Emerging infectious disease, the household built environment characteristics, and urban planning: Evidence on avian influenza in Vietnam.

Recent concerns with pandemic outbreaks of human disease and their origins in animal populations have ignited concerns regarding connections between Emerging Infectious Diseases (EID) and development. As disasters, health, and infectious disease become part of planning concern (Matthew & McDonald, 2007), greater focus on household infrastructure and EID disease outbreaks among poultry is warranted. Following Spencer (2013), this study examines the relationship between the mix of household-scale water supplies, sanitation systems, and construction materials, and Highly Pathogenic Avian Influenza (HPAI) among poultry in a developing country: Vietnam. Findings of our multivariate logistic regressions suggest that a non-linear, Kuznets-shaped urban transition (Spencer, 2013) has an independent effect on the outbreak of HPAI, especially as it relates to household-level sanitation infrastructure. We conclude that the Kuznets-shape development of household infrastructure characteristics in Vietnam play a significant role in explaining where poultry outbreaks occur. Using secondary data from the Census of Population and Housing, and the Agricultural Census at the District and Commune levels for the country of Vietnam, we performed logistic regression to test the relationship between outbreaks of HPAI in poultry and newly-developed "coherence indices" (Spencer, 2013) of household water supply, sanitation, and construction materials that measure nonlinear, transitional development. Results show that district-scale coherence indices are negatively and independently correlated with HPAI outbreaks, especially for sanitation. Findings also suggest that community-scale coherence of urban infrastructures is a powerful tool for predicting where HPAI poultry outbreaks are likely to occur, thereby providing health planners new tools for efficient surveillance.

Study on non-linear bistable dynamics model based EEG signal discrimination analysis method.

Electroencephalogram (EEG) is the recording of electrical activity along the scalp. EEG measures voltage fluctuations generating from ionic current flows within the neurons of the brain. EEG signal is looked as one of the most important factors that will be focused in the next 20 years. In this paper, EEG signal discrimination based on non-linear bistable dynamical model was proposed. EEG signals were processed by non-linear bistable dynamical model, and features of EEG signals were characterized by coherence index. Experimental results showed that the proposed method could properly extract the features of different EEG signals.