Data from: Unveiling the paths of COVID-19 in a large city based on public transportation data

Human mobility plays a key role in the dissemination of infectious diseases around the world. However, the complexity introduced by commuting patterns in the daily life of cities makes such a role unclear, especially at the intracity scale. Here, we propose a multiplex network fed with 9 months of mobility data with more than 107 million public bus validations in order to understand the relation between urban mobility and the spreading of COVID-19 within a large city, namely, Fortaleza in the northeast of Brazil. Our results suggest that the shortest bus rides in Fortaleza changed ≈ 25% more than the longest ones after an epidemic wave. Such a result is the opposite of what has been observed at the intercity scale. We also find that mobility changes among the neighborhoods are synchronous and geographically homogeneous. Furthermore, we find that the most central neighborhoods in mobility are the primary targets for infectious disease outbreaks, which is quantified here in terms of the linear relation between the disease arrival time and the average of the closeness centrality ranking. These central neighborhoods are also the top neighborhoods in the number of reported cases at the end of an epidemic wave as indicated by the exponential decay behavior of the disease arrival time in relation to the number of accumulated reported cases with decay constant λ ≈ 33 days. We believe that these results can help in the development of new strategies to impose restriction measures in the cities guiding decision-makers with smart actions in public health policies, as well as supporting future research on urban mobility and epidemiology.

人员流动在全球传染病的传播进程中扮演着关键角色。然而，城市日常通勤模式所衍生的复杂性使得该作用难以被清晰认知，在城市内部尺度上这一问题尤为突出。为此，我们构建了多层网络（multiplex network）模型，并纳入巴西东北部大城市福塔莱萨（Fortaleza）的9个月出行数据——包含超1.07亿次公共公交刷卡验证记录——以探究城市出行与新冠病毒（COVID-19）在该城市内部传播之间的关联。研究结果显示，本轮疫情浪潮过后，福塔莱萨市内短途公交出行量的变化幅度较长途出行高出约25%。这一结果与城际尺度下观测到的现象恰好相反。我们还发现，各街区的出行变化呈现同步性与地理同质性特征。此外，我们发现出行网络中核心度最高的街区是传染病暴发的主要目标区域，这一关联可通过传染病抵达时间与接近中心性（closeness centrality）排名平均值之间的线性关系进行量化。疫情浪潮结束时，这些核心街区的报告病例数也位居前列；这一结论可通过传染病抵达时间与累计报告病例数之间的指数衰减关系得到佐证，其衰减常数λ≈33天。我们认为，本研究结果可为城市管控措施的制定提供新思路，辅助决策者制定科学合理的公共卫生政策，同时也可为未来城市出行与流行病学相关研究提供支撑。