Cheng, Kueh Yee
(2006)
Quantitative forecasting trend of dengue fever and dengue haemorrhagic fever cases in Peninsular Malaysia.
Masters thesis, Universiti Sains Malaysia.
Abstract
Introduction: Forecasting dengue fever (DF} cases (including dengue
haemorrhagic fever, DHF) is important in improving the effectiveness of
control measures by early identification of period with higher DF cases, areas
and populations at risk.
Objective: This study was carried out to describe epidemic, seasonal and
trend-cycle of DF patterns, to determine the best time series forecasting
model, to test the predictability of rainfall and temperature and finally, to
forecast the monthly OF cases in the next two years (2005-2006) in
Peninsular Malaysia which includes 11 states and Federal Territory of Kuala
Lumpur.
Methods: A quantitative forecasting study was conducted using reported
monthly DF cases (including DHF), rainfall and temperature during eight years,
from 1997 to 2004. The OF data were obtained from the Vector Borne
Diseases Section, Ministry of Health Malaysia and rainfall and temperature
data was obtained from Malaysian Meteorological Service, Malaysia. The
patterns of DF were described using decomposition time series method. The
best time series forecasting model was identified among six time series
models. The predictability of rainfall and temperature was tested using linear
regression model and ARIMA mixed model. Finally, the best model was used
to forecast the DF cases in the next two years (2005-2006).Results: During eight years, all states in Peninsular Malaysia faced epidemic
outbreak, especially in 1998 and 2002. Seasonal variation was found in all
states of Peninsular Malaysia except Selangor, Malacca, Perak and Kedah.
Increasing trend of DF cases was observed in seven out of twelve study areas.
Decomposition m~thod was identified as the best forecasting model for all
states except Perak and Terengganu where Winter's and ARIMA with
seasonal components model were the best respectively. However, rainfall and
temperature were not found to be good predictors for DF. With a cut point (i.e.
mean+2SD) for epidemic, several states such as Malacca, Kedah, Penang,
Perlis, and Terengganu were forecasted to have at least one month of
epidemic during 2005 to 2006.
Conclusion: The study successfully identified the best forecasting model, and
calculate forecasts for the next two years for each state in Peninsular
Malaysia, which could help the health management in carrying out the
activities of DF prevention and control by making the appropriate choice of
strategy.
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