Trie Adriana Ramadhani, Fathoni Fathoni

Analisis Pola Temporal Penyebaran Penyakit DBD dan HIV Berbasis Time Series Clustering

Introduction

Analisis pola temporal penyebaran penyakit dbd dan hiv berbasis time series clustering. Analisis pola temporal penyebaran DBD dan HIV di Jawa Timur (2018–2024) menggunakan time series clustering. Mengelompokkan daerah berdasarkan dinamika kasus untuk mendukung perencanaan pengendalian penyakit.

Abstract

In Indonesia, including in East Java Province, infectious diseases such as Dengue Fever (DHF) and Human Immunodeficiency Virus (HIV) remain public health concerns. Incidence patterns vary by region and time of year. Variations in temporal patterns among districts and cities may lead to suboptimal identification of priority intervention areas when analyses rely solely on absolute case counts. This study aims to analyze the temporal patterns of DHF and HIV case distribution in East Java Province during the 2018–2024 period in order to cluster regions based on similarities in case dynamics over time.The analysis was conducted using a time series clustering approach to group districts and cities according to the similarity of their case development patterns. Temporal similarity was measured using the Dynamic Time Warping method and subsequently clustered using Hierarchical Clustering. Prior to analysis, the data were normalized using the Z-score method to minimize the influence of differences in case scale among regions. The results show that the temporal patterns of DHF and HIV cases were each classified into three main clusters. Cluster quality evaluation using the Silhouette index yielded a value of 0.408 for DHF, indicating a relatively clear cluster structure, whereas a value of 0.197 was obtained for HIV, suggesting a weaker cluster structure due to the complexity and heterogeneity of regional-level case data. Nevertheless, the resulting clusters still provide preliminary information on variations in temporal patterns. The identified clusters represent regions with stable, fluctuating, and increasing case patterns. Several urban areas, such as Pasuruan City, Probolinggo City, and Banyuwangi Regency, tend to belong to clusters with relatively high case levels for more than one disease, indicating challenges in disease control within these regions. These findings provide an initial overview of the temporal dynamics of DHF and HIV cases in East Java, which may serve as supporting evidence for region- and time-based disease control planning.

Review

This study addresses a pertinent public health issue in Indonesia, specifically the temporal dynamics of Dengue Hemorrhagic Fever (DHF) and Human Immunodeficiency Virus (HIV) in East Java. By moving beyond simple absolute case counts, the authors aim to identify nuanced temporal patterns of disease spread, which is crucial for optimizing intervention strategies. The focus on clustering regions based on similarities in case dynamics over a 6-year period (2018-2024) is a commendable approach to provide a more granular understanding of disease distribution and to identify priority areas for public health action, aligning well with the need for targeted, data-driven disease control. Methodologically, the study employs a sound time series clustering approach. The use of Dynamic Time Warping (DTW) is appropriate for measuring similarity between time series, as it can handle variations in timing and duration that traditional Euclidean distance might miss. This is coupled with Hierarchical Clustering for grouping, and Z-score normalization to mitigate scale differences, ensuring a robust analysis of temporal patterns across regions. This combination of techniques is well-suited for the study's objective of categorizing districts and cities based on their disease development trajectories over time, offering a sophisticated analytical framework to address the research question. The findings indicate that both DHF and HIV cases can be classified into three main temporal clusters, broadly characterized as stable, fluctuating, and increasing patterns. While the Silhouette index for DHF (0.408) suggests a relatively clear cluster structure, the lower value for HIV (0.197) highlights the inherent complexity and heterogeneity in HIV case data, a valuable insight in itself. The identification of specific urban areas, such as Pasuruan City, Probolinggo City, and Banyuwangi Regency, consistently appearing in high-case-level clusters for multiple diseases, offers actionable preliminary information for local health authorities. Overall, the study provides a foundational understanding of temporal dynamics that can inform more precise and effective region- and time-based disease control planning in East Java.

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