Browsing by Author "Wiah, Eric Neebo"

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    A comprehensive cost-effectiveness analysis of control of maize streak virus disease with Holling’s Type II predation form and standard incidence
    (Elsevier, 2022-06) Seidu, Baba; Asamoah, Joshua Kiddy K.; Wiah, Eric Neebo; Ackora-Prah, Joseph; 0000-0002-7066-246X
    Maize streak virus disease, caused by the maize streak virus, has been identified as severe vector-borne disease in Africa. In most regions of the continent, the disease is generally uncontrolled, and in epidemic years, it contributes to massive yield losses and famine. We propose a Holling-type predation functional response to explore the disease transmission. We show the sensitivity indices of various embedded parameters in the basic reproduction number. To illustrate the dynamics of the disease of the maize–leafhopper interaction, we perform a numerical simulation, and the results are graphically displayed. Incorporating four control methods (infection control, predation control, removal of infected maize plants, and insecticide application) into the basic model yields an optimal control issue. We used the Incremental Cost-Effectiveness Ratio technique to evaluate the most cost-effective combination of the four controls. We notice that the most cost-effective strategy combines the simultaneous adoption of the four controls.
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    Modelling the volatility of the Ghana stock market: A comparative study
    (International Journal of Statistics and Applied Mathematics, 2023) Agyarko, Kofi; Wiah, Eric Neebo; Frempong, Nana Kena; Odoi, Benjamin; 0000-0002-7138-3526
    The Ghana stock market is considered attractive to both local and international investors, as it is a developing market with potential for growth. The volatility of stock returns is one of the crucial features of Ghana's stock market that should be carefully taken into account by any investor or policymaker. As a result, the GARCH, TGARCH, and EGARCH models were used in this study to analyze the volatility of the Ghanaian stock market. The models were assessed using Akaike Information Criterion (AIC), RMSE and MAPE. The TGARCH (1,1) with generalized error distribution was the model that suited the data the best based on the AIC, RMSE, and MAPE values.