Modelling the Economic Growth Rate of Ghana using the Solow Model

The main objective of this study was to use the Solow growth model (Augmented Cobb-Douglas production function) as a basis to model the economic growth of Ghana during the period 1990 to 2010. Economic growth around the world has not been equal for a long time. Some economies grow faster than others. Economists have predicted that the slower growing economies will eventually converge to the faster growing economies at some point in time. In this study, we model the economic growth of Ghana using the Solow production model and applying growth differential equations. Starting from the estimates of the parameters from other studies, the growth model was simulated for the period 1990 to 2010. The recording and computation of the data was done using Matlab, SPSS and Excel. The computations were Capital, Labour force, Total Factor Productivity, and Total Production and the results from models were compared with the real GDP growth figures and variations noted. The estimations from the model were compared with the actual figures from the Ghana Statistical Service, World Bank and Bank of Ghana. The model provides a good approximation of the dynamics of the Ghanaian economy for the 1990 to 2010 periods, with respect to the dynamics of the real aggregate GDP growth and to the ratios of the main macroeconomic variables, like production per worker, capital-output ratio or capital per worker. The results showed a very close relationship between the actual and calculated growth rates over the periods 1990 to 2010. The actual average growth rate over the period was 4.5% as compared to the calculated average value of 4.21%. In conclusion, there was a correlation between the actual growth rates and the calculated but the strength was weak. Keywords: Solow growth model, Economic growth of Ghana, Real GDP growth, Macroeconomic variables, actual and calculated growth rat

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century