Kostandova Natalya, Prosperi Christine, Mutembo Simon, Nakazwe Chola, Namukoko Harriet, Nachinga Bertha, Lai Shengjie, Tatem Andrew J, Duan Qianwen, Kabalo Elliot N, Makungo Kabondo, Chongwe Gershom, Chilumba Innocent, Musukwa Gloria, Matakala Kalumbu H, Hamahuwa Mutinta, Mufwambi Webster, Matoba Japhet, Mutale Irene, Situtu Kenny, Simulundu Edgar, Ndubani Phillimon, Hasan Alvira Z, Truelove Shaun A, Winter Amy K, Carcelen Andrea C, Lau Bryan, Moss William J, Wesolowski Amy
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
PLOS Glob Public Health. 2025 May 20;5(5):e0003906. doi: 10.1371/journal.pgph.0003906. eCollection 2025.
Quantifying population mobility is crucial in developing accurate models of infectious disease dynamics. Increasingly, multiple data sources are available to describe individual and population mobility in a single location; however, there are no methods to systematically integrate these data. Combining information from these data sets may be valuable and help mitigate inherent biases in each data set due to sampling, censoring, and recall. We examined four commonly used data sources (mobile phone records, travel survey, Demographic and Health Survey, and Facebook location information) to quantify subnational travel patterns in Zambia. First, we explored summary metrics of mobility from each data set. Estimates of the probability of a trip and location of travel varied across data sets, with some data quantifying twice the frequency of travel as others. Then, we developed a departure-diffusion model that is able to produce a single estimate of travel by combining these data sets. When multi-data set models included mobile phone records, this data source dominated estimates given the broader spatial coverage. We then used a metapopulation model to simulate a measles outbreak to identify how these different data sets and models would impact estimates of the spatial spread of a highly infectious disease. We found that using travel survey data to parameterize mobility resulted in the introduction of cases in 98% of districts compared to less than 50% when mobile phone data or Facebook data were used. This study highlights the need for methods that facilitate integrating multiple data sets to improve validity of mobility estimates and resultant infectious disease transmission dynamics.
量化人口流动性对于建立准确的传染病动态模型至关重要。目前，越来越多的数据源可用于描述单个地点的个人和人口流动性；然而，尚无系统整合这些数据的方法。整合这些数据集的信息可能很有价值，并有助于减轻每个数据集中由于抽样、审查和回忆而固有的偏差。我们研究了四个常用的数据源（手机记录、旅行调查、人口与健康调查以及脸书位置信息），以量化赞比亚国内的旅行模式。首先，我们探索了每个数据集中流动性的汇总指标。不同数据集对出行概率和出行地点的估计各不相同，有些数据量化的出行频率是其他数据的两倍。然后，我们开发了一种出发-扩散模型，该模型能够通过整合这些数据集得出单一的出行估计值。当多数据集模型包含手机记录时，鉴于其更广泛的空间覆盖范围，该数据源在估计中占主导地位。接着，我们使用一个集合种群模型来模拟麻疹疫情爆发，以确定这些不同的数据集和模型将如何影响对高传染性疾病空间传播的估计。我们发现，与使用手机数据或脸书数据时不到50%的地区相比，使用旅行调查数据来参数化流动性会导致98%的地区出现病例。这项研究强调了需要有便于整合多个数据集的方法，以提高流动性估计以及由此产生的传染病传播动态的有效性。
