| dc.contributor.author | Luwero, Peter | |
| dc.contributor.author | Karungi, Cynthia Samantha | |
| dc.contributor.author | Nkono, Michael | |
| dc.contributor.author | Kajubi, Sepi Junias | |
| dc.date.accessioned | 2024-01-22T09:08:53Z | |
| dc.date.available | 2024-01-22T09:08:53Z | |
| dc.date.issued | 2023 | |
| dc.identifier.citation | Luwero, P. et al (2023). Med-Hunch(Unpublished undergraduate dissertation). Kampala: Makerere University | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.12281/18346 | |
| dc.description | A Project Report Submitted to the School of Computing and Informatics Technology for the Study Leading to a Project Report in Partial Fulfilment of the requirements for the Award of the Degree of Bachelor of Science in Computer Science of Makerere University | en_US |
| dc.description.abstract | Uganda regularly experiences an outbreak of Ebola virus disease (EVD). According to World Health Organisation (WHO) situation reports, the latest outbreak began in late September 2022 and was localised to the central region of the country [1]. In response to the cataclysm, The Med-hunch is a machine learning software designed to analyse the dynamics of this outbreak to anticipate further spread of EVD and investigate the effects of control interventions. Our system provides recommendations on the appropriate type of recommended counter-measures to the Ebola Virus Disease (EVD) through automated pattern and prediction analysis tools that can be used to determine the spread and therefore utilize the data to draw up an optimal management system for the outbreaks and eradication of the disease. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Makerere University | en_US |
| dc.subject | Machine Learning Software | en_US |
| dc.subject | Med-hunch | en_US |
| dc.title | Med-Hunch | en_US |
| dc.type | Thesis | en_US |
