Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Paris, France.
Centre National de la Recherche Scientifique, UMR2000: Génomique évolutive, modélisation et santé (GEMS), Paris, France.
Influenza Other Respir Viruses. 2018 Nov;12(6):772-779. doi: 10.1111/irv.12599. Epub 2018 Aug 21.
Maps of influenza activity are important tools to monitor influenza epidemics and inform policymakers. In France, the availability of a high-quality data set from the Oscour surveillance network, covering 92% of hospital emergency department (ED) visits, offers new opportunities for disease mapping. Traditional geostatistical mapping methods such as Kriging ignore underlying population sizes, are not suited to non-Gaussian data and do not account for uncertainty in parameter estimates.
Our objective was to create reliable weekly interpolated maps of influenza activity in the ED setting, to inform Santé publique France (the French national public health agency) and local healthcare authorities.
We used Oscour data of ED visits covering the 2016-2017 influenza season. We developed a Bayesian model-based geostatistical approach, a class of generalized linear mixed models, with a multivariate normal random field as a spatially autocorrelated random effect. Using R-INLA, we developed an algorithm to create maps of the proportion of influenza-coded cases among all coded visits. We compared our results with maps obtained by Kriging.
Over the study period, 45 565 (0.82%) visits were coded as influenza cases. Maps resulting from the model are presented for each week, displaying the posterior mean of the influenza proportion and its associated uncertainty. Our model performed better than Kriging.
Our model allows producing smoothed maps where the random noise has been properly removed to reveal the spatial risk surface. The algorithm was incorporated into the national surveillance system to produce maps in real time and could be applied to other diseases.
流感活动图是监测流感疫情和为政策制定者提供信息的重要工具。在法国，Oscour 监测网络提供了一个高质量的数据集，涵盖了 92%的医院急诊部（ED）就诊，为疾病制图提供了新的机会。传统的地质统计学映射方法（如克里金法）忽略了潜在的人口规模，不适合非高斯数据，也没有考虑参数估计的不确定性。
我们的目的是创建可靠的每周 ED 中流感活动的插值图，为法国公共卫生署（法国国家公共卫生机构）和地方卫生当局提供信息。
我们使用了 2016-2017 流感季节的 Oscour ED 就诊数据。我们开发了一种基于贝叶斯模型的地质统计学方法，即广义线性混合模型的一类，具有多元正态随机场作为空间自相关随机效应。使用 R-INLA，我们开发了一种算法来创建所有编码就诊中流感编码病例比例的地图。我们将我们的结果与克里金法获得的地图进行了比较。
在研究期间，45565 次就诊（0.82%）被编码为流感病例。为每一周展示了模型生成的地图，显示了流感比例的后验均值及其相关的不确定性。我们的模型表现优于克里金法。
我们的模型允许生成平滑的地图，其中适当去除了随机噪声以揭示空间风险表面。该算法已被纳入国家监测系统，以便实时生成地图，并且可以应用于其他疾病。
