<![CDATA[Latar belakang: Infeksi Saluran Pernapasan Akut (ISPA) tetap menjadi tantangan morbiditas utama di kawasan urban padat seperti DKI Jakarta, yang diperburuk oleh polusi udara. Studi epidemiologi digital ini bertujuan untuk mengevaluasi kelayakan data Google Trends dari berbagai domain kueri sebagai instrumen peringatan dini (early warning system) komplementer untuk memprediksi insiden ISPA, sekaligus menganalisis kekuatan korelasi dan jeda waktu (temporal lag) antara perilaku pencarian informasi spesifik dengan insiden yang terjadi.Metode: Penelitian observasional retrospektif ini membandingkan data mingguan rawat jalan ISPA di Jakarta 2023–2024 dengan Volume Pencarian Relatif (RSV) Google Trends. Kueri diklasifikasikan ke dalam lima domain, di antaranya adalah penyakit, gejala, lingkungan, perawatan dan gaya hidup. Analisis dilakukan menggunakan regresi linier sederhana dan uji korelasi Spearman dengan jeda waktu (time-lag) -3 hingga +3 minggu.Hasil: Kueri spesifik gejala (Batuk) dan pengobatan (Obat Batuk) terbukti bersifat reaktif, memuncak satu minggu setelah lonjakan kasus aktual (Lag +1). Sebaliknya, kueri lingkungan (Air Quality Index, Lag -3) dan proteksi (Air Purifier, Lag -2) bersifat prediktif. Anomali ditemukan pada kueri (PM2.5, Lag 0) dan (Kabut Asap, Lag +3) yang secara signifikan terdistorsi oleh keriuhan media (media clamor) berskala nasional.Simpulan: Pemantauan kueri internet pada domain lingkungan dan proteksi efektif berfungsi sebagai indikator peringatan dini ISPA hingga 2-3 minggu sebelum kasus klinis terjadi. Kendati demikian, data digital rentan terhadap bias media, sehingga integrasinya dengan data klinis konvensional tetap harus dilakukan.ABSTRACT Title: Digital Epidemiology of Information Seeking, Diseases, Symptoms, Environment, Treatment, and Lifestyle as Early Warning Indicators for Acute Respiratory InfectionsBackground: Acute Respiratory Infection (ARI) remains a major morbidity challenge in densely populated urban areas like Jakarta, exacerbated by air pollution. This digital epidemiology study aims to evaluate the feasibility of Google Trends data across various query domains as a complementary early warning system to predict ARI incidence.Method: This retrospective observational time-series study compared weekly outpatient ARI data in Jakarta (2023–2024) with Google Trends Relative Search Volume (RSV). Queries were classified into five domains: disease, symptoms, causes, treatment, and protection. Data were analyzed using simple linear regression and Spearman’s rank correlation with a time-lag of -3 to +3 weeks. Result: Symptom-specific queries (e.g., Batuk) and treatments (Obat Batuk) were reactive, peaking one week after the actual surge in cases (Lag +1). Conversely, environmental (Air Quality Index, Lag -3) and protection queries (Air Purifier, Lag -2) were predictive. Anomalies were found in (PM2.5, Lag 0) and (Kabut Asap, Lag +3) queries, which were significantly distorted by national-scale media clamor.Conclusion: Monitoring internet queries in the environmental and protection domains effectively serves as an early warning indicator for ARI up to 2-3 weeks prior to clinical manifestations. However, digital data are susceptible to media bias, making their integration with conventional clinical data essential.]]>
