What can urban mobility data reveal about the spatial distribution of infection in a single city?

Abstract

Background Infectious diseases spread through inherently spatial processes. Road and air traffic data have been used to model these processes at national and global scales. At metropolitan scales, however, mobility patterns are fundamentally different and less directly observable. Estimating the spatial distribution of infection has public health utility, but few studies have investigated this at an urban scale. In this study we address the question of whether the use of urban-scale mobility data can improve the prediction of spatial patterns of influenza infection. We compare the use of different sources of urban-scale mobility data, and investigate the impact of other factors relevant to modelling mobility, including mixing within and between regions, and the influence of hub and spoke commuting patterns. Methods We used journey-to-work (JTW) data from the Australian 2011 Census, and GPS journey data from the Sygic GPS Navigation & Maps mobile app, to characterise population mixing patterns in a spatially-explicit SEIR (susceptible, exposed, infectious, recovered) meta-population model. Results Using the JTW data to train the model leads to an increase in the proportion of infections that arise in central Melbourne, which is indicative of the city’s spoke-and-hub road and public transport networks, and of the commuting patterns reflected in these data. Using the GPS data increased the infections in central Melbourne to a lesser extent than the JTW data, and produced a greater heterogeneity in the middle and outer regions. Despite the limitations of both mobility data sets, the model reproduced some of the characteristics observed in the spatial distribution of reported influenza cases. Conclusions Urban mobility data sets can be used to support models that capture spatial heterogeneity in the transmission of infectious diseases at a metropolitan scale. These data should be adjusted to account for relevant urban features, such as highly-connected hubs where the resident population is likely to experience a much lower force of infection that the transient population. In contrast to national and international scales, the relationship between mobility and infection at an urban level is much less apparent, and requires a richer characterisation of population mobility and contact. Electronic supplementary material The online version of this article (10.1186/s12889-019-6968-x) contains supplementary material, which is available to authorized users.

Risk mapping for COVID-19 outbreaks in Australia using mobility data

“…Over the past two decades, due to public health concern over the pandemic potential of SARS, MERS and novel influenza, spatially explicit models of disease transmission have become commonplace in simulations of realistic pandemic intervention policies [38,39]. Such models rely on descriptions of mobility patterns which are usually derived from static snapshots of mobility obtained from census data [35,40,41]. While this approach is justifiable given the known importance of mobility in disease transmission, it is also clear that the shocks to normal mobility behaviour induced by the intervention policies of the COVID-19 pandemic will not be captured by static treatments of mobility patterns.…”

Revisión rápida: probabilidad de contagio por infecciones respiratorias agudas en el transporte público colectivo

“…Lo anterior podría ser consecuencia de la disposición de la red de transporte, la concentración de la fuerza laboral dentro y alrededor de la zona de corporativos y a la naturaleza de los datos recabados que caracterizan exclusivamente los traslados al lugar de trabajo. 19 Cooley y colaboradores simularon las interacciones de usuarios del transporte subterráneo con lugares de trabajo, escuela, hogar y actividades comunitarias. Se estimó que 4.4% de las 2.6 millones de infecciones durante una epidemia de influenza simulada ocurrirían en el metro.…”

Interplay between intra-urban population density and mobility in determining the spread of epidemics

“…Quantifying the impact of local mobility on the global diffusion of a pandemic constitutes a challenging task. In this sense, several examples addressing the impact of daily recur-rent mobility patterns on the spread of contagious diseases can be found in the literature [24][25][26][27][28][29][30][31]. The majority of these, however, are theoretical frameworks analyzing the features of synthetic mobility networks, and the influence of total volume of travelers on the course of the epidemic.…”