Spatial analysis of the distribution of reported dengue incidence in the National Capital Region, Philippines

BACKGROUND AND OBJECTIVE: With an aim of developing an effective disease monitoring and surveillance of dengue fever, this study intends to analyze the spatial distribution of dengue incidences in the National Capital Region (NCR), across four years of reported dengue cases.MATERIALS AND METHODS: Data used was provided by the Department of Health (DOH) consisting of all reported dengue cases in NCR from 2010-2013. For mapping and visualization, a shapefile of NCR was made readily available by www.philgis.org. Both Moran's I and Kulldorff's spatial scan statistics (SaTScan) were used to identify clusters across the same time period.RESULTS AND CONCLUSION: The analyses identified significant clustering of dengue incidence and revealed that the northern cities of NCR, such as Caloocan, Malabon, Navotas and Valenzuela, exhibited high spatial autocorrelation using local Moran's I and Kulldorff's SaTScan. A temporal analysis of the results also suggested movement in increased dengue incidence through time, from the northwest cities to the northeast cities. Presence of spatial autocorrelation in dengue incidence suggests possible enhancements of early detection schemes for dengue surveillance. Moreover, the results of these analyses will be of interest to both policymakers and health experts in providing a basis for which they can properly allocate resources for the prevention and treatment of dengue fever.

Sequence-dependent cluster analysis of biomineralization peptides.

A reliable and statistically valid classification of biomineralization peptides is herein presented. 27 biomineralization peptides (BMPep) were randomly selected as representative samples to establish the classification system using k-means method. These biomineralization peptides were either discovered through isolation from various organisms or via phage display. Our findings show that there are two types of biomineralization peptides based on their length, molecular weight, heterogeneity, and aliphatic residues. Type-1 BMPeps are more commonly found and exhibit higher values for these significant clustering variables. In contrast are the type-2 BMPeps, which have lower values for these parameters and are less common. Through our clustering analysis, a more efficient and systematic approach in BMPep selection is possible since previous methods of BMPep classification are unreliable.