1 research outputs found
Exploiting Population Activity Dynamics to Predict Urban Epidemiological Incidence
Ambulance services worldwide are of vital importance to population health.
Timely responding to incidents by dispatching an ambulance vehicle to the
location a call came from can offer significant benefits to patient care across
a number of medical conditions. Moreover, identifying the reasons that drive
ambulance activity at an area not only can improve the operational capacity of
emergency services, but can lead to better policy design in healthcare. In this
work, we analyse the temporal dynamics of 5.6 million ambulance calls across a
region of 7 million residents in the UK. We identify characteristic temporal
patterns featuring diurnal and weekly cycles in ambulance call activity. These
patterns are stable over time and across geographies. Using a dataset sourced
from location intelligence platform Foursquare, we establish a link between the
spatio-temporal dynamics of mobile users engaging with urban activities locally
and emergency incidents. We use this information to build a novel metric that
assesses the health risk of a geographic area in terms of its propensity to
yield ambulance calls. Formulating then an online classification task where the
goal becomes to identify which regions will need an ambulance at a given time,
we demonstrate how semantic information about real world places crowdsourced
through online platforms, can become a useful source of information in
understanding and predicting regional epidemiological trends