A survey of patient and public perceptions and awareness of SARS-CoV-2-related risks among participants in India and South Africa (preprint)

A cross-sectional survey was performed among the adult population of participating countries, India and South Africa. The purpose of this study was to explore perceptions and awareness of SARS-CoV-2-related risks in the relevant countries. The main outcome measures were the proportion of participants aware of SARS-CoV-2, and their perception of infection risks. Self-administered questionnaires were used to collect data via a web- and paper-based survey over three months. For data capturing, Microsoft Excel was employed, and descriptive statistics used for presenting data. Pearsons Chi-squared test was used to assess relationships between variables, and a p-value less than 0.05 was considered significant. There were 844 respondents (India: n=660, South Africa: n=184; response rate 87.6%), with a 61.1% vs 38.3% female to male ratio. Post-high-school or university education was the lowest qualification reported by most respondents in India (77.3%) and South Africa (79.3%). Sources of information about the pandemic were usually media and journal publications (73.2%), social media (64.6%), family and friends (47.7%) and government websites (46.2%). Most respondents correctly identified infection prevention measures (such as physical distancing, mask use), with 90.0% reporting improved hand hygiene practices since the pandemic. Hesitancy or refusal to accept the SARS-CoV-2 vaccine was reported among 17.9% and 50.9% of respondents in India and South Africa, respectively. Reasons cited included rushed vaccine development and the futility of vaccines for what respondents considered a self-limiting flu-like illness. Respondents identified public health promotion measures for SARS-CoV-2. Reported hesitancy to the up-take of SARS-CoV-2 vaccines was much higher in South Africa. Vaccination campaigns should consider robust public engagement and contextually fit communication strategies with multimodal, participatory online and offline initiatives to address public concerns, specifically towards vaccines developed for this pandemic and general vaccine hesitancy.

Smart-Plexer: a breakthrough workflow for hybrid development of multiplex PCR assays (preprint)

Developing multiplex PCR assays requires an extensive amount of experimental testing, the number of which exponentially increases by the number of multiplexed targets. Dedicated efforts must be devoted to the design of optimal multiplex assays for specific and sensitive identification of multiple analytes in a single well reaction. Inspired by data-driven approaches, we reinvent the way of designing and developing multiplex assays by proposing a hybrid, easy-to-use workflow, named Smart-Plexer, which couples empirical testing of singleplex assays and computer simulation of multiplexing. The Smart-Plexer leverages kinetic inter-target distances among amplification curves to generate optimal multiplex PCR primer sets for accurate multi-pathogen identification. The optimal single-channel assays, together with a novel data-driven approach, Amplification Curve Analysis (ACA), were demonstrated to be capable of classifying the presence of desired targets in a single test for seven common respiratory infection pathogens.

How acceptable is rapid whole genome sequencing for infectious disease management in hospitals? Perspectives of those involved in managing nosocomial SARS-CoV-2 (preprint)

Structured summary Background Whole genome sequencing (WGS) for managing healthcare associated infections (HCAIs) has developed considerably through experiences with SARS-CoV-2. We interviewed various healthcare professionals (HCPs) with direct experience of using WGS in hospitals (within the COG-UK Hospital Onset COVID-19 Infection (HOCI) study) to explore its acceptability and future use. Method An exploratory, cross-sectional, qualitative design employed semi-structured interviews with 39 diverse HCPs between December 2020 and June 2021. Participants were recruited from five sites within the larger clinical study of a novel genome sequencing reporting tool for SARS-CoV-2 (the HOCI study). All had experience, in their diverse roles, of using sequencing data to manage nosocomial SARS-CoV-2 infection. Deductive and inductive thematic analysis identified themes exploring aspects of the acceptability of sequencing. Findings The analysis highlighted the overall acceptability of rapid WGS for infectious disease using SARS-CoV-2 as a case study. Diverse professionals were largely very positive about its future use and believed that it could become a valuable and routine tool for managing HCAIs. We identified three key themes ‘1) ‘Proof of concept achieved’; 2) ‘Novel insights and implications’; and 3) ‘Challenges and demands’. Conclusion Our qualitative analysis, drawn from five diverse hospitals, shows the broad acceptability of rapid sequencing and its potential. Participants believed it could and should become an everyday technology capable of being embedded within typical hospital processes and systems. However, its future integration into existing healthcare systems will not be without challenges (e.g., resource, multi-level change) warranting further mixed methods research.

