Background Climatic factors have been shown to influence communicable disease dynamics especially in tropical regions where temperature could swing from extreme heat and dryness to wet and cold within a short period of time. This is more pronounced in the spread of airborne diseases. In this study, the effect of some local weather variables (average temperature, average relative humidity, average wind speed and average atmospheric pressure) on the risk of Severe Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Ghana is investigated. The daily confirmed new COVID-19 cases were compiled from the Ghana Health Service and the weather data extracted from Weatherbase. The type of relationship between the climatic variable and risk of spread were explored using the Generalized Additive Model (GAM). Results Results obtained showed that wind speed and atmospheric pressure have positive linear relationship with the spread of infection an increase in the risk of COVID-19 spread. In addition, the risk of spread fluctuates for temperature between 24 and 29 °C but sharply decreases when average temperature exceeds 29 °C. The risk of spread of COVID-19 significantly decrease for relative humidity between 72 and 76% and leveled afterwards. Conclusion The results indicate that wind speed and pressure have a positive linear relationship with the risk of spread of COVID-19 whilst temperature and humidity have a non-linear relationship with the spread of COVID-19. These findings highlight the need for policy makers to design effective countermeasures for controlling the spread as we are still within the low temperature season.

Geographically weighted principal component analysis for characterising the spatial heterogeneity and connectivity of soil heavy metals in Kumasi, Ghana, HELIYON, https://doi.org/10.1016/ j.heliyon.2021.e08039. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Prediction of COVID-19 incidence and transmissibility rates are essential to inform disease control policy and allocation of limited resources (especially to hotspots), and also to prepare towards healthcare facilities demand. This study demonstrates the capabilities of nonlinear smooth transition autoregressive (STAR) model for improved forecasting of COVID-19 incidence in the Africa sub-region were investigated. Data used in the study were daily confirmed new cases of COVID-19 from February 25 to August 31, 2020. The results from the study showed the nonlinear STAR-type model with logistic transition function aptly captured the nonlinear dynamics in the data and provided a better fit for the data than the linear model. The nonlinear STAR-type model further outperformed the linear autoregressive model for predicting both in-sample and outof-sample incidence.

At least in the last 10 years, considerable effort has been given to studying the dynamics of fluid flow in porous media. The phenomena is widely applicable in many areas of science and engineering. In many cases, the effect of capillary pressure and discontinuities in the two-phase flow dynamics is not fully clear, especially in petroleum reservoirs. In this paper, we introduce a new method based on the Chebyshev wavelets collocation method and the so-called operational matrices of integration. The method was implemented specifically for an oil-water-phase flow in heterogeneous reservoir using different capillary pressure treatments. Convergence and accuracy of this method were established and used to simulate the partial differential equations governing the two-phase model. The method incorporates the various conditions of the complex governing equations as a single system. The system is subsequently reduced into a simple set of algebraic equations making the problem easier to solve. Numerical results showed that the method is able to account for the expected discontinuities occurring in the flow process. It was also found that these discontinuities or jumps in the two-phase flow are caused by the capillary pressure as expected physically.

In this paper, the Chebyshev wavelet method, constructed from the Chebyshev polynomial of the first kind is proposed to numerically simulate the single-phase flow of fluid in a reservoir. The method was used together with the operational matrices of integration which resulted in an algebraic system of equations. The system of equation was solved for the wavelet coefficient and used to construct the solutions. The efficiency and accuracy of the method were demonstrated through error measurements. Both the root mean square and the maximum absolute error analysis used in the study were within significantly close range. The Chebyshev wavelet collocation method subsequently was observed to closely approximate the analytic solution to the single phase flow model quite well.