Forecast of Dengue Hemorrhagic Fever Cases Based on Climate and Population Density Data Using Autoregressive Integrated Moving Average
DOI:
https://doi.org/10.53770/amhj.v5i4.816Keywords:
Dengue Fever, Forecasting, Temperature, Humidity, RainfallAbstract
Dengue fever remains a major public health problem in Bali, with Denpasar consistently reporting high incidence rates in recent years. However, limited studies have quantitatively examined the influence of climate variability on dengue fever incidence and its temporal trends in this area. This study aimed to predict the trend of dengue fever incidence and to assess the impact of climate factors on dengue occurrence in Denpasar. This observational study used secondary data and was analyzed using cross-correlation, Pearson correlation, and Autoregressive Integrated Moving Average (ARIMA) time series modeling. The results of cross-correlation analysis showed that temperature had a significant negative correlation with dengue incidence, while rainfall showed a significant positive correlation. Humidity was not significantly associated with dengue incidence. The ARIMA model demonstrated good predictive performance with an R-squared value of 0.698, indicating that approximately 69.8% of the variation in dengue incidence could be explained by the model. The model also identified a consistent increase in dengue cases at the beginning of the year. These findings indicate that climate factors, particularly temperature and rainfall, play a significant role in influencing dengue incidence in Denpasar. The ARIMA model provides a reliable tool for early prediction of dengue outbreaks. Therefore, vector control and preventive interventions should be intensified at least one month prior to the expected increase in cases, particularly during periods of high rainfall.
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