Abstract
Background: Coronavirus disease 2019 (COVID-19) is a pandemic that has affected the daily life, governments and economies of many countries all over the globe. Ghana is currently experiencing a surge in the number of cases with a corresponding increase in the cumulative confirmed cases and deaths. The surge in cases and deaths clearly shows that the preventive and management measures are ineffective and that policy makers lack a complete understanding of the dynamics of the disease. Most of the deaths in Ghana are due to lack of adequate health equipment and facilities for managing the disease. Knowledge of the number of cases in advance would aid policy makers in allocating sufficient resources for the effective management of the cases. Methods: A predictive tool is necessary for the effective management and prevention of cases. This study presents a predictive tool that has the ability to accurately forecast the number of cumulative cases. The study applied polynomial and spline models on the COVID-19 data for Ghana, to develop a generalized additive model (GAM) that accurately captures the growth pattern of the cumulative cases. Results: The spline model and the GAM provide accurate forecast values. Conclusion: Cumulative cases of COVID-19 in Ghana are expected to continue to increase if appropriate preventive measures are not enforced. Vaccination against the virus is ongoing in Ghana, thus, future research would consider evaluating the impact of the vaccine.
Highlights
Three months after the emergence of the coronavirus (SARS-CoV-2) in China, about 118,000 confirmed cases and 4,291 associated deaths were reported globally
Growth curves and generalized additive models (GAMs) have been used to assess whether the basic reproductive number of COVID-19 is different across countries and to determine factors that increase the level of an individual’s vulnerability to the virus.[18]
There are methods that can be used to modify the linear regression model to enable them capture non-linear effects. Such modifications lead to polynomial regression, spline regression, and GAMs that are accurate for modeling non-linear relationships between responses and predictor.[9,16,34]
Summary
Three months after the emergence of the coronavirus (SARS-CoV-2) in China, about 118,000 confirmed cases and 4,291 associated deaths were reported globally. Other researchers studied the relationship between urban planning and public health to support decisions and policies in the “fight” against the virus.[4] They looked at how we can leverage on the pandemic to build healthier cities since currently, only a few Ghanaians live in well-planned settlements and majority of Ghanaians are susceptible to the pandemic due to their less hygenic environments.[4] Growth curves and generalized additive models (GAMs) have been used to assess whether the basic reproductive number of COVID-19 is different across countries and to determine factors that increase the level of an individual’s vulnerability to the virus.[18] In this study, linear, polynomial and generalized linear models (GLMs) are employed to explain the growth pattern of the number of cumulative cases of COVID-19 and to predict and forecast the number of cumulative cases in Ghana
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