Universitat Politècncia de Catalunya - Barcelona Tech, Barcelona, Catalunya 08034, Spain E-mail:
Universitat Politècncia de Catalunya - Barcelona Tech, Barcelona, Catalunya 08034, Spain.
J Water Health. 2024 Mar;22(3):584-600. doi: 10.2166/wh.2024.345. Epub 2024 Feb 9.
Monitoring SARS-CoV-2 spread is challenging due to asymptomatic infections, numerous variants, and population behavior changes from non-pharmaceutical interventions. We developed a Digital Twin model to simulate SARS-CoV-2 evolution in Catalonia. Continuous validation ensures our model's accuracy. Our system uses Catalonia Health Service data to quantify cases, hospitalizations, and healthcare impact. These data may be under-reported due to screening policy changes. To improve our model's reliability, we incorporate data from the Catalan Surveillance Network of SARS-CoV-2 in Sewage (SARSAIGUA). This paper shows how we use sewage data in the Digital Twin validation process to identify discrepancies between model predictions and real-time data. This continuous validation approach enables us to generate long-term forecasts, gain insights into SARS-CoV-2 spread, reassess assumptions, and enhance our understanding of the pandemic's behavior in Catalonia.
由于无症状感染、大量变异和非药物干预引起的人群行为变化，监测 SARS-CoV-2 的传播具有挑战性。我们开发了一种数字孪生模型来模拟加泰罗尼亚的 SARS-CoV-2 进化。连续验证确保了我们模型的准确性。我们的系统使用加泰罗尼亚卫生局的数据来量化病例、住院和医疗保健的影响。由于筛查政策的变化，这些数据可能报告不足。为了提高我们模型的可靠性，我们将来自加泰罗尼亚 SARS-CoV-2 污水监测网络（SARSAIGUA）的数据纳入其中。本文展示了我们如何在数字孪生验证过程中使用污水数据来识别模型预测与实时数据之间的差异。这种连续验证方法使我们能够生成长期预测，深入了解 SARS-CoV-2 的传播，重新评估假设，并增强我们对加泰罗尼亚大流行行为的理解。
