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Because of its important role in health policy-shaping, population healthmonitoring (PHM) is considered a fundamental block for public health services.However, traditional public health data collection approaches, such asclinic-visit-based data integration or health surveys, could be very costly andtime-consuming. To address this challenge, this paper proposes a cost-effectiveapproach called Compressive Population Health (CPH), where a subset of a givenarea is selected in terms of regions within the area for data collection in thetraditional way, while leveraging inherent spatial correlations of neighboringregions to perform data inference for the rest of the area. By alternatingselected regions longitudinally, this approach can validate and correctpreviously assessed spatial correlations. To verify whether the idea of CPH isfeasible, we conduct an in-depth study based on spatiotemporal morbidity ratesof 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. Inthe second study, by deploying multiple state-of-the-art data recoveryalgorithms, we verify that these spatiotemporal correlations can be leveragedto do data inference accurately using only a small number of samples. Finally,we compare different methods for region selection for traditional datacollection and show how such methods can further reduce the overall cost whilemaintaining high PHM quality.

Enabling Cost-Effective Population Health Monitoring By Exploiting Spatiotemporal Correlation: An Empirical Study