Deterministic And Stochastic Simulations Of Infectious Diseases
Modeling the dynamics of infectious disease transmission in a specific region is the main focus of this thesis. Movements of infective individuals from one region to another promote the spread of infectious diseases, such as severe acute respiratory syndrome (SARS) and influenza A (H1N1). The primar...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://eprints.usm.my/43435/1/Tan%20Wai%20Kiat24.pdf http://eprints.usm.my/43435/ |
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| Summary: | Modeling the dynamics of infectious disease transmission in a specific region is the main focus of this thesis. Movements of infective individuals from one region to another promote the spread of infectious diseases, such as severe acute respiratory syndrome (SARS) and influenza A (H1N1). The primary objective of this research is to develop the capability within Malaysian public health authorities to plan and implement intervention strategies that is effective for mitigating future epidemic outbreaks. The collaboration between health authorities and local community is essential in implementing mitigation measures to reduce local infection. For this purpose, an influenza simulation models-based upon the SIR formulation and codenamed FluSiM is developed to investigate the dynamics of infectious disease transmission and to suggest appropriate intervention strategies to control the epidemic outbreak. The user-friendly Window-based FluSiM is developed to aid university graduate students as well as academic researchers in conducting epidemiology related research. This version of FluSiM is also used to simulate the 1918 influenza pandemic in Switzerland, the H1N1 2009 in United States of America (USA) and Furunculosis in salmon population. This deterministic FluSiM is later enhanced into a stochastic model by incorporating stochasticity in disease transmission characteristics. This stochastic FluSiM is utilized to investigate the uncertainties during an epidemic outbreak. Simulations of stochastic FluSiM indicate that significant heterogeneity in population may be a hindrance to implementation of effective interventions. |
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