Modelling Covid-19 Transmission In Ghana Using A Discrete-Time Markov Model And Machine Learning Time-Series Forecasting Algorithms
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Date
2022-09
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University Of Ghana
Abstract
The 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.
Description
MPhil. Actuarial Science
Keywords
Ghana, Covid-19, Machine, Transmission