Lee Duncan, Robertson Chris, Marques Diogo
School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8SQ, Scotland, United Kingdom.
Department of Mathematics and Statistics, University of Strathclyde, Glasgow, G1 1XH, Scotland, United Kingdom.
Spat Stat. 2022 Jun;49:100508. doi: 10.1016/j.spasta.2021.100508. Epub 2021 Apr 10.
Modelling the small-area spatio-temporal dynamics of the Covid-19 pandemic is of major public health importance, because it allows health agencies to better understand how and why the virus spreads. However, in Scotland during the first wave of the pandemic testing capacity was severely limited, meaning that large numbers of infected people were not formally diagnosed as having the virus. As a result, data on confirmed cases are unlikely to represent the true infection rates, and due to the small numbers of positive tests these data are not available at the small-area level for confidentiality reasons. Therefore to estimate the small-area dynamics in Covid-19 incidence this paper analyses the spatio-temporal trends in telehealth data relating to Covid-19, because during the first wave of the pandemic the public were advised to call the national telehealth provider NHS 24 if they experienced symptoms of the virus. Specifically, we propose a multivariate spatio-temporal correlation model for modelling the proportions of calls classified as either relating to Covid-19 directly or having related symptoms, and provide software for fitting the model in a Bayesian setting using Markov chain Monte Carlo simulation. The model was developed in partnership with the national health agency Public Health Scotland, and here we use it to analyse the spatio-temporal dynamics of the first wave of the Covid-19 pandemic in Scotland between March and July 2020, specifically focusing on the spatial variation in the peak and the end of the first wave.
对新冠疫情的小区域时空动态进行建模具有重大的公共卫生意义，因为它能让卫生机构更好地理解病毒传播的方式和原因。然而，在苏格兰疫情的第一波期间，检测能力严重受限，这意味着大量感染者未被正式诊断出感染该病毒。因此，确诊病例数据不太可能代表真实感染率，而且由于阳性检测数量较少，出于保密原因，这些数据在小区域层面无法获取。所以，为了估计新冠发病率的小区域动态，本文分析了与新冠相关的远程医疗数据的时空趋势，因为在疫情第一波期间，若民众出现病毒症状，会被建议致电国家远程医疗服务提供商NHS 24。具体而言，我们提出了一个多元时空相关模型，用于对被归类为直接与新冠相关或有相关症状的呼叫比例进行建模，并提供了在贝叶斯框架下使用马尔可夫链蒙特卡罗模拟来拟合该模型的软件。该模型是与国家卫生机构苏格兰公共卫生合作开发的，在此我们用它来分析2020年3月至7月苏格兰新冠疫情第一波的时空动态，特别关注第一波高峰期和结束期的空间变化。
