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Integration of sequencing and epidemiological data for surveillance of SARS-CoV-2 infections in a tertiary-care hospital

Abstract Background The ongoing COVID-19 pandemic significantly burdens hospitals and other healthcare facilities. Therefore, understanding the entry and transmission of SARS-CoV-2 is critical for effective prevention and preparedness measures. We performed surveillance and analysis of testing and transmission of SARS-CoV-2 infections in a tertiary-care hospital in Germany during the second and third pandemic waves in fall/winter 2020. Methods Between calendar weeks 41/2020 and 1/2021 40% of all positive patient and staff samples (284 total) were subjected to full-length viral genome sequencing. Clusters were defined based on similar genotypes indicating common sources of infection. We integrated phylogenetic, spatial, and temporal metadata to detect nosocomial infections and outbreaks, uncover transmission chains, and evaluate containment measures’ effectiveness. Results Epidemiologic data and contact tracing readily recognize most healthcare-associated patient infections. However, sequencing data reveal that temporally preceding index cases and transmission routes can be missed using epidemiologic methods, resulting in delayed interventions and serially linked outbreaks being counted as independent events. While hospital-associated transmissions were significantly elevated at a moderate rate of community transmission during the second wave, systematic testing and high vaccination rates among staff have led to a substantial decrease in healthcare-associated infections at the end of the second/beginning of the third wave despite high community transmissions. Conclusions While epidemiologic analysis is critical for immediate containment of healthcare-associated SARS-CoV-2 outbreaks, integration of genomic surveillance revealed weaknesses in identifying staff contacts. Our study underscores the importance of high testing frequency and genomic surveillance to detect, contain and prevent SARS-CoV-2-associated infections in healthcare settings.

We aimed to identify transmission chains and potential vulnerabilities to implement targeted 16 infection control measures by integrating genomic and classical epidemiological data. We 17 launched this surveillance strategy at the start of the second wave. From October 2020 to 18 January 2021, we sequenced 40% of positive cases, staff, and patients. We compare 19 epidemiological and genomic approaches individually with an integrated approach of 20 genomic, temporal, and spatial resolutions to identify healthcare-associated transmissions. In 21 addition, we highlight the benefit of regular staff testing and vaccination on the frequency of 22 healthcare-associated transmissions. 23 24

Study design 26
We conducted a surveillance study of SARS-CoV-2 infections at the UKE during the 2nd 27 COVID-19 wave in Germany (October 2020 to January 2021). The UKE is one of northern 28 A C C E P T E D M A N U S C R I P T Whole genome amplicon sequencing was performed as previously published [11,12]. The assignment of phylogenetically related samples to clusters was based on the presence 2 ≥2 identical samples plus samples with ≤3 mutations to the cluster defining sequence. 3 4

Contact data 5
Reported contacts (RC) were determined by interviewing persons for their close contacts 6 48hrs before testing positive or being symptomatic. Possible contact (PC) on non-COVID 7 wards have been tested positive within a maximum distance of 14 days to each other and 8 resided at the same ward within 30 days prior to being tested positive. For PC between 9 patients and staff on COVID wards patients and staff resided at the same ward within 2-14 10 days prior to staff being tested positive. 11 12 Public data sources 13 7-day incidence rates of SARS-CoV-2 infections and vaccination rates of the population in 14