An adaptive filtering framework for non-specific and inefficient reactions in multiplex digital PCR based on sigmoidal trends (preprint)

ABSTRACT Real-time digital PCR (qdPCR) coupled with artificial intelligence has shown the potential of unlocking scientific breakthroughs, particularly in the field of molecular diagnostics for infectious diseases. One of the most promising applications is the use of machine learning (ML) methods to enable single fluorescent channel PCR multiplex by extracting target-specific kinetic and thermodynamic information contained in amplification curves. However, the robustness of such methods can be affected by the presence of undesired amplification events and nonideal reaction conditions. Therefore, here we proposed a novel framework to filter non-specific and low efficient reactions from qdPCR data using outlier detection algorithms purely based on sigmoidal trends of amplification curves. As a proof-of-concept, this framework is implemented to improve the classification performance of the recently reported ML-based Amplification Curve Analysis (ACA), using available data from a previous publication where the ACA method was used to screen carbapenemase-producing organisms in clinical isolates. Furthermore, we developed a novel strategy, named Adaptive Mapping Filter (AMF), to consider the variability of positive counts in digital PCR. Over 152,000 amplification events were analyzed. For the positive reactions, filtered and unfiltered amplification curves were evaluated by comparing against melting peak distribution, proving that abnormalities (filtered out data) are linked to shifted melting distribution or decreased PCR efficiency. The ACA was applied to compare classification accuracies before and after AMF, showing an improved sensitivity of 1.18% for inliers and 20% for outliers (p-value < 0.0001). This work explores the correlation between kinetics of amplification curves and thermodynamics of melting curves and it demonstrates that filtering out non-specific or low efficient reactions can significantly improve the classification accuracy for cutting edge multiplexing methodologies.

Evaluating the effectiveness of rapid SARS-CoV-2 genome sequencing in supporting infection control teams: the COG-UK hospital-onset COVID-19 infection study (preprint)

Introduction Viral sequencing of SARS-CoV-2 has been used for outbreak investigation, but there is limited evidence supporting routine use for infection prevention and control (IPC) within hospital settings. Methods We conducted a prospective non-randomised trial of sequencing at 14 acute UK hospital trusts. Sites each had a 4-week baseline data-collection period, followed by intervention periods comprising 8 weeks of 'rapid' (<48h) and 4 weeks of 'longer-turnaround' (5-10 day) sequencing using a sequence reporting tool (SRT). Data were collected on all hospital onset COVID-19 infections (HOCIs; detected [≥]48h from admission). The impact of the sequencing intervention on IPC knowledge and actions, and on incidence of probable/definite hospital-acquired infections (HAIs) was evaluated. Results A total of 2170 HOCI cases were recorded from October 2020-April 2021, with sequence reports returned for 650/1320 (49.2%) during intervention phases. We did not detect a statistically significant change in weekly incidence of HAIs in longer-turnaround (IRR 1.60, 95%CI 0.85-3.01; P=0.14) or rapid (0.85, 0.48-1.50; P=0.54) intervention phases compared to baseline phase. However, IPC practice was changed in 7.8% and 7.4% of all HOCI cases in rapid and longer-turnaround phases, respectively, and 17.2% and 11.6% of cases where the report was returned. In a per-protocol sensitivity analysis there was an impact on IPC actions in 20.7% of HOCI cases when the SRT report was returned within 5 days. Conclusion While we did not demonstrate a direct impact of sequencing on the incidence of nosocomial transmission, our results suggest that sequencing can inform IPC response to HOCIs, particularly when returned within 5 days.

