<![CDATA[Dengue Hemorrhagic Fever (DHF) continues to be one of the leading public health challenges in Indonesia, with North Sumatra experiencing a sharp increase to 8,541 reported cases in 2022. The recurring seasonal pattern of this disease necessitates an approach that can capture cyclic characteristics. Therefore, this study applied circular regression to examine the influence of time (expressed in monthly cycles), rainfall, and temperature on the distribution of DHF cases. The research utilized secondary data collected from the North Sumatra Provincial Health Office for case numbers and from the Meteorology, Climatology, and Geophysics Agency (BMKG) for climate information. Monthly data were transformed into radians, followed by correlation testing and model construction to determine the strength of relationships. The resulting model demonstrated a high explanatory power with an adjusted R² value of 92.44%, indicating strong model fit. Among the independent variables tested, only the sine transformation of the month (sin α), which represents seasonal peaks and troughs, showed a statistically significant contribution to case variation (p < 0.001). In contrast, the cosine transformation (cos α), rainfall, and temperature were not significant predictors, most likely due to relatively stable climatic conditions and the lagged effects of rainfall on mosquito breeding. The model further identified August and September as the months with the highest risk of DHF, aligning with descriptive case data. These findings confirm that seasonal dynamics are the primary driver of dengue transmission in the region. The study concludes that circular regression is an effective analytical tool for diseases with cyclical patterns and can provide essential evidence for public health planning. Strengthening preventive actions and vector control in peak months is crucial to reducing the impact of DHF in North Sumatra.]]>
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