A study of spatial-temporal distribution of Human Swine Influenza in Hong Kong based on GIS and spatial analysis

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A study of spatial-temporal distribution of Human Swine Influenza in Hong Kong based on GIS and spatial analysis

 

Author: Zhang, Shasha
Title: A study of spatial-temporal distribution of Human Swine Influenza in Hong Kong based on GIS and spatial analysis
Degree: M.Sc.
Year: 2011
Subject: Hong Kong Polytechnic University -- Dissertations
H1N1 influenza -- China -- Hong Kong
Spatial analysis (Statistics)
Geographic information systems
Department: Dept. of Land Surveying and Geo-Informatics
Pages: xii, 90 leaves : ill., maps ; 30 cm.
InnoPac Record: http://library.polyu.edu.hk/record=b2412280
URI: http://theses.lib.polyu.edu.hk/handle/200/5965
Abstract: Following news of the flu outbreak in Mexico and the United States, the pandemic started its journey in Hong Kong with the first reported case of swine flu on 1st May, 2009. Up to 25 November 2009, there have been 32,301 confirmed cases of swine flu in the city. This project aims to investigate the spatial and temporal distribution of Human Swine Influenza A (H1N1) in Hong Kong based on GIS and spatial analysis techniques. The primary measures we took were Bayesian modeling and other exploratory analysis methods. Using case event data, Buffer Analysis was adopted for the investigation of incidence of infective cases. Daily incidence of each infective case and corresponding transmission efficiency were obtained. In count event section, Moran's I and LISA statistics were used to examine the spatial autocorrelation of area data (count of cases) aggregated by two different divisions: administrative 18 districts of Hong Kong, and grid with fixed cell size. A comparison between the two was conducted, too. In modelling section, two different models were built to simulate the relationship between morbidity and potential influencing factors both spatially and temporally, and as a result, the high risk factors for the transmission of H1N1 in Hong Kong were identified. Besides that, Kernel Density Estimation was also used to detect point patterns intensity for a basic interpretation of disease patterns. From the results of buffer analysis, we found our buffers are useful in predicting coming cases. In spite of missing data and the effect of transmission along traffic lines, the accuracy of our prediction still reach up to nearly 60%. In the exploration of spatial autocorrelation, the results are more detailed and helpful using grid layer with fixed cell size, specifically, when the cell size is about 300 * 300 square meters, the spatial autocorrelation results of map is the most recognizable and reasonable in details. In addition, in spatial modeling, districts with more young people will have more occurrences during the influencing season of H1N1, while in temporal modelling; the results indicate that low temperature and high humidity will aggravate the spread of disease.

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