Modelling Covid-19 Transmission In Ghana Using A Discrete-Time Markov Model And Machine Learning Time-Series Forecasting Algorithms

dc.contributor.authorKoduah, P.P.
dc.date.accessioned2024-02-14T13:16:07Z
dc.date.available2024-02-14T13:16:07Z
dc.date.issued2022-09
dc.descriptionMPhil. Actuarial Scienceen_US
dc.description.abstractThe COVID-19 pandemic has and continue to have a severe impact on the health sectors, businesses, economies, and the world at large, despite many healthcare interventions, with much still yet to be learnt regarding its infection dynamics. In addition, researchers have developed classical compartmental or epidemiological models and other advanced mathematical models to better explain COVID- 19 infection dynamics across many countries. Critical information, such as the likelihood of first infection and recovery, average infection duration before this infection dies out entirely, COVID-19 infected people's life expectancy, and generalised transition probabilities, is understudied at any given future time. Using nationwide aggregated COVID-19 datasets and a discrete-time Markov model (to estimate these key disease metrics), the current study adds to our understanding of COVID-19 infection dynamics in Ghana. Additionally, the predictive power of some existing state-of-the-art machine learning (ML) algorithms such as K-Nearest Neighbor regression (KNN), Neural Network Auto-Regressive (NNAR), Generalized Regression Neural Network (GRNN), Multi-Layer Perceptron (MLP), and Extreme Learning Machines (ELM) in forecasting daily cases of COVID-19 infection (over the study period) is investigated using an out-of sample rolling-origin evaluation by exploring the trade-o_ between computational speed and accuracy. It was estimated that there would be a prolonged COVID- 19 transmission for at least 150 years before infection could die out. The study supports the idea that with a high overall recovery rate, a low infection rate, and a longer infection period, there is a possibility of herd immunity (as evident in the 2021 infection period despite the relatively high overall rate of infection). Finally, the K-Nearest Neighbour (KNN) regression was found to be the most cost-effective ML algorithm to predict the daily cases of COVID-19 in Ghana via the rolling-origin evaluation strategy.en_US
dc.identifier.urihttp://ugspace.ug.edu.gh:8080/handle/123456789/41245
dc.language.isoenen_US
dc.publisherUniversity Of Ghanaen_US
dc.subjectGhanaen_US
dc.subjectCovid-19en_US
dc.subjectMachineen_US
dc.subjectTransmissionen_US
dc.titleModelling Covid-19 Transmission In Ghana Using A Discrete-Time Markov Model And Machine Learning Time-Series Forecasting Algorithmsen_US
dc.typeThesisen_US

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