Journal of Environmental Engineering· 2026Q2
Atık Su Gözetimi, Solunum Yolu Hastalıklarını Salgın Sonrası Dönemde Tahmin Ediyor
Is Wastewater Surveillance Predictive during the Endemic Phase of Respiratory Disease? An Analysis Based on Clinical, Wastewater, and Digital Search Data in Detroit, Michigan
- 0atıf
- Q2SCImago
- 2026yıl
Kısa özet
Atık su gözetimi, klinik vakalar ortaya çıkmadan 14 güne kadar erken uyarılar sağlayarak COVID-19, grip ve RSV gibi endemik solunum yolu hastalıklarını tahmin edebilmektedir.
Yapay zekâ ile başlık ve abstract'tan üretildi; tam metin okunmaz.
Ana noktalar
- Atık su gözetimi, COVID-19'un pandemi aşamasından endemik faza geçişini başarıyla tespit etmiştir.
- Endemik dönemde RSV, influenza A/B ve SARS-CoV-2 için atık su verileri, klinik, sendromik ve dijital arama verileriyle ilişkilendirilmiştir.
- Zaman gecikmeli çapraz korelasyon, atık su verilerinin bu hastalıklar için klinik vakaları 14 güne kadar daha erken tahmin edebildiğini göstermiştir.
- Atık su verilerine dayanan rastgele orman modelleri, klinik vaka sayılarını doğru bir şekilde tahmin etmiştir.
Yapay zekâ ile başlık ve abstract'tan üretildi; tam metin okunmaz.
Özet (abstract)
Abstract Wastewater-based epidemiology (WBE) has emerged as a major public health innovation catalyzed by the COVID-19 pandemic. However, its potential remains underexplored during and after COVID-19’s transition to endemicity. This study evaluates WBE’s potential to identify the COVID-19 pandemic-to-endemic transition and to provide early warnings across multiple surveillance systems for influenza A (IAV) and B (IBV), respiratory syncytial virus (RSV), and SARS-CoV-2 in the postpandemic era. We monitored SARS-CoV-2 N1 concentrations in wastewater from April 8, 2020, and July 31, 2025, generating the earliest and longest-running wastewater dataset in Detroit, MI. A peak identification method was implemented to identify the pandemic-to-endemic transition by comparing the frequency of N1 concentration peaks between phases. During the endemic phase, from October 1, 2022, and March 31, 2025, we monitored RSV, IAV, IBV, and SARS-CoV-2 in Detroit’s wastewater. Pearson correlations were implemented to quantify the associations between wastewater concentrations and clinical, syndromic, and digital epidemiological data. Time-lagged cross-correlation (TLCC) was used to examine temporal dynamics among these datasets and identify the earliest emerging data for each disease and time lags. Extensive literature studies were conducted to elucidate the time-lag mechanisms for each disease, embracing the TLCC results. Random forest models were established to predict clinical cases based on WBE datasets. This was among the first studies using WBE-based approaches to identify pandemic-to-endemic transition of SARS-CoV-2. The relationships of wastewater data to traditional clinical, syndromic, and digital epidemiological surveillance data were systematically analyzed during the endemic phase of these respiratory diseases. This study demonstrates the predictive value of WBE during the endemic phase, providing early warnings and predictions for seasonal respiratory diseases, including COVID-19, influenza, and flu-like disease.
Yazarların özeti; kaynağından alınmıştır. Journal of Environmental Engineering, 2026 · DOI ↗
Ücretsiz hesapla devam et
Makaleye Sor ile bu makaleye günde 3 soru ücretsiz; makaleyi kaydet, kaynakçasını al, ilgi alanına göre her gün yeni özetler. Çıkarımlar Premium.
Web'de ücretsiz devam etGoogle ya da Apple hesabınla giriş; kart istemez. Bu makaleye geri dönersin.
Telefonda:
Alan: Enfeksiyon Hastalıkları
Infectious DiseasesMedicine