个月)。因此，疾病总数最终是已知的，但不能及时用于预警系统。此外，这一总数通常不是真实的疾病计数，因为一些病例从未报告(漏报)，这可能是由于几个原因造成的，例如个人被错误诊断或没有寻求医疗咨询。该项目涉及研究制定用于早期预警系统的校正和预测疾病计数的综合统计建模框架，并具体应用于巴西的这类系统。目前纠正延迟报告的最先进方法要么过于僵化，要么计算过于密集，无法在实践中发挥最佳作用。这项工作旨在制定一个“两全其美”的新框架，即模型的灵活性和实际可行性，同时引入新的要素，如纠正报告不足和纳入适当的预测值。

A useful intervention and prevention method for mitigating impact from infectious disease is the use of early warning systems, particularly in order to alert for possible epidemic outbreaks. This is currently in place for diseases like dengue, zika, chikungunya and severe respiratory syndrome (SRAG) in Brazil. These warning systems would ideally use counts of the number of people currently infected with the disease in order to determine whether an alert should be sent out. These alerts mean that procedures such as the allocation of resources can be carried out in a way that is suitable to the severity of the disease outbreak that is occurring. However, the actual number of cases of the disease on a given day is almost always not known. This is caused by two main problems: the under-reporting of disease counts and the delayed reporting of disease counts. Delayed reporting is when only a proportion of the disease counts are available immediately after they occur, while the rest of the counts eventually become available but too long (sometimes months) after they have occurred. Hence, the total disease count becomes known eventually but not in time to use in a warning system. Moreover, this total count is usually not the true disease count due to some cases never being reported (under-reporting) which can happen for several reasons such as individuals being incorrectly diagnosed or not seeking medical advice. This project involves research on developing a comprehensive statistical modelling framework for correcting and predicting disease counts for use in early warning systems, with specific application to such systems in Brazil. Current state-of-the-art approaches to correcting delayed reporting are either too inflexible or too computationally intensive to be of optimal use in practice. This work aims to develop a new framework, that brings the "best of both worlds", i.e. modelling flexibility and practical feasibility, while at the same time introduce new elements such as correcting for under-reporting and the incorporation of suitable predictors.