Health Crowd Sensing and Computing: From Crowdsourced Digital Health Footprints to Population Health Intelligence

Population health monitoring and modelling is important and fundamental for public health operations for the control and intervention of Non-Communicable Diseases (NCD). Healthcare administrators often perform data collection for population health monitoring either by integrating records of hospital visits or conducting survey among a sample of residents, but both approaches are of high cost and time-consuming, which results in limited spatial coverage. The proliferation of devices embedded with multimodality sensors and digital health applications in our daily lives generates data at an unprecedented scale, providing valuable crowdsourced information about personal health status or health-related context. In this book chapter, we propose a new vision, called Health Crowd Sensing and Computing (HCSC), which leverages opportunistic and crowdsourced digital health footprints within a full lifecycle of data collection, linkage, integration, augmentation, and analytics, to realise the goal of more intelligent population health monitoring for NCD. Specifically, our own case study called Compressive Population Health will be introduced, where we combine AI techniques with HCSC to achieve cost-effective public health monitoring. Finally, existing gaps will be discussed with future research opportunities and proposal in this interesting and novel research area.