Statistical methods for characterising the severity of an emerging pathogen: case studies of the COVID-19 pandemic

对一种新病原体的持续流行进行建模提出了几个特别的挑战，特别是当病例往往没有症状或容易被误认为感冒或流感等常见疾病时。传染病的数学模型依赖于数据的输入，而在新冠肺炎大流行之初，研究人员可以获得的数据集非常有限。这导致估计有很大的偏差，包括很大的不确定区间，或者需要假设不同国家的病毒严重程度是相同的。这一假设在当时是必要的，但使结果非常令人怀疑，例如，当使用高收入国家的数据来模拟低收入或中等收入国家的流行病时。数据带来的另一个问题是收集数据的延迟。对正在进行的流行病的监测需要每天监测新病例或死亡人数，但由于报告延迟，这往往是不可能的，因为许多医院工作人员被患者数量压得喘不过气来，无法及时更新在线数据。这导致系统性地低估了疫情的真实情况。我的博士项目的主要主题是弥合一些数据差距，减少数据中的偏差。在第一个项目中，我们基于住院数据提供了巴西新冠肺炎疫情常见流行病学分布的估计，例如从发病到死亡的时间。我们的估计给出了拟合分布中空间异质性的证据。在第二个项目中，我们提出了一种使用潜在高斯过程(GP)来校正数据报告延迟的新方法，并给出了该方法在巴西新冠肺炎死亡率数据中的适用性。

Modelling of an ongoing epidemic of a new pathogen poses several particular challenges, especially when the cases are often symptomless or easy to mistake for a common disease such as a cold or flu. Mathematical models of infectious diseases rely on the input of data, and at the beginning of the COVID-19 pandemic very limited datasets were available to the researchers. This caused the estimates to have large biases, including large uncertainty intervals or the need to assume the severity of the virus across different countries was the same. This assumption was necessary at the time but made the results highly questionable e.g. when data from a high-income country were used to model the epidemic in a low- or middle-income country.An additional problem posed by the data was the delay with which they were collected. Surveillance of an ongoing epidemic requires daily monitoring of the numbers of new cases or deaths, but this was often impossible due to the reporting delays, as a lot of hospital staff was overwhelmed with the number of patients and unable to promptly update the data online. This led to systematic underreporting of the true state of the epidemic.The main topic of my PhD projects is closing some of those data gaps and decreasing the bias in the data. In the first project, we provided estimates of common epidemiological distributions, such as onset-to-death time, for the COVID-19 epidemic in Brazil based on hospitalisation data. Our estimates gave evidence of spatial heterogeneity in the fitted distributions. In the second project, we proposed a new approach to correcting the delays in data reporting using latent Gaussian processes (GPs) and presented the applicability of the method to the COVID-19 mortality data in Brazil.