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This paper investigates the levels and determinants of desired and actual number of children, and the risk of having more than two children in Ghana and Nigeria using 2013 and 2014 Demographic Health Survey (DHS) conducted in the two countries, respectively. The question is whether the rate of childbearing in sub-Saharan African countries can be slowed down by changes in fertility demand-driven factors such as economic, socio-cultural and family planning programs. Our analysis results showed that these two countries are yet to adopt replacement level policy. Thus, putting every socio-demographic conditions that support fertility in place, these sub-Saharan African countries will continue to increase average childbearing per household, particularly in the case of Nigeria. Thus, government could intervene in the area of education of mothers since this will delay the age at first birth and serve as avenue to orientate women on family planning measures.

The dataset investigates the magnitude of the misinformation content influencing scepticisms about the novel COVID-19 pandemic in Africa. The data is collected via an electronic questionnaire method and twenty-one Africa countries randomly participated. Responses were received from all the five regions of Africa. The data is structured to identify some leading misinformation been propagated in the media. For data, in brief, we performed a descriptive analysis of the data and also examine the degree of each selected misinformation contents on the immune perception of respondents using Confirmatory Factor Analysis. Another research can use the dataset to investigate how misinformation and religion misconception promote ignorance about disease or pandemic in Africa or the dataset could serve as supplementary material for further investigation of COVID-19 pandemic in Africa.

Coronaviruses are types of viruses that are widely spread in humans, birds, and other mammals, leading to hepatic, respiratory, neurologic, and enteric diseases. The disease is presently a pandemic with great medical, economical, and political impacts, and it is mostly spread through physical contact. To extinct the virus, keeping physical distance and taking vaccine are key. In this study, a dynamical transmission compartment model for coronavirus (COVID-19) is designed and rigorously analyzed using Routh–Hurwitz condition for the stability analysis. A global dynamics of mathematical formulation was investigated with the help of a constructed Lyapunov function. We further examined parameter sensitivities (local and global) to identify terms with greater impact or influence on the dynamics of the disease. Our approach is data driven to test the efficacy of the proposed model. The formulation was incorporated with available confirmed cases from January 22, 2020, to December 20, 2021, and parameterized using real-time series data that were collected on a daily basis for the first 705 days for fourteen countries, out of which the model was simulated using four selected countries: USA, Italy, South Africa, and Nigeria. A least square technique was adopted for the estimation of parameters. The simulated solutions of the model were analyzed using MAPLE-18 with Runge–Kutta–Felberg method (RKF45 solver). The model entrenched parameters analysis revealed that there are both disease-free and endemic equilibrium points. The solutions depicted that the free equilibrium point for COVID-19 is asymptotic locally stable, when the epidemiological reproduction number condition $$(R_{0}<1)$$ ( R 0 < 1 ) . The simulation results unveiled that the pandemic can be controlled if other control measures, such as face mask wearing in public areas and washing of hands, are combined with high level of compliance to physical distancing. Furthermore, an autonomous derivative equation for the five-dimensional deterministic was done with two control terms and constant rates for the pharmaceutical and non-pharmaceutical strategies. The Lagrangian and Hamilton were formulated to study the model optimal control existence, using Pontryagin’s Maximum Principle describing the optimal control terms. The designed objective functional reduced the intervention costs and infections. We concluded that the COVID-19 curve can be flattened through strict compliance to both pharmaceutical and non-pharmaceutical strategies. The more the compliance level to physical distance and taking of vaccine, the earlier the curve is flattened and the earlier the economy will be bounce-back.

The study investigates the impact of uncertainties on energy pricing during the COVID-19 pandemic using five uncertainty measures that include the COVID-Induced Uncertainty (CIU), Economic Policy Uncertainty (EPU), Global Fear Index (GFI); Volatility Index (VIX), and the Misinformation Index of Uncertainty (MIU). The data, which span between 2-January, 2020 and 19-January, 2021, corresponding to the period of the COVID-19 pandemic. The study finds energy prices to respond significantly to the examined uncertainty measures, with EPU seen to affect the prices of most energy types during the pandemic. We also find predictive potentials inherent in VIX, CIU, and MIU for global energy sources.

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