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Strong social networks can encourage healthy behaviors. In this paper, we introduce a sociology-based computational framework for influence networks. The model construct is generic and is applicable to diverse social network analysis. We demonstrate its usage in calibrating the positive influence of church clergy in spreading HIV/AIDs information in a large metropolitan city. Five experiments are designed to contrast influence with respect to the interaction style between clergy and churchgoers. Competitive and non-competitive knowledge dissemination are also analyzed. The results show that when only one set of information exists, the spreading scope is directly proportional to the product of population size and the disease infection rate. When competing information is present, the importance of clergy in spreading the information decreases when the original propagation sources are ample. However, if sufficient interaction and trust are present among the clergy and the participants, the clergy's positive influence remains significant despite pre-existing knowledge. The generalized framework requires minimal regional data to establish the influence network. It provides useful policy insights for decision makers to determine effective avenues for information dissemination through community influencers.

In this study, we present an interactive web-based real-time decision support suite, RealOpt©. The system integrates visualization, information and cognitive analytics, and dynamic large-scale computational modeling and optimization tools that allow public health emergency preparedness coordinators to determine optimal response facilities and locations, resource needs and supply-routes, and population flow in real time. With an eye towards flexibility and future system expansion, RealOpt is designed in modular format allowing direct linkage to multiple functional modules. Currently, the system has twelve modules covering emergency response preparedness and operations for biological, chemical, radiological/nuclear incidents, biosurveillance, epidemiology, and decontamination models, operations logistics and networks, a real-time crowd sourcing data feed, and evacuation planning. RealOpt has been used for biodefense and H1N1 regional planning and operations, regional flood and hurricane responses, 2010 Haiti earthquake disaster relief, 2011 Japan Fukushima disaster, 2014-2015 Ebola containment assistance and after-event public health preparedness training in West Africa, and current Zika virus containment analysis. The fast solution engines enable real-time use for rapid decision and scenario analysis, since it requires only one CPU minute to determine an optimal network of facilities and resource needs to serve a population of over 10 million.

Remote patient monitoring systems (RMS) have gained increasing popularity in recent years. RMS have great potential to improve medical services by providing more affordable, timely, and accessible care. This paper describes an effective low-cost RMS that is readily deployable. The system targets chronic disease patients and attempts to reduce patient visits to the hospital and healthcare costs. The system is comprised of three modules: (1) an application for data acquisition, processing, and transmission, (2) an adaptable set of "personalized" sensors for measuring vitals and reporting emergency situations, and (3) a secure communication module for remote patient-physician interactions. The users interface with the RMS through an application installed on a mobile device. Using a return of investment (ROI) cost-benefit analysis and a cohort of 2.7 million patients, we estimate that through the implementation of such a system, the patients and the healthcare system would see benefits within one year.