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                <text>The study intended to investigate the sustainability of mobile money in Ghana, which is currently making waves in Ghana. The problem addressed in this research was how mobile money systems are sustained amidst challenges and setbacks to the system. The methods employed to solve the problem was a quantitative approach through the use of a descriptive research design, specifically cross-sectional correlational survey design. From the research results, the study revealed that respondents have knowledge on the sustainability of mobile money, and education is key to make the sustainability of mobile money in Ghana a reality.&#13;
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                <text>In this paper, we assessed volatility of Ghana’s inflation rates for 2000 to 2018 using the auto-regressive conditionally heteroskedasticity (ARCH), generalized ARCH (GARCH), and the exponential GARCH (EGARCH) models. The inflation data were obtained from the Ghana Statistical Service (GSS). The proposed model should be able to provide projections of inflation volatility from 2019 and beyond. The results showed that higher order models are required to properly explain Ghana’s inflation volatility and the EGARCH(12, 1) is the best fitting model for the data. The EGARCH(12, 1) model is robust to model and forecast volatility of inflation rates. Also, the results suggest that we are forecasting increasing volatility and there is increasing trend in general prices of goods and services for 2018 and beyond. The forecasts figures revealed that Ghana’s economy is likely to be unstable in 2018 and 2019. This study therefore recommends that policy makers and industry players need to put in place stringent monetary and fiscal policies that would put the anticipated increase in inflation under control. The models were implemented using R software.</text>
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                <text>Modeling Cholera Epidemiology Using Stochastic Differential Equations</text>
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                <text>Wahab A Iddrisu, Inusah Iddrisu, Abdul-Karim Iddrisu</text>
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                <text>In this study, we extend Codeço’s classical SI‐B epidemic and endemic model from a deterministic framework into a stochastic framework. Then, we formulated it as a stochastic differential equation for the number of infectious individuals I(t) under the role of the aquatic environment. We also proved that this stochastic differential equation (SDE) exists and is unique. The reproduction number, R0, was derived for the deterministic model, and qualitative features such as the positivity and invariant region of the solution, the two equilibrium points (disease‐free and endemic equilibrium), and stabilities were studied to ensure the biological meaningfulness of the model. Numerical simulations were also carried out for the stochastic differential equation (SDE) model by utilizing the Euler‐Maruyama numerical method. The method was used to simulate the sample path of the SI‐B stochastic differential equation for the …</text>
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                <text>Modeling downtime severity of telecommunication networks using discrete time Markov chains</text>
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                <text>Wahab A Iddrisu, Ibrahim A Gedel</text>
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                <text>Telecommunication network reliability remains a top priority for both customers and service providers. Downtime can result in revenue loss for providers and productivity loss for customers. Thus, accurately predicting downtime severity can help providers plan and respond effectively. This study models telecommunication network downtime severity using discrete-time Markov chains (DTMC). The data used consists of 1,211 daily network downtime records, in minutes, recorded by Ghana's national communications authority (NCA) from August 1, 2015, to April 30, 2021. The severity of daily downtime was categorized into 5 categories based on duration. Results indicate that the majority (n= 905) of daily network downtime was negligible, while only 25 outages were severe. The transition probability matrix indicates that if the present network downtime severity is negligible, there is an 81% chance that the next network downtime severity may also be negligible, a 12% chance of minimal severity, a 4% chance of significant severity, a 2% chance of serious severity, and a 1% chance of severe severity. The steady-state distribution indicates that over the long term (n≥ 17), 74% of network downtime severity is expected to be negligible, while only 2% is expected to be severe. Based on probability simulations for 12 steps, it is evident that the'negligible'category is the most probable network downtime severity, regardless of the initial severity category. These findings can assist telecommunication providers in better planning and delivering more reliable services to their customers.</text>
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                <text>Modeling ICT adoption and electricity consumption in emerging digital economies: Insights from the West African Region</text>
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                <text>Isaac Ankrah, Michael Appiah Kubi, Sampson Twumasi-Ankrah, Frank Gyimah Sackey, Richard Asravor, Brenya Boahemaa, Derrick Donkor, Lilian Arthur, Christopher Lamptey, Eric Ekobor-Ackah Mochiah</text>
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                <text>This study investigates the impact of Information and Communication Technologies (ICT) on electricity consumption in West Africa, employing a dynamic panel data model. The results show a significant long-term positive effect of ICT adoption on electricity consumption. Notably, internet connections increase the demand for electricity, with estimates ranging from 13.4 % to 19.3 %. While mobile phone subscriptions demonstrate modest positive effect of 6.85 %, personal computer ownership appears to have a negligible impact.&#13;
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                <text>Isaac Ankrah, Michael Appiah Kubi, Sampson Twumasi-Ankrah, Frank Gyimah Sackey, Richard Asravor, Brenya Boahemaa, Derrick Donkor, Lilian Arthur, Christopher Lamptey, Eric Ekobor-Ackah Mochiah</text>
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                <text>This study investigates the impact of Information and Communication Technologies (ICT) on electricity consumption in West Africa, employing a dynamic panel data model. The results show a significant long-term positive effect of ICT adoption on electricity consumption. Notably, internet connections increase the demand for electricity, with estimates ranging from 13.4 % to 19.3 %. While mobile phone subscriptions demonstrate modest positive effect of 6.85 %, personal computer ownership appears to have a negligible impact.&#13;
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