Forecast Incidence of Dengue Fever Cases in Fiji Utilizing Autoregressive Integrated Moving Average (ARIMA) Model

This paper examined the trend of dengue fever cases obtained from January 1995 to July 2017 from National Notifiable Disease Surveillance System (NNDSS) records, Fiji Ministry of Health and Medical Services. Box-Jenkins technique and model is applied to forecast incidence of dengue cases from August 2017 until December 2018. ARIMA model is proposed to forecast incidence of dengue fever in Fiji through Box-Jenkins approach. The Augmented Dickey Fullers test revealed that the time series data had unit root indicating non-stationary. The Autocorrelation and Partial Auto-correlation plots of the first order difference of the dengue fever data suggested parameters ARIMA(3,0,4) and ARIMA(3,1,4). The model ARIMA(3,0,4) was determined as the best fitted model which made a good forecasting performance in estimating the expected incidence dengue cases with lower Mean Absolute Percentage Error (MAPE) of 1148.319 and lower Bayesian Information Criterion (BIC) of 11.389. Finally, a forecast for dengue cases was obtained indicating the highest number of cases for December 2018 with estimated cases of 265. The ARIMA model method utilized in this paper forecasted the incidence trend of dengue fever cases effectively. Such results would be beneficial to health professionals and policy makers in planning of public health interventions and improvement to such disease epidemics. The efficacy of expected cases of dengue fever accomplish not only in detecting outbreaks, but also in delivering decision makers with a reasonable trend of the variability of future observations encompassing both historical, recent information and for evidence based decision making purposes

A Goal Programming Approach: Multi-objective Optimization

The purpose of this paper is to accentuate the development of multi-objective non-linear programming (MONLP) technique and its advantages of applying to numerical problems. In particular, non-linear programming model is the process of solving an optimization problem defined by a system of inequalities along with an objective function of several variables that exist in various fields. In certain instances, there are situations in these fields where multiple objectives are required to be achieved simultaneously, owing to limited timeframe and convenience of budget. The Multi-objective programming under non-linear conditions and the solution procedure on the goal programming approach is embedded with algorithm and the relevant technique is developed. Numerical examples, specifically, multi-objective quadratic programming problem and examples of other multi-objective non-linear programming problem are presented to illustrate practical use and the computational details of the proposed procedure. The proposed goal programming technique is then solved using a user-friendly optimization software LINGO