Modelling of the Abundance of Malaria Mosquitoes Using Poisson Mixed Model

Anopheles funestus and Anopheles gambiae are malaria vector mosquitoes. Knowing their resting behavior is important for implementing control methods. The aim of this study was to investigate the resting behaviour of the two malaria mosquitoes. The study was conducted in Kilombero River Valley and mosquitoes were collected using indoor and outdoor traps from 2012-2015. Poisson mixed models were used to quantify the impact of environment variables on resting behaviour. A log ratio rate between the type of trap and its interaction with environmental variables was used to determine if there was a change over time in the resting behaviour. A total of 4696 mosquitoes were resting indoors of which 57% were A. funestus and 43% were A. gambiae. Similarly, a total of 12,028 mosquitoes were resting outdoor of which 13% were A. funestus and 87% were A. gambiae. Temperature was significant and affected the resting behaviour of A. funestus. Humidity, saturation deficit and temperature were significant variables influencing the resting behaviour of A. gambiae. A. funestus was resting indoor while A. gambiae was resting outdoor over time generally. The findings of this study on the effects of environmental variables and the variations in the resting behaviour of A. gambiae and A. funestus could be used as a guide to implementing appropriate intervention measures such as indoor residential spraying (IRS), insecticide treated nets (ITNs) and mosquito repellents.

Forecasting Annual International Tourist Arrivals in Zambia Using Holt-Winters Exponential Smoothing

Tourism is one of the major contributors to foreign exchange earnings to Zambia and world economy. Annual International tourist arrivals in Zambia from 1995 to 2014 are considered in this paper. In this study we evaluated the model performance of Auto-Regressive Integrated Moving Average (ARIMA) and Holt Winters exponential smoothing (HWES). The error indicators: Mean percentage error (MPE), Mean absolute error (MAE), Mean absolute scaled error (MASE), Root-mean-square error (RMSE) and Mean absolute percentage error (MAPE) showed that HWES is an appropriate model with reasonable forecast accuracy. The HWES (α = 1, β = 0.1246865) showed smallest error than those of the ARIMA (0, 1, 2) models. Hence, the HWES (α = 1, β = 0.1246865) can be used to model annual international tourist arrivals in Zambia. Further, forecasting results give a gradual increase in annual international tourist arrivals of about 42% by 2024. Accurate forecasts are key to new investors and Policymakers. The Zambian government should use such forecasts in formulating policies and making strategies that will promote the tourism industry.

Modeling Consumer Price Index in Zambia: A Comparative Study between Multicointegration and Arima Approach

Consumer Price Index (CPI) is an important indicator used to determine inflation. The main objective of this research was to compare the forecasting ability of two time-series models using Zambia Monthly Consumer Price Index. We used monthly CPI data which were collected from January 2003 to December 2017. The models that were compared are the Autoregressive Integrated Moving average (ARIMA) model and Multicointegration (ECM) model. Results show that the ECM was the best fit model of CPI in Zambia since it showed smallest errors measures. Lastly, a forecast was done using the ECM and results show an average growth rate for food CPI at 6.63% and an average growth rate for nonfood CPI at 7.41%. Forecasting CPI is an important factor for any economy because it is essential in economic planning for the future. Hence, identifying a more accurate forecasting model is a major contribution to the development of Zambia.