Impact of the COVID-19 pandemic on community antibiotic prescribing and stewardship: a qualitative interview study with general practitioners in England (preprint)

The COVID-19 pandemic has had a profound impact on the delivery of primary care services. We aimed to identify general practitioners (GPs) perceptions and experiences of how the COVID-19 pandemic influenced antibiotic prescribing and antimicrobial stewardship (AMS) in general practice in England. Twenty-four semi-structured interviews were conducted with 18 GPs at two time-points: autumn 2020 (14 interviews) and spring 2021 (10 interviews). Interviews were audio-recorded, transcribed and analysed thematically, taking a longitudinal approach. Participants reported a lower threshold for antibiotic prescribing (and fewer consultations) for respiratory infections and COVID-19 symptoms early in the pandemic, then returning to more usual (pre-pandemic) prescribing. They perceived less impact on antibiotic prescribing for urinary and skin infections. Participants perceived the changing ways of working and consulting (e.g., proportions of remote and in-person consultations), and the changing patient presentations and GP workload as influencing the fluctuations in antibiotic prescribing. This was compounded by decreased engagement with, and priority of, AMS due to COVID-19-related urgent priorities. Re-engagement with AMS is needed, e.g., through reviving antibiotic prescribing feedback and targets/incentives. While the pandemic disrupted the usual ways of working, it also produced opportunities, e.g., for re-organising ways of managing infections and AMS in the future.

Predicting hospital-onset COVID-19 infections using dynamic networks of patient contacts: an observational study (preprint)

BackgroundReal-time prediction is key to prevention and control of healthcare-associated infections. Contacts between individuals drive infections, yet most prediction frameworks fail to capture the dynamics of contact. We develop a real-time machine learning framework that incorporates dynamic patient contact networks to predict patient-level hospital-onset COVID-19 infections (HOCIs), which we test and validate on international multi-site datasets spanning epidemic and endemic periods. MethodsOur framework extracts dynamic contact networks from routinely collected hospital data and combines them with patient clinical attributes and background contextual hospital data to forecast the infection status of individual patients. We train and test the HOCI prediction framework using 51,157 hospital patients admitted to a UK (London) National Health Service (NHS) Trust from 01 April 2020 to 01 April 2021, spanning UK COVID-19 surges 1 and 2. We then validate the framework by applying it to data from a non-UK (Geneva) hospital site during an epidemic surge (40,057 total inpatients) and to data from the same London Trust from a subsequent period post surge 2, when COVID-19 had become endemic (43,375 total inpatients). FindingsBased on the training data (London data spanning surges 1 and 2), the framework achieved high predictive performance using all variables (AUC-ROC 0{middle dot}89 [0{middle dot}88-0{middle dot}90]) but was almost as predictive using only contact network variables (AUC-ROC 0{middle dot}88 [0{middle dot}86-0{middle dot}90]), and more so than using only hospital contextual (AUC-ROC 0{middle dot}82 [0{middle dot}80-0{middle dot}84]) or patient clinical (AUC-ROC 0{middle dot}64 [0{middle dot}62-0{middle dot}66]) variables. The top three risk factors we identified consisted of one hospital contextual variable (background hospital COVID-19 prevalence) and two contact network variables (network closeness, and number of direct contacts to infectious patients), and together achieved AUC-ROC 0{middle dot}85 [0{middle dot}82-0{middle dot}88]. Furthermore, the addition of contact network variables improved performance relative to hospital contextual variables on both the non-UK (AUC-ROC increased from 0{middle dot}84 [0{middle dot}82-0{middle dot}86] to 0{middle dot}88 [0{middle dot}86-0{middle dot}90]) and the UK validation datasets (AUC-ROC increased from 0{middle dot}52 [0{middle dot}49-0{middle dot}53] to 0{middle dot}68 [0{middle dot}64-0{middle dot}70]). InterpretationOur results suggest that dynamic patient contact networks can be a robust predictor of respiratory viral infections spreading in hospitals. Their integration in clinical care has the potential to enhance individualised infection prevention and early diagnosis. FundingMedical Research Foundation, World Health Organisation, Engineering and Physical Sciences Research Council, National Institute for Health Research, Swiss National Science Foundation, German Research Foundation.

