Communicable disease disaster management

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dc.contributor.author Ndiwalana, Alexandra S.
dc.contributor.other University of Pretoria. Faculty of Engineering, Built Environment and Information Technology. Dept. of Industrial and Systems Engineering
dc.date.accessioned 2011-04-08T14:28:25Z
dc.date.available 2011-04-08T14:28:25Z
dc.date.created 2010-10
dc.date.issued 2011-04-08T14:28:25Z
dc.description Thesis (B Eng. (Industrial and Systems Engineering))--University of Pretoria, 2010. en_US
dc.description.abstract Outbreaks of communicable diseases in the African context are considered disasters. As such, disaster management protocols and methodologies are applied to combat communicable diseases and the resulting consequences. Outbreaks escalate into epidemics and the prediction of outbreaks and epidemics is near impossible. However, it is possible to monitor outbreaks and the associated spread patterns. The intelligence gained can be used for proactive decision making that will facilitate the expedient execution of retrospective disaster management activities. An interactive simulation of outbreaks and the disease transmission that will lead to epidemics was developed. The simulation was focused on two diseases namely H1N1 (Swine- u) and Measles within the City of Johannesburg municipal area. Insight gleaned from this simulation would fa- cilitate proactive decision making in the area and inform future simulation based epidemiology studies. Mathematical epidemiology and Agent Based Modelling (ABM) are two techniques that in combination are expected to produce a realistic simulation. Mathematical epidemiology is the ap- plication of mathematics and related concepts in the study of disease. Compartmental epidemiology is a subset of mathematical epidemiology where individuals of the concerned population or location are grouped into one of three groups. Each group or compartment has one of the following states assigned to it and all its occupants: Susceptible, Infected and Recovered. Measles and H1N1 both have a Susceptible Infected Recovered (SIR) compartmental infectious disease models. When exploring the SIR model in a stochastic context, a Markov Chain is an applicable tool to enable the modelling of inter-state transition of an individual within a popula- tion. ABM is used to study complex systems and to convey how macro phenomena emerge from micro level behaviour and interactions between agents in an environment. An epidemic (macro phe- nomenon) is the consequence of many lower level individual infections and the associated disease transmission (micro phenomena).Compartmental epidemiology is thus used to demonstrate disease transmission while ABM will be the interface that enables simulation of the interaction of humans within a population or environment. en_US
dc.identifier.uri http://hdl.handle.net/2263/16252
dc.language en
dc.language.iso en en_US
dc.rights Copyright: University of Pretoria en_US
dc.subject Mini-dissertations (Industrial and Systems Engineering) en_US
dc.subject Epidemics en_US
dc.subject Disaster management en_US
dc.subject Mathematical epidemiology en_US
dc.subject Markov chain en_US
dc.subject Agent based modelling en_US
dc.title Communicable disease disaster management en_US
dc.type Text en_US


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