Monitoring the spread of SARS-CoV-2 presents significant challenges due to the asymptomatic nature of the infections, the presence of numerous variants, and changes in population behavior resulting from the implementation of non-pharmaceutical interventions aimed at reducing transmission. To accurately estimate the true rate of infection, we have developed a Digital Twin model that simulates the evolution of SARS-CoV-2 infections in Catalonia. Continuous validation plays a crucial role in ensuring the accuracy of our model's predictions. Our system utilizes data from the Catalonia Health Service to quantify detected cases, hospitalizations, and the overall impact on the healthcare system. However, these data are susceptible to under-reporting due to changes in screening policies. To enhance the reliability of our model's forecasts, we incorporate data from the Catalan Surveillance Network of SARS-CoV-2 in Sewage (SARSAIGUA). In this paper, we show how we utilize sewage data in the validation process of the Digital Twin to identify any discrepancies between the model's predictions and real-time data. We outline how this continuous validation approach allows us to generate long-term forecasts and gain insights into the current situation regarding the spread of SARS-CoV-2, enabling us to reassess assumptions and enhance our understanding of the pandemic's behavior in Catalonia.
The paper presents a Digital Twin model to analyze the evolution of SARS-CoV-2.
It shows how sewage data are used to validate the model's predictions.
It outlines how the continuous validation allows long-term forecasts.
The model can account for the asymptomatic nature of the infections, variants, and changes in population behavior due to NPIs.
The method enhances the reliability and accuracy of the model's forecasts.