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Network Modelling of Infectious Diseases

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dc.contributor.author NAFULA, Cameline
dc.date.accessioned 2021-05-07T11:49:09Z
dc.date.available 2021-05-07T11:49:09Z
dc.date.issued 2014
dc.identifier.uri https://repository.maseno.ac.ke/handle/123456789/3731
dc.description.abstract Mathematical modelling of infectious diseases is an important tool for assessing disease dynamics. This branch of mathematics has provided many significant insights concerning the epidemiology of infectious diseases. Previous disease models studied normally took the deterministic approach in modelling. This form of modelling, even thoti~h it has helped to bring out some powerful epidemiological insights; has been criticized to be less realistic because it oversimplifies the biology of real world disease dynamics. In recent years, where machines with more computing power have been introduced; more complex modelling scenarios have been developed. Much of this complexity can be incorporated within the population level framework provided by compartmental models. This form modelling of diseases has heavily borrowed techniques from network science to come up with complex epidemiological scenarios that do not make the unrealistic assumptions of homogenous populations. These complex modelling scenarios are believed to give a more realistic picture of epidemiological dynamics thus providing important insights in disease control and prevention. In this study we investigated how network modelling can be incorporated in existing compartmental models. This was done with an aim of gaining a better understanding of different disease properties and more importantly to predict how various disease mitigation and prevention measures can work in different disease scenarios. A comparison of compartmental modelling scenarios and network modelling scenarios is also done using an interactive modelling software called NetLogo. rv : en_US
dc.language.iso en_US en_US
dc.publisher Maseno University en_US
dc.subject Network Modelling of Infectious Diseases en_US
dc.title Network Modelling of Infectious Diseases en_US
dc.type Thesis en_US


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