Chronic diseases like cancer and diabetes are major
threats to human life. Understanding the distribution
and progression of chronic diseases of a
population is important in assisting the allocation of
medical resources as well as the design of policies
in preemptive healthcare. Traditional methods to
obtain large scale indicators on population health,
e.g., surveys and statistical analysis, can be costly
and time-consuming and often lead to a coarse
spatio-temporal picture. In this paper, we leverage
a dataset describing the human mobility patterns
of citizens in a large metropolitan area. By viewing
local human lifestyles we predict the evolution
rate of several chronic diseases at the level of a city
neighborhood. We apply the combination of a collaborative
topic modeling (CTM) and a Gaussian
mixture method (GMM) to tackle the data sparsity
challenge and achieve robust predictions on
health conditions simultaneously. Our method enables
the analysis and prediction of disease rate
evolution at fine spatio-temporal scales and demonstrates
the potential of incorporating datasets from
mobile web sources to improve population health
monitoring. Evaluations using real-world check-in
and chronic disease morbidity datasets in the city
of London show that the proposed CTM+GMM
model outperforms various baseline methods