Nowcasting methods to improve the performance of Respiratory Sentinel Surveillance: Lessons from the COVID-19 Pandemic

Abstract

Respiratory diseases, including influenza and coronaviruses, pose recurrent global threats. This study delves into the respiratory surveillance systems, focusing on the effectiveness of SARI sentinel surveillance for total and severe cases incidence estimation. Leveraging data from the COVID-19 pandemic in Chile, we examined a 159-week period comparing census surveillance results of confirmed cases and hospitalizations, with sentinel surveillance carried out during the same period. Our analyses revealed a consistent underestimation of total cases and an overestimation of severe cases of sentinel surveillance. To address these limitations, we introduce a nowcasting model, improving the precision and accuracy of incidence estimates. Our findings are applicable beyond COVID-19, with implications for respiratory virus surveillance and early detection of respiratory epidemics. The nowcasting model offers real-time insights into an outbreak for public health decision-making, using the same surveillance data that is routinely collected. This approach enhances global preparedness for emerging respiratory diseases by the development of practical solutions with wide-ranging applications in public health.