ABSTRACT
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 out-of-sample incidence.
ABSTRACT
The use of principal component analysis (PCA) for soil heavy metals characterization provides useful information for decision making and policies regarding the potential sources of soil contamination. However, the concentration of heavy metal pollutants is spatially heterogeneous. Accounting for such spatial heterogeneity in soil heavy metal pollutants will improve our understanding with respect to the distribution of the most influential soil heavy metal pollutants. In this study, geographically weighted principal component analysis (GWPCA) was used to describe the spatial heterogeneity and connectivity of soil heavy metals in Kumasi, Ghana. The results from the conventional PCA revealed that three principal components cumulatively accounted for 86% of the total variation in the soil heavy metals in the study area. These components were largely dominated by Fe and Zn. The results from the GWPCA showed that the soil heavy metals are spatially heterogeneous and that the use of PCA disregards this considerable variation. This spatial heterogeneity was confirmed by the spatial maps constructed from the geographically weighted correlations among the variables. After accounting for the spatial heterogeneity, the proportion of variance explained by the three geographically weighted principal components ranged between 85% and 89%. The first three identified GWPC were largely dominated by Fe, Zn and As, respectively. The location of the study area where these variables are dominated provides information for remediation.
ABSTRACT
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.
