Development and delivery of a real-time hospital-onset COVID-19 surveillance system using network analysis

Price, James Richard; Mookerjee, Siddharth; Dyakova, Eleonora; Myall, Ashleigh; Leung, Wendy; Weiße, Andrea Yeong; Shersing, Yeeshika; Brannigan, Eimear Therese; Galletly, Tracey; Muir, David; Randell, Paul; Davies, Frances; Bolt, Frances; Barahona, Mauricio; Otter, Jonathan Ashley; Holmes, Alison H

BACKGROUND: Understanding nosocomial acquisition, outbreaks and transmission chains in real-time will be fundamental to ensuring infection prevention measures are effective in controlling COVID-19 in healthcare. We report the design and implementation of a hospital-onset COVID-19 infection (HOCI) surveillance system for an acute healthcare setting to target prevention interventions. METHODS: The study took place in a large teaching hospital group in London, UK. All patients tested for SARS-CoV-2 between 4(th) March and 14(th) April 2020 were included. Utilising data routinely collected through electronic healthcare systems we developed a novel surveillance system for determining and reporting HOCI incidence and providing real-time network analysis. We provided daily reports on incidence and trends over time to support HOCI investigation, and generated geo-temporal reports using network analysis to interrogate admission pathways for common epidemiological links to infer transmission chains. By working with stakeholders the reports were co-designed for end users. RESULTS: Real-time surveillance reports revealed: changing rates of HOCI throughout the course of the COVID-19 epidemic; key wards fuelling probable transmission events; HOCIs over-represented in particular specialities managing high-risk patients; the importance of integrating analysis of individual prior pathways; and the value of co-design in producing data visualisation. Our surveillance system can effectively support national surveillance. CONCLUSIONS: Through early analysis of the novel surveillance system we have provided a description of HOCI rates and trends over time using real-time shifting denominator data. We demonstrate the importance of including the analysis of patient pathways and networks in characterising risk of transmission and targeting infection control interventions.