Entropy Correlation and Its Impacts on Data Aggregation in a Wireless Sensor Network.

A correlation characteristic has significant potential advantages for the development of efficient communication protocols in wireless sensor networks (WSNs). To exploit the correlation in WSNs, the correlation model is required. However, most of the present correlation models are linear and distance-dependent. This paper proposes a general distance-independent entropy correlation model based on the relation between joint entropy and the number of members in a group. This relation is estimated using entropy of individual members and entropy correlation coefficients of member pairs. The proposed model is then applied to evaluate two data aggregation schemes in WSNs including data compression and representative schemes. In the data compression scheme, some main routing strategies are compared and evaluated to find the most appropriate strategy. In the representative scheme, with the desired distortion requirement, a method to calculate the number of representative nodes and the selection of these nodes are proposed. The practical validations showed the effectiveness of the proposed correlation model and data reduction schemes.

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats.

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development's (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security.