The COVID-19 pandemic underscored the need and value of a standardised and timely surveillance system for severe acute respiratory infections (SARI) to inform epidemic preparedness and response.

We aimed to develop an automated SARI surveillance system using electronic health records retrieved from pre-existing national health registers in Denmark.

We used the Danish Civil Register, the Danish National Patient Register and the Danish Microbiology Database to set up the system. First, we determined a SARI case definition for surveillance, choosing among six different potentially usable combinations of ICD-10 diagnosis codes by exploring how each combination captured patient characteristics (age, hospital admission length, mortality, laboratory tests and seasonality). Second, using this case definition, we evaluated the surveillance system’s timeliness and completeness by comparing weekly data reported with a delay of 1, 8, 15, 22 and 29 days, respectively, against a complete set of data extracted after 120 days.

The selected case definition combined ICD-10 codes for influenza (J09-J11), acute lower viral and bacterial respiratory tract infections and bronchiolitis (J12-J22) and COVID-19 (B342A and B972A). With regards to timeliness and completeness of this definition, weekly data reported with a delay of 8 days was 89–93% complete and showed very similar patterns in weekly changes in SARI cases as data reported after 120 days.

Our SARI surveillance system detected fluctuations in weekly SARI cases in a consistent and timely manner. We recommend countries to explore using electronic health registers as a resource-efficient alternative to standard SARI sentinel surveillance.

Since March 2024, cases of highly pathogenic avian influenza (HPAI) caused by A(H5N1) virus of clade 2.3.4.4b have been reported in dairy cattle in the United States, followed by spillover to avian and other mammalian species including humans. Although human-to-human transmission has not been reported, the virus's ability to infect mammals and potential of adaptation raise public health concerns, necessitating enhanced monitoring and preparedness.

We aimed to develop digital RT-PCR assays to detect and quantify influenza A(H5N1) 2.3.4.4b viruses in biological and environmental samples.

We developed two digital RT-PCR assays targeting the matrix protein (JRC-MP) and haemagglutinin (JRC-HA) genes of A(H5N1) 2.3.4.4b viruses. After in silico assessment of inclusivity and exclusivity, we evaluated the assays’ performance using RNAs from influenza A(H5N1) viruses isolated from infected animal specimens, in an inter-laboratory exercise with diverse target and non-target isolates, and on wastewater samples either negative or spiked with A(H5N1) 2.3.4.4b RNA.

The JRC-MP assay detects influenza A viruses of different subtypes and origins, while the JRC-HA assay specifically detects HPAI A(H5Nx) 2.3.4.4b strains. The assays demonstrated high sensitivity, showing consistent results in the inter-laboratory exercise. They also detected target RNAs in wastewater samples with high accuracy, despite background components, supporting potential use in wastewater surveillance programmes.

Aligned with One Health strategies for zoonotic avian influenza surveillance, we propose the combined use of these two assays for the rapid and sensitive detection of influenza A(H5Nx) 2.3.4.4b in biological and environmental samples to enhance monitoring and outbreak control measures.