This study examined the applicability of artificial neural network models in modelling univariate time series ambulance demand for short-term forecasting horizons in Zimbabwe. Bulawayo City Councils’ ambulance services department was used as a case study. Two models, feed-forward neural network (FFNN) and seasonal autoregressive integrated moving average, (SARIMA) were developed using monthly historical data from 2010 to 2017 and compared against observed data for 2018. The mean absolute error (MAE), root mean square error (RMSE), and paired sample t-test were used as performance measures. Calculated performance measures for FFNN were MAE (94.0), RMSE (137.19), and the test statistic value p=0.493(>0.05) whilst corresponding values for SARIMA were 105.71, 125.28, and p=0.005(<0.05), respectively. Findings of this study suggest that the FFNN model is inclined to value estimation whilst the SARIMA model is directional with a linear pattern over time. Based on the performance measures, the parsimonious FFNN model was selected to predict short-term annual ambulance demand. Demand forecasts with FFNN for 2019 reflected the expected general trends in Bulawayo. The forecasts indicate high demand during the months of January, March, September, and December. Key ambulance logistic activities such as vehicle servicing, replenishment of essential equipment and drugs, staff training, leave days scheduling, and mock drills need to be planned for April, June, and July when low demand is anticipated. This deliberate planning strategy would avoid a dire situation whereby ambulances are available but without adequate staff, essential drugs, and equipment to respond to public emergency calls.

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Aim/purpose – The underground economy is a major challenge across the world affect- ing both developed and developing economies. South Africa is no exception to this phenomenon and has lost billions of rands due to the underground economy. The aim of this study is to estimate the size of the underground economy in South Africa. Design/methodology/approach – The study used quarterly time series data from 2000 to 2020 and employed the Currency Demand Approach (CDA) for modeling the under- ground economy. Findings – The model results revealed that the underground economy is positively influ- enced by unemployment rate, tax burden, and social benefits granted by the government, however it is negatively influenced by Nominal Gross Domestic Product (NGDP), deposit interest rate, and self-employment rate. Furthermore, the study showed that there was a distinct growth of the underground economy, from 23.9% of GDP in 2003 to 34.5% of GDP in 2019. On average, the underground economy represented 28.8% of GDP for the period 2003 to 2020. Research implications/limitations – This model can be used in conjunction with other models to observe the trend in the South African underground economic activities. The South African government should take note of the spiraling growth of this economy and come up with measures to curb this growth to protect the formal economy. Originality/value/contribution – This study makes a significant contribution to the body of knowledge in this research area and provides much needed insights into the magnitude of the underground economy and the extent of tax evasion in South Africa. Keywords: underground economy, South Africa, currency demand approach. JEL Classification: C32, O17, H26, C53

There is a vast amount of geo-referenced data in many fields of study including ecological studies. Geo-referencing is usually by point referencing; that is, latitudes and longitudes or by areal referencing, which includes districts, counties, states, provinces and other administrative units. The availability of large geo-referenced datasets for modelling has necessitated the development and application of spatial statistical methods. However, spatial varying coefficients models exploring the abundance of tick counts remain limited. In this study we used data that was collected and prepared by researchers in the Department of Biological Sciences from the Old Dominion University, Virginia, USA. We modelled tick life-stage counts and abundance variability from 12 sampling locations, with 5 different habitats (numbered 1-5), three habitat types; namely: woods, edges and grass; collected monthly from May 2009 through December 2018. Spatio-temporal Poisson and spatio-temporal negative binomial (NB) count data models were fitted to the data and compared using the deviance information criteria (DIC). The NB model outperformed the Poisson models with all its DIC values being smaller than those of the Poisson model. Results showed that the covariates varied spatially across counties. There was a decreasing time (in years) effect over the study period. However, even though the time effect was decreasing over the study period, space-time interaction effects were seen to be increasing over time in York County.