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.

SARS-CoV-2 lineage B.1.1.7 is associated with greater disease severity among hospitalised women but not men (preprint)

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage B.1.1.7 has been associated with an increased rate of transmission and disease severity among subjects testing positive in the community. Its impact on hospitalised patients is less well documented. Methods We collected viral sequences and clinical data of patients admitted with SARS-CoV-2 and hospital-onset COVID-19 infections (HOCIs), sampled 16/11/2020 - 10/01/2021, from eight hospitals participating in the COG-UK-HOCI study. Associations between the variant and the outcomes of all-cause mortality and intensive therapy unit (ITU) admission were evaluated using mixed effects Cox models adjusted by age, sex, comorbidities, care home residence, pregnancy and ethnicity. Results Sequences were obtained from 2341 inpatients (HOCI cases = 786) and analysis of clinical outcomes was carried out in 2147 inpatients with all data available. The hazard ratio (HR) for mortality of B.1.1.7 compared to other lineages was 1.01 (95% CI 0.79-1.28, P=0.94) and for ITU admission was 1.01 (95% CI 0.75-1.37, P=0.96). Analysis of sex-specific effects of B.1.1.7 identified increased risk of mortality (HR 1.30, 95% CI 0.95-1.78) and ITU admission (HR 1.82, 95% CI 1.15-2.90) in females infected with the variant but not males (mortality HR 0.82, 95% CI 0.61-1.10; ITU HR 0.74, 95% CI 0.52-1.04). Conclusions In common with smaller studies of patients hospitalised with SARS-CoV-2 we did not find an overall increase in mortality or ITU admission associated with B.1.1.7 compared to other lineages. However, women with B.1.1.7 may be at an increased risk of admission to intensive care and at modestly increased risk of mortality.

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.

Macro Level Influences on Strategic Responses to the COVID-19 Pandemic - A Tool for National Assessments (preprint)

Background: Variation in the approaches taken to contain the SARS-CoV-2 (COVID-19) pandemic at country level has been shaped by economic and political considerations, technical capacity, and assumptions about public behaviours. To address the limited application of learning from previous pandemics, this study aimed to analyse perceived facilitators and inhibitors during the pandemic and to inform the development of an assessment tool for pandemic response planning.Methods: A cross-sectional electronic survey of health and non-healthcare professionals (5 May - 5 June 2020) in six languages, with respondents recruited via email, social media and website posting. Participants were asked to score inhibitors (-10 to 0) or facilitators (0 to +10) impacting country response to COVID-19 from the following domains – Political, Economic, Sociological, Technological, Ecological, Legislative, and wider Industry (the PESTELI framework). Participants were then asked to explain their responses using free text. Descriptive and thematic analysis was followed by triangulation with the literature and expert validation to develop the assessment tool, which was then compared with four existing pandemic planning frameworks.Findings: 928 respondents from 66 countries (57% healthcare professionals) participated. Political and economic influences were consistently perceived as powerful negative forces and technology as a facilitator across high- and low-income countries. The 103-item tool developed for guiding rapid situational assessment for pandemic planning is comprehensive when compared to existing tools and highlights the interconnectedness of the 7 domains.Interpretation: The tool developed and proposed addresses the problems associated with decision making in disciplinary silos and offers a means to refine future use of epidemic modelling.Funding Statement: This study did not receive any external funding.Declaration of Interests: None to declare. Ethics Approval Statement: The study was approved by the Joint Research Compliance Office, Imperial College London (ICREC reference: 20IC5947).

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.