Back

Syndromic surveillance for severe acute respiratory infections (SARI) enables valid estimation of COVID-19 hospitalization incidence and reveals underreporting of hospitalizations during pandemic peaks of three COVID-19 waves in Germany, 2020-2021

Tolksdorf, K.; Haas, W.; Schuler, E.; Wieler, L. H.; Schilling, J.; Hamouda, O.; Diercke, M.; Buda, S.

2022-02-13 epidemiology
10.1101/2022.02.11.22269594 medRxiv
Show abstract

ObjectiveThe emergence of coronavirus disease 2019 (COVID-19) required countries to establish COVID-19 surveillance by adapting existing systems, such as mandatory notification and syndromic surveillance systems. We estimated age-specific COVID-19 hospitalization and intensive care unit (ICU) burden from existing severe acute respiratory infections (SARI) surveillance and compared the results to COVID-19 notification data. MethodsUsing data on SARI cases with ICD-10 diagnosis codes for COVID-19 (COVID-SARI) from the ICD-10 based SARI sentinel, we estimated age-specific incidences for COVID-SARI hospitalization and ICU for the first five COVID-19 waves in Germany and compared these to incidences from notification data on COVID-19 cases using relative change {Delta}r at the peak of each wave. FindingsThe COVID-SARI incidence from sentinel data matched the notified COVID-19 hospitalization incidence in the first wave with {Delta}r=6% but was higher during second to fourth wave ({Delta}r =20% to 39%). In the fifth wave, the COVID-SARI incidence was lower than the notified COVID-19 hospitalization incidence ({Delta}r =-39%). For all waves and all age groups, the ICU incidence estimated from COVID-SARI was more than twice the estimation from notification data. ConclusionThe use of validated SARI sentinel data adds robust and important information for assessing the true disease burden of severe COVID-19. Mandatory notifications of COVID-19 for hospital and ICU admission may underestimate (work overload in local health authorities) or overestimate (hospital admission for other reasons than the laboratory-confirmed SARS-CoV-2 infection) disease burden. Syndromic ICD-10 based SARI surveillance enables sustainable cross-pathogen surveillance for seasonal epidemics and pandemic preparedness of respiratory viral diseases.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.