tetano
Editor, Senior Moderator
BMC Public Health. 2019 May 29;19(1):656. doi: 10.1186/s12889-019-6968-x.
[h=1]What can urban mobility data reveal about the spatial distribution of infection in a single city?[/h] Moss R[SUP]1[/SUP], Naghizade E[SUP]2[/SUP], Tomko M[SUP]2[/SUP], Geard N[SUP]3,[/SUP][SUP]4,[/SUP][SUP]5[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]BACKGROUND:[/h] 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.
[h=4]METHODS:[/h] 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.
[h=4]RESULTS:[/h] 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.
[h=4]CONCLUSIONS:[/h] 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.
[h=4]KEYWORDS:[/h] Influenza; Spatial epidemiology; Urban mobility
PMID: 31142311 DOI: 10.1186/s12889-019-6968-x
[h=1]What can urban mobility data reveal about the spatial distribution of infection in a single city?[/h] Moss R[SUP]1[/SUP], Naghizade E[SUP]2[/SUP], Tomko M[SUP]2[/SUP], Geard N[SUP]3,[/SUP][SUP]4,[/SUP][SUP]5[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]BACKGROUND:[/h] 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.
[h=4]METHODS:[/h] 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.
[h=4]RESULTS:[/h] 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.
[h=4]CONCLUSIONS:[/h] 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.
[h=4]KEYWORDS:[/h] Influenza; Spatial epidemiology; Urban mobility
PMID: 31142311 DOI: 10.1186/s12889-019-6968-x