  Because of its important role in health policy-shaping, population health
monitoring (PHM) is considered a fundamental block for public health services.
However, traditional public health data collection approaches, such as
clinic-visit-based data integration or health surveys, could be very costly and
time-consuming. To address this challenge, this paper proposes a cost-effective
approach called Compressive Population Health (CPH), where a subset of a given
area is selected in terms of regions within the area for data collection in the
traditional way, while leveraging inherent spatial correlations of neighboring
regions to perform data inference for the rest of the area. By alternating
selected regions longitudinally, this approach can validate and correct
previously assessed spatial correlations. To verify whether the idea of CPH is
feasible, we conduct an in-depth study based on spatiotemporal morbidity rates
of chronic diseases in more than 500 regions around London for over ten years.
We introduce our CPH approach and present three extensive analytical studies.
The first confirms that significant spatiotemporal correlations do exist. In
the second study, by deploying multiple state-of-the-art data recovery
algorithms, we verify that these spatiotemporal correlations can be leveraged
to do data inference accurately using only a small number of samples. Finally,
we compare different methods for region selection for traditional data
collection and show how such methods can further reduce the overall cost while
maintaining high PHM quality.
