Mobile Healthcare - Epidemic Disease Control Exploiting Contact Information In Mobile Devices Open Access
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Health is fundamental to personal life, social, and economic development in society. The challenges from the population growth and the aging society have placed a considerable strain on the traditional healthcare system. The recent advances in mobile devices, such as cell phones and wearable body sensors, have given rise to the concept of mobile healthcare, which uses mobile devices to improve health outcomes. Compared with the traditional healthcare, mobile healthcare provides high global coverage, low cost, and easy access, in continuous health monitoring and in-time healthcare. Inspired by those advantages, the deployment of mobile phone assisted health care are accelerated in many applications, i.e, remote health monitoring and electronic health record.In our work, we explore the use of mobile phones in epidemic disease control. Since most infectious diseases spread through human contacts, we focus on modeling the diffusion of diseases by analyzing the social relationship among individuals. In particular, we try to capture the interaction pattern among people using the contact information in mobile devices, and investigate its impact over the spread of disease. Disease control strategies are then proposed based on learned patterns and evaluated over real world datasets. Considering the time that disease control strategies are applied, we partition them into two categories: 1) Disease control strategies that are applied before disease occurs in the population. 2) Disease control strategies that are applied after disease occurs in the population. In this dissertation research, we design disease control strategies for both categories.First, we consider the problem of disease control over social communities. The social communities are identified from mobile device records. A two level disease control strategy is designed to prevent both intra-community and inter-community infections.Second, we consider the problem of disease control over groups of different relationship types is considered. We classify each individual's contacts into groups of different relationship types, and apply disease control strategy over different groups separately. Percolation theory is employed by both strategies to determine the expected number of infected individuals.Third, we propose a preventive disease control strategy which focuses on choosing individuals for vaccination so that the expected number of people infected will be minimized. We prove that this problem is NP-hard, and design an approximation algorithm that provides both upper bound and lower bound to the optimal solution. The former two problems analyzes relationships between infected individuals and their contacts, while the latter attempt to look for best candidates for vaccination in the entire contact network. Results demonstrate our methods are able to protect the population from disease and outperforms the existing disease control strategies.The contributions of this dissertation research lie in the area of disease diffusion modeling, social communities identification, submodular minimization in mobile contact networks. This work is the first to investigate the diffusion of disease from the perspective of mobile contact networks and analyze their impact over the epidemic disease control. Our considerations of social communities, vaccination influence, and mobile device assisted strategies are novel contributions and achieve significant enhancement in epidemic disease control.