Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
| dc.contributor.advisor | Shi, Wenzhong John (LSGI) | - |
| dc.creator | Wang, Bin | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/8245 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Analyzing and predicting risks of infectious diseases by geographic information science | en_US |
| dcterms.abstract | Epidemic waves of new emerging infectious diseases have awakened global concerns regarding their potential pandemic threats.Early prevention and control measures can prevent the spread or even control the outbreak of an infectious disease.Geographic information science (GIS) is used in this study as an integrated platform for epidemic surveillance.Combined with powerful spatial data management and statistical analysis methods,geospatial technologies offer a new perspective to model how a disease may spread together with its evolutionary path.This tool enables policy makers to explore the spatial interactions between disease emergence and its risk or protective factors,thereby allowing epidemiologists or public health officials to target areas with more effective means to control a disease spread.This research aims to develop innovative models within the GIS framework for characterizing the spatial and temporal distribution patterns of an infectious disease,to assist the planning of preventive intervention measures.Firstly,it summarizes background,methods and research developments in emerging infectious diseases.Chapter 2 gives a description of relevant experimental data in GIS and Epidemiology based on H1N1 of Hong Kong in 2009, H7N9 of Mainland China in 2013,and the Ebola epidemic of West Africa in 2014.An in-depth discussion of elementary analysis methods - Standard Deviational Ellipse (SDE),is introduced in Chapter 3,using H1N1 infection of Hong Kong to highlight the spatiotemporal concentrations.Mathematics has long been a powerful tool for understanding and assessing the disease spread. Understanding the how,when,and why an epidemic spreads across a geographic landscape is of critical importance,as effective preventive measures can be put in place before a disaster occurs.Chapter 4 devotes to discussing how the temporal dynamics of infectious disease are modeled by basic SIR compartmental models, and how the meta-population model is used to characterize the spatiotemporal movement of a disease infection. The typical reaction diffusion equation models are also thoroughly explored, followed by a detailed description of computer implementation procedures using the Runge-Kutta method. | en_US |
| dcterms.abstract | Spatiotemporal analysis can potentially contribute to characterizing the temporal evolution process and revealing possible spatial propagation patterns. As such, an innovative approach was proposed in Chapter 5 to examine the impact of spatiotemporal proximity upon the onset risk prediction of an emerging infectious disease.Experiments based on the avian influenza A H7N9 that occurred in eastern China from February to May 2013 demonstrated that such spatiotemporal proximity integrated approach was capable of providing approximately 70% correct prediction on average in predicting the H7N9 illness onset risk for the 5 days following the forecast date.Furthermore,a sequential Bayesian inference combined with stochastic SEIR model has been employed to estimate the time-varying effective reproduction numbers, together with their 95% confidence intervals, for the Ebola virus epidemic in West Africa. Experimental results indicated that concerted efforts should be made to halt all transmission in Liberia for the dreadful reproduction number there. Based on the aforementioned theoretical models, a software prototype framework has resulted for further development and to enable the analysis of spatiotemporal spreading patterns and dynamic evolution trends of an infectious disease. Discussions regarding the limitations and potentialities of the models explored here are also carried out for guiding future research work to better prevent the infectious disease spread. | en_US |
| dcterms.extent | x, 132 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2015 | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.LCSH | Public health -- Geographic information systems. | en_US |
| dcterms.LCSH | Medical mapping. | en_US |
| dcterms.LCSH | Medical geography. | en_US |
| dcterms.LCSH | Communicable diseases -- Prevention. | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b28261586.pdf | For All Users | 5.21 MB | Adobe PDF | View/Open |
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