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The study objective is to develop a big spatial data model to predict the epidemiological impact of influenza in Vellore, India. Large repositories of geospatial and health data provide vital statistics on surveillance and epidemiological metrics, and valuable insight into the spatiotemporal determinants of disease and health. The integration of these big data sources and analytics to assess risk factors and geospatial vulnerability can assist to develop effective prevention and control strategies for influenza epidemics and optimize allocation of limited public health resources. We used the spatial epidemiology data of the HIN1 epidemic collected at the National Informatics Center during 2009-2010 in Vellore. We developed an ecological niche model based on geographically weighted regression for predicting influenza epidemics in Vellore, India during 2013-2014. Data on rainfall, temperature, wind speed, humidity and population are included in the geographically weighted regression analysis. We inferred positive correlations for H1N1 influenza prevalence with rainfall and wind speed, and negative correlations for H1N1 influenza prevalence with temperature and humidity. We evaluated the results of the geographically weighted regression model in predicting the spatial distribution of the influenza epidemic during 2013-2014.

A chaotic cipher is presented in this paper using 1-Dimensional and 2-Dimensional chaotic maps like logistic, Chebyshev and Arnold cat map. Permutation phase utilizes logistic map followed by Arnold cat map whereas in diffusion phase, Chebyshev's map is used. Subsequently, another complex diffusion matrix is generated from the original image. This matrix is employed to enhance the diffusion effect further. Eventually, strong input image sensitivity is explored due to this diffusion. Simulation results exhibit that the recommended cipher ensures not only high key and entropy value but also less correspondence between nearby pixels along all directions. The key point of this cipher is the high Number of Pixels Change Rate (NPCR) and Unified Average Changing Intensity (UACI) values. Due to this impact, the proposed cipher produces completely random encrypted images.

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