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An Integer-Valued Modelling and Forecast of COVID-19 Cases in Ghana: A Review and Comparative Study

Alexander Boateng, Simon Kojo Appiah, Daniel Maposa


Background: The outbreak of COVID-19 disease has severely affected people's health, livelihoods and economic wellbeing. How long this pandemic will last and when the spread of the disease will be controlled continues to elude everyone.

 Material and methods: This paper empirically examines daily reported cases of COVID-19 in Ghana by comparing the predictive powers of an autoregressive integrated moving average (ARIMA) and integer-valued generalised autoregressive conditional heteroskedasticity (INGARCH) models. This study aims to describe the dependency structure in the counts of cases; predict the number of possible infections, and determine the trend and prevalence of COVID-19 daily cases for the next 21 days. Accuracy measures such as root mean square error (RMSE) and normalised square error (NSE) were utilised in the comparison process.

 Results: The results revealed that the Poisson INGARCH (1,1) model provides a good fit to the data compared to the ARIMA(2,1,1) model in modelling the short and long-term dependence of the daily confirmed cases COVID-19. The study also showed strong evidence of the effect of the lockdown and mass vaccination of the AstraZeneca vaccine in significantly reducing the number of daily cases.

 Conclusion: The current prediction trend depicts a flattening of the curve informed by a slowdown in incidence and prevalence rates. The projection shows that COVID-19 cases may decline in Ghana for the next 21 days (7 April – 27 April 2021). These results will be crucial for public health policy implications.


ARIMA models, COVID-19, NSE, Poisson INGARCH, RMSE

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