Berman Yuval, Algar Shannon D, Walker David M, Small Michael
Complex Systems Group, Department of Mathematics and Statistics, University of Western Australia, Perth, WA, Australia.
CSIRO, Kensington, WA, Australia.
Front Epidemiol. 2023 Jul 10;3:1201810. doi: 10.3389/fepid.2023.1201810. eCollection 2023.
Data that is collected at the individual-level from mobile phones is typically aggregated to the population-level for privacy reasons. If we are interested in answering questions regarding the mean, or working with groups appropriately modeled by a continuum, then this data is immediately informative. However, coupling such data regarding a population to a model that requires information at the individual-level raises a number of complexities. This is the case if we aim to characterize human mobility and simulate the spatial and geographical spread of a disease by dealing in discrete, absolute numbers. In this work, we highlight the hurdles faced and outline how they can be overcome to effectively leverage the specific dataset: Google COVID-19 Aggregated Mobility Research Dataset (GAMRD). Using a case study of Western Australia, which has many sparsely populated regions with incomplete data, we firstly demonstrate how to overcome these challenges to approximate absolute flow of people around a transport network from the aggregated data. Overlaying this evolving mobility network with a compartmental model for disease that incorporated vaccination status we run simulations and draw meaningful conclusions about the spread of COVID-19 throughout the state without de-anonymizing the data. We can see that towns in the Pilbara region are highly vulnerable to an outbreak originating in Perth. Further, we show that regional restrictions on travel are not enough to stop the spread of the virus from reaching regional Western Australia. The methods explained in this paper can be therefore used to analyze disease outbreaks in similarly sparse populations. We demonstrate that using this data appropriately can be used to inform public health policies and have an impact in pandemic responses.
出于隐私原因，从手机收集的个人层面数据通常会汇总到人口层面。如果我们有兴趣回答有关均值的问题，或者处理由连续体适当建模的群体，那么这些数据会立即提供信息。然而，将关于总体的此类数据与需要个人层面信息的模型相结合会带来许多复杂性。如果我们旨在通过处理离散的绝对数字来描述人类流动性并模拟疾病的空间和地理传播，情况就是如此。在这项工作中，我们强调了所面临的障碍，并概述了如何克服这些障碍以有效利用特定数据集：谷歌新冠疫情综合流动性研究数据集（GAMRD）。通过以西澳大利亚为例进行研究，该地区有许多人口稀少且数据不完整的地区，我们首先展示了如何克服这些挑战，以便从汇总数据中近似得出交通网络周围人员的绝对流动情况。将这个不断演变的流动网络与一个纳入疫苗接种状况的疾病 compartmental 模型叠加，我们进行模拟，并在不使数据去匿名化的情况下得出关于新冠疫情在该州传播的有意义结论。我们可以看到，皮尔巴拉地区的城镇极易受到源自珀斯的疫情爆发影响。此外，我们表明，对旅行的区域限制不足以阻止病毒传播到西澳大利亚的各地区。因此，本文所解释的方法可用于分析类似人口稀少地区的疾病爆发情况。我们证明，适当地使用这些数据可为公共卫生政策提供信息，并对疫情应对产生影响。
