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、大量变异以及非药物干预措施导致的人群行为变化，监测 SARS-CoV-2 传播具有挑战性。我们开发了数字孪生模型来模拟加泰罗尼亚的 SARS-CoV-2 进化。持续验证确保我们模型的准确性。我们的系统使用加泰罗尼亚卫生服务数据来量化病例、住院情况和医疗保健影响。由于筛查政策的变化，这些数据可能被低估。为了提高模型的可靠性，我们纳入了来自加泰罗尼亚污水中 SARS-CoV-2 监测网络 (SARSAIGUA) 的数据。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。这种持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了解。