Show simple item record

dc.contributor.advisorKonur, Savas
dc.contributor.advisorPeng, Yonghong
dc.contributor.advisorAsyhari, A.Taufiq
dc.contributor.authorModu, Babagana
dc.date.accessioned2022-03-09T10:57:55Z
dc.date.available2022-03-09T10:57:55Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/10454/18771
dc.description.abstractThe uninterrupted spread of malaria, besides its seasonal uncertainty, is due to the lack of suitable planning and intervention mechanisms and tools. Several studies have been carried out to understand the factors that affect the development and transmission of malaria, but these efforts have been largely limited to piecemeal specific methods, hence they do not offer comprehensive solutions to predict disease outbreaks. This thesis introduces a ’holistic’ approach to understand the relationship between climate parameters and the occurrence of malaria using both mathematical and computational methods. In this respect, we develop new climate-based models using mathematical, agent-based and data-driven modelling techniques. A malaria model is developed using mathematical modelling to investigate the impact of temperature-dependent delays. Although this method is widely applicable, but it is limited to the study of homogeneous populations. An agent-based technique is employed to address this limitation, where the spatial and temporal variability of agents involved in the transmission of malaria are taken into account. Moreover, whilst the mathematical and agent-based approaches allow for temperature and precipitation in the modelling process, they do not capture other dynamics that might potentially affect malaria. Hence, to accommodate the climatic predictors of malaria, an intelligent predictive model is developed using machine-learning algorithms, which supports predictions of endemics in certain geographical areas by monitoring the risk factors, e.g., temperature and humidity. The thesis not only synthesises mathematical and computational methods to better understand the disease dynamics and its transmission, but also provides healthcare providers and policy makers with better planning and intervention tools.en_US
dc.language.isoenen_US
dc.rights<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.eng
dc.subjectAgent-based modellingen_US
dc.subjectClimate-factorsen_US
dc.subjectMathematical modellingen_US
dc.subjectPredictionen_US
dc.subjectMachine learningen_US
dc.subjectMalaria transmissionen_US
dc.subjectPreventionen_US
dc.subjectControlen_US
dc.subjectInterventionen_US
dc.subjectMalariaen_US
dc.titleA Holistic Approach to Dynamic Modelling of Malaria Transmission. An Investigation of Climate-Based Models used for Predicting Malaria Transmissionen_US
dc.type.qualificationleveldoctoralen_US
dc.publisher.institutionUniversity of Bradfordeng
dc.publisher.departmentFaculty of Engineering and Informaticsen_US
dc.typeThesiseng
dc.type.qualificationnamePhDen_US
dc.date.awarded2020
refterms.dateFOA2022-03-09T10:57:55Z


Item file(s)

Thumbnail
Name:
13018210 B MODU - Final Thesis.pdf
Size:
4.520Mb
Format:
PDF
Description:
PhD Thesis

This item appears in the following Collection(s)

Show simple item record