Changing patterns of bloodstream infections in the community and in acute care across two COVID-19 epidemic waves: a retrospective analysis (preprint)

Introduction We examined the epidemiology of community- and hospital-acquired bloodstream infections (BSIs) in COVID-19 and non-COVID-19 patients across two epidemic waves. Methods We analysed blood cultures, SARS-CoV-2 tests, and hospital episodes of patients presenting and admitted to a London hospital group between January 2020 and February 2021. We reported BSI incidence, as well as changes in sampling, case mix, bed and staff capacity, and COVID-19 variants. Results 34,044 blood cultures were taken. We identified 1,047 BSIs; 653 (62.4%) defined epidemiologically as community-acquired and 394 (37.6%) as hospital-acquired. BSI rates and community / hospital ratio were similar to those pre-pandemic. However, important changes in patterns were seen. Among community-acquired BSIs, Escherichia coli BSIs remained lower than pre-pandemic level during the two COVID-19 waves, however peaked following lockdown easing in May 2020, deviating from the historical trend of peaking in August. The hospital-acquired BSI rate was 100.4 per 100,000 patient-days across the pandemic, increasing to 132.3 during the first COVID-19 wave and 190.9 during the second, with significant increase seen in elective non-COVID-19 inpatients. Patients who developed a hospital-acquired BSI, including those without COVID-19, experienced 20.2 excess days of hospital stay and 26.7% higher mortality, higher than reported in pre-pandemic literature. In intensive care units (ICUs), the overall BSI rate was 311.8 per 100,000 patient-ICU days, increasing to 421.0 during the second wave, compared to 101.3 pre-COVID. The BSI incidence in those infected with the SARS-CoV-2 Alpha variant was similar to that seen with earlier variants. Conclusion The pandemic and national responses have had an impact on patterns of community- and hospital-acquired BSIs, in both COVID-19 and non-COVID-19 patients. Factors driving the observed BSI patterns are complex, including changed patient mix, deferred access to health care, and sub-optimal practice. Infection surveillance needs to consider key aspects of pandemic response and changes in healthcare access and practice.

Characterising contact in disease outbreaks via a network model of spatial-temporal proximity (preprint)

Contact tracing is a key tool in epidemiology to identify and control outbreaks of infectious diseases. Existing contact tracing methodologies produce contact maps of individuals based on a binary definition of contact which can be hampered by missing data and indirect contacts. Here, we present our Spatial-temporal Epidemiological Proximity (StEP) model to recover contact maps in disease outbreaks based on movement data. The StEP model accounts for imperfect data by considering probabilistic contacts between individuals based on spatial-temporal proximity of their movement trajectories, creating a robust movement network despite possible missing data and unseen transmission routes. We showcase the potential of StEP for contact tracing with outbreaks of multidrug-resistant bacteria and COVID-19 in a large hospital group in London, UK. In addition to the core structure of contacts that can be recovered using traditional methods of contact tracing, the StEP model reveals missing contacts that connect seemingly separate outbreaks. Comparison with genomic data further confirmed that these additional contacts indeed improve characterisation of disease transmission and so highlights how the StEP framework can inform effective strategies of infection control and prevention.

A handheld point-of-care system for rapid detection of SARS-CoV-2 in under 20 minutes (preprint)

The COVID-19 pandemic is a global health emergency characterized by the high rate of transmission and ongoing increase of cases globally. Rapid point-of-care (PoC) diagnostics to detect the causative virus, SARS-CoV-2, are urgently needed to identify and isolate patients, contain its spread and guide clinical management. In this work, we report the development of a rapid PoC diagnostic test (< 20 min) based on reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) and semiconductor technology for the detection of SARS-CoV-2 from extracted RNA samples. The developed LAMP assay was tested on a real-time benchtop instrument (RT-qLAMP) showing a lower limit of detection of 10 RNA copies per reaction. It was validated against 183 clinical samples including 127 positive samples (screened by the CDC RT-qPCR assay). Results showed 90.55% sensitivity and 100% specificity when compared to RT-qPCR and average positive detection times of 15.45 {+/-} 4.43 min. For validating the incorporation of the RT-LAMP assay onto our PoC platform (RT-eLAMP), a subset of samples was tested (n=40), showing average detection times of 12.89 {+/-} 2.59 min for positive samples (n=34), demonstrating a comparable performance to a benchtop commercial instrument. Paired with a smartphone for results visualization and geo-localization, this portable diagnostic platform with secure cloud connectivity will enable real-time case identification and epidemiological surveillance.