Analyzing the spatio-temporal relationship between dengue vector larval density and land-use using factor analysis and spatial ring mapping.

BACKGROUND: Dengue, a mosquito-borne febrile viral disease, is found in tropical and sub-tropical regions and is now extending its range to temperate regions. The spread of the dengue viruses mainly depends on vector population (Aedes aegypti and Aedes albopictus), which is influenced by changing climatic conditions and various land-use/land-cover types. Spatial display of the relationship between dengue vector density and land-cover types is required to describe a near-future viral outbreak scenario. This study is aimed at exploring how land-cover types are linked to the behavior of dengue-transmitting mosquitoes. METHODS: Surveys were conducted in 92 villages of Phitsanulok Province Thailand. The sampling was conducted on three separate occasions in the months of March, May and July. Dengue indices, i.e. container index (C.I.), house index (H.I.) and Breteau index (B.I.) were used to map habitats conducible to dengue vector growth. Spatial epidemiological analysis using Bivariate Pearson's correlation was conducted to evaluate the level of interdependence between larval density and land-use types. Factor analysis using principal component analysis (PCA) with varimax rotation was performed to ascertain the variance among land-use types. Furthermore, spatial ring method was used as to visualize spatially referenced, multivariate and temporal data in single information graphic. RESULTS: Results of dengue indices showed that the settlements around gasoline stations/workshops, in the vicinity of marsh/swamp and rice paddy appeared to be favorable habitat for dengue vector propagation at highly significant and positive correlation (p = 0.001) in the month of May. Settlements around the institutional areas were highly significant and positively correlated (p = 0.01) with H.I. in the month of March. Moreover, dengue indices in the month of March showed a significant and positive correlation (p <= 0.05) with deciduous forest. The H.I. of people living around horticulture land were significantly and positively correlated (p = 0.05) during the month of May, and perennial vegetation showed a highly significant and positive correlation (p = 0.001) in the month of March with C.I. and significant and positive correlation (p <= 0.05) with B.I., respectively. CONCLUSIONS: The study concluded that gasoline stations/workshops, rice paddy, marsh/swamp and deciduous forests played highly significant role in dengue vector growth. Thus, the spatio-temporal relationships of dengue vector larval density and land-use types may help to predict favorable dengue habitat, and thereby enables public healthcare managers to take precautionary measures to prevent impending dengue outbreak.

Exploring spatial patterns and hotspots of diarrhea in Chiang Mai, Thailand.

BACKGROUND: Diarrhea is a major public health problem in Thailand. The Ministry of Public Health, Thailand, has been trying to monitor and control this disease for many years. The methodology and the results from this study could be useful for public health officers to develop a system to monitor and prevent diarrhea outbreaks. METHODS: The objective of this study was to analyse the epidemic outbreak patterns of diarrhea in Chiang Mai province, Northern Thailand, in terms of their geographical distributions and hotspot identification. The data of patients with diarrhea at village level and the 2001-2006 population censuses were collected to achieve the objective. Spatial analysis, using geographic information systems (GIS) and other methods, was used to uncover the hidden phenomena from the data. In the data analysis section, spatial statistics such as quadrant analysis (QA), nearest neighbour analysis (NNA), and spatial autocorrelation analysis (SAA), were used to identify the spatial patterns of diarrhea in Chiang Mai province. In addition, local indicators of spatial association (LISA) and kernel density (KD) estimation were used to detect diarrhea hotspots using data at village level. RESULTS: The hotspot maps produced by the LISA and KD techniques showed spatial trend patterns of diarrhea diffusion. Villages in the middle and northern regions revealed higher incidences. Also, the spatial patterns of diarrhea during the years 2001 and 2006 were found to represent spatially clustered patterns, both at global and local scales. CONCLUSION: Spatial analysis methods in GIS revealed the spatial patterns and hotspots of diarrhea in Chiang Mai province from the year 2001 to 2006. To implement specific and geographically appropriate public health risk-reduction programs, the use of such spatial analysis tools may become an integral component in the epidemiologic description, analysis, and risk assessment of diarrhea.

GIS-based analysis of the fate of waste-related pathogens Cryptosporidium parvum, Giardia lamblia and Escherichia coli in a tropical canal network.

Urban canals play a major socio-economic role in many tropical countries and, particularly, Thailand. One of the overlooked functions that they perform is a significant attenuation of waste-related pathogens posing considerable health risk, as well as pollution attenuation in general. The study dealt with a comparison of three canals receiving: (i) municipal, (ii) mainly industrial and (iii) mainly agricultural wastewater, listed in order of progressively decreasing organic loading. The occurrence and fate of waterborne Cryptosporidium parvum, Giardia lamblia and Escherichia coli were monitored in the canals by both real-time PCR and conventionally for 12 months. The pathogens are etiological agents of an estimated 38% and 47% of diarrhea cases worldwide and in Thailand, respectively. The geographic information system (GIS) was used to evaluate and map point and, particularly, non-point pollution sources which allowed differentiating the canal sections in terms of predominant pathogen sources. The flowthrough canals, which can be viewed as waste stabilization ponds, were found to be efficiently removing the pathogens at the following generalized specific rates: 0.3 (C. parvum), 1.2 (G. lamblia), 1.8 (E. coli) log10/km.d in the dry season. The rates decreased in the rainy season for E. coli and G. lamblia, but increased for C. parvum which indicated different removal mechanisms. Data suggest that E. coli and G. lamblia were mainly removed through sedimentation and sunlight (UV) irradiation, while the likely mechanism for C. parvum was predation. Overall, the specific pathogen removal rates positively correlated with the canal organic loading rates in the rainy season. As an important result, an estimate of the municipal pollution mitigation by over 2280 km canals in the Greater Bangkok suggests that concomitant to the pathogens at least 36-95 tons of BOD5 is being removed daily, thereby saving the receiving Chao Phraya River and Bight of Bangkok, by far exceeding current, from major eutrophication problems.