Rapid feedback on hospital onset SARS-CoV-2 infections combining epidemiological and sequencing data

Abstract

BackgroundRapid identification and investigation of healthcare-associated infections (HCAIs) is important for suppression of SARS-CoV-2, but the infection source for hospital onset COVID-19 infections (HOCIs) cannot always be readily identified based only on epidemiological data. Viral sequencing data provides additional information regarding potential transmission clusters, but the low mutation rate of SARS-CoV-2 can make interpretation using standard phylogenetic methods difficult.MethodsWe developed a novel statistical method and sequence reporting tool (SRT) that combines epidemiological and sequence data in order to provide a rapid assessment of the probability of HCAI among HOCI cases (defined as first positive test >48 hours following admission) and to identify infections that could plausibly constitute outbreak events. The method is designed for prospective use, but was validated using retrospective datasets from hospitals in Glasgow and Sheffield collected February-May 2020.ResultsWe analysed data from 326 HOCIs. Among HOCIs with time-from-admission ≥8 days the SRT algorithm identified close sequence matches from the same ward for 160/244 (65.6%) and in the remainder 68/84 (81.0%) had at least one similar sequence elsewhere in the hospital, resulting in high estimated probabilities of within-ward and within-hospital transmission. For HOCIs with time-from-admission 3-7 days, the SRT probability of healthcare acquisition was >0.5 in 33/82 (40.2%).ConclusionsThe methodology developed can provide rapid feedback on HOCIs that could be useful for infection prevention and control teams, and warrants further prospective evaluation. The integration of epidemiological and sequence data is important given the low mutation rate of SARS-CoV-2 and its variable incubation period.

Enhancing epidemiological investigation of nosocomial SARS-CoV-2 infection with whole genome sequencing: A retrospective cohort study across four hospitals in the UK

“…Individuals infected with identical or near-identical (≤1 SNP) viruses, are more likely to be linked in a transmission chain than those with more distantly related viruses, as demonstrated by previous retrospective studies that have utilised WGS to identify nosocomial infections and outbreaks. [7][8][9][10][11][12] We investigated whether sequencing could enhance epidemiological investigation of healthcare-associated SARS-CoV-2 acquisition in two areas: i) confirming/excluding nosocomial acquisition and ii) understanding the role of outbreaks in nosocomial acquisition. We highlight the benefits and pitfalls of this approach, to help guide local practice in individual centres.…”

Genetic epidemiology of SARS-CoV-2 transmission in renal dialysis units – A high risk community-hospital interface

“…We applied a novel algorithm, the Sequence Reporting Tool (SRT) to estimate the probability of healthcare- vs community-acquired infection in each case, based on the statistical approach developed for the COG-UK hospital-onset COVID-19 infection (HOCI) study (https://clinicaltrials.gov/ct2/show/NCT04405934). 22 The approach is based on Bayesian principles and involves comparison of the proportion of similar viral sequences (with maximum pairwise SNP difference of two, with no difference where there is an overlap in ambiguous nucleotide codes or an ‘N’ in either sequence) observed within potential locations of infection for the case of interest: i) patients’ RDU and elsewhere in this hospital, ii) inpatient ward and hospital (if the patient was admitted) all within the prior three weeks; along with a weighted proportion of similar sequences in the local community of the patient within the prior six weeks based on the outer postcode of their home address. There are 61 districts based on this outcode (49 in Glasgow and 12 in Lanarkshire).…”

“…The SRT algorithm was coded in R version 3.6.0, using the ape v5.3 package for calculation of pairwise SNP differences and PostcodesioR v0.1.1 and gmt v2.0-1 packages to calculate distances between postcodes. 22 …”

“…We applied a novel algorithm, the Sequence Reporting Tool (SRT) to estimate the probability of healthcare-vs communityacquired infection in each case, based on the statistical approach developed for the COG-UK hospital-onset COVID-19 infection (HOCI) study (https://clinicaltrials.gov/ct2/show/NCT04405934). 22 The approach is based on Bayesian principles and involves comparison of the proportion of similar viral sequences (with maximum pairwise SNP difference of two, with no difference where there is an overlap in ambiguous nucleotide codes or an 'N' in either sequence) observed within potential locations of infection for i MagNA Pure 96 system (Roche, Penzberg, Germany), Abbott M20 0 0 (Abbott, Chicago, US) or Cobas® 6800 Systems (Roche). SARS-CoV-2 was detected using one of three RT-PCR assays: RdRp gene/E gene.…”

Protocol for the COG-UK hospital onset COVID-19 infection (HOCI) multicentre interventional clinical study: evaluating the efficacy of rapid genome sequencing of SARS-CoV-2 in limiting the spread of COVID-19 in United Kingdom NHS hospitals

“…Using an overdispersion parameter of 0.82 based on retrospective analysis of data from Sheffield and Glasgow (dataset as described by Stirrup et al) results in 81% power to detect a reduction in mean weekly incidence from 12.5 to 10. 6…”