Abstract:
The number of dengue fever, one of the most dangerous mosquito viral infection,
cases has dramatically increased in recent years. Since the first reported case of
dengue fever in 1965, there had been occurrences on and off until the recent.
Previous studies based on meteorological factors and the epidemiological pattern
of cases are recommended for effective control programmes. Consequently, several
programmes are conducted Island wide to control the cases. A mechanism is
needed to estimate the number of cases and thus this study will provide support to
forecast them. Therefore, the objective of the study is to develop a time series
model to predict the dengue fever cases in future.
Statistical tests are used to construct Auto Regressive Moving Averages (ARMA)
models to predict the number of dengue cases. At preliminary stage, graphs of
Auto Correlation Function, Partial Auto Correlation Function and Augmented
Dickey Fuller test is used to test stationary of the series. For model selection,
coefficient of determination, Durbin-Watson statistics, Akaike information
criterion and Schwartz’s Bayesian criterion are used. Diagnostics tests on residuals
are also carried out. Mean Absolute Percentage Error (MAPE) statistics is used to
measure the accuracy of the model.
The results reveal that 4 cases per day are recorded in Sri Lanka. Further, it shows
that in two seasons: December- January and June- July; the cases are very high.
The accuracy of the fitted model shows more than 75%. Therefore, the proposed
model can be used to forecast the number of dengue cases in Sri Lanka.