Cohort description and sampling strategy 25
With the beginning of the second wave of the SARS-CoV-2 pandemic in Germany in fall 26 2020, the numbers of COVID-19 patients and SARS-CoV-2 positively tested staff increased 27 accordingly ( Figure 1A). To rapidly detect and contain hospital-acquired infections, we 28 initiated systematic hospital surveillance by sequencing in week 41 of 2020, with the local 1 rate of community transmission being moderate ( Figure 1A). All available positive patient and 2 staff specimens were included to identify index cases and transmissions. However, with the 3 increasing incidence in the community after week 45 (high rate of community transmission), 4 we collected random samples to monitor containment of prior outbreaks and detect new 5 clusters of infection and cryptic transmission ( Figure 1B). We successfully genotyped in 6 weeks 41 -45, 37% -100% (n=118) and weeks 46 -51, 15% -80% (n=156) of all cases. In 7 addition, based on epidemiologic studies, 10 cases of suspected clusters were sequenced 8 weeks 52-1. these studies, which only allowed a distance of ≤ 1 mutation between samples to define 16 phylogenetic clusters, we included samples with ≤ 3 mutations to the cluster defining 17 sequence to detect possible origins of outbreaks or failed containments. We identified 24 18 clusters with 2 to 43 samples (Figures 2A-B). Some clusters display a high number of 19 samples with identical sequences or low divergence in a short period most likely indicating a 20 common source of infection for each cluster (clusters 1, 3, 4, 5, and 6). Other clusters, e.g., 21 cluster 2, demonstrate higher divergence and spread over a more extended period.  Figure 3A). In general, clustered 1 samples show a significantly higher proportion of HA infections (pHA and dHA) than samples 2 outside of clusters ( Figure 3B). In particular, clusters 1, 4 -8, 13, 16, and 18 contain a high 3 proportion of HA infections ( Figure 3C). Almost all clusters also contain employees, with no 4 significant difference in the proportions of patients and staff in clustered vs. not clustered 5 samples ( Figure 3D). 6 7 Integration of spatial and sequencing data 8 To identify index cases, transmissions, and the role of staff in potential nosocomial infections, 9 we included contact data obtained by interviewing positive tested individuals (reported 10 contacts, RC). Also, we identified possible contacts (PCs) by comparing the physical 11 locations on non-COVID wards where individuals were residing before testing positive. 12 Figure 4A shows the contact network obtained by integrating spatial and genomic 13 information. Contact tracing connects samples located in the same cluster defined by 14 sequence similarity in many cases. However, samples without sequence similarity are also 15 connected by RC and PC, while many samples with sequence similarity or identity are not. 16 17

Epidemiologic evaluations are insufficient in defining clusters 18
To better understand transmissions, we analyzed all clusters identified by sequencing 19 individually. Figure 4B shows a combined representation of all data for cluster 1, the largest 20 cluster including 43 cases, occurring between weeks 41 and 45. 28 of 43 cases share an 21 identical SARS-CoV-2 sequence suggesting a common infection source. Cluster 1, based on 22 contact data, is reported as three separate epidemiologic clusters ( Figure 4B). However, 23 integration of genomics reveals identical cluster-defining sequences in all three clusters and 24 identifies additional cases with no previously known contacts, including the index case (#1) in 25 week 41. All cases except #1 and #43 are classified as nosocomial transmissions according 26 to ECDC criteria. However, transmission from the index to patients and staff at weeks 43 -45 27 remains unclear because no RCs or PCs were reported. A targeted follow-up revealed that 28 cases #3, #11, #17, and #37 were mobile workers moving between wards ( Figure 4B). After 1 week 45, genomic surveillance assigned six additional samples to cluster 1. Patients #38 and 2 #40 at weeks 47-48 are pHA and linked by PCs. In addition, they show identical sequences 3 (+3 mutations) to #42 (staff), suggesting a common source of infection in the healthcare 4 setting. 5 Similar to cluster 1, we find five additional clusters (4, 5, 6, 13, and 18) that show explicit 6 properties of HA outbreaks based on the low divergence of sequences and temporal and 7 spatial criteria (Supplementary Figure S1). 8 9 Alignment with regional sequence data from GISAID 10 Although we did not acquire regional SARS-CoV-2 sequence data during weeks 41-45, we 11 obtained data on the local distribution of sequence variants through alignment with GISAID 12 data. A comparison of cluster-defining/prototypic sequences with the GISAID database 13 shows that some clusters contain highly prevalent sequences in Germany and Europe, while 14 others are rarely found (Supplementary Figure S2). For cluster 1, we identified only 29 15 related sequences of the prototype sequence in GISAID at that time with 12 being sampled 16 in Northern Germany. In contrast, we identified >1000 related sequences of the prototype 17 sequence defining cluster 2. 18 Similarly to cluster 2, the sequences from clusters 7, and 8 show a high prevalence in 19   Table S5). For 20 of 22 dHA and all pHA cases assignment into 5 clusters by sequencing provided plausible infection routes (clusters 1, 2, 4, 5, 6, 7, 8, 13, 16,  6 18). Furthermore, we identified mobile workers in cluster 1 and 5 most likely responsible for 7 the transmission from the index to patients and other staff members. 8 By integrating genomic, temporal and social information, we find that in the majority of cases, 9 transmissions occur from patient to staff on non-COVID wards (clusters 6, 9, 10, 12, 19, 20,  Table S1). However, during the 18 time of our study two critical measures have been introduced to prevent hospital-acquired 19 infections: Systematic testing of staff and the introduction of vaccination for healthcare 20 workers. Starting in January 2021, weekly PCR testing was made mandatory for staff with 21 patient contact with the mean number of PCR tests per week increasing from 2,200 (August -22 December 2020) to 7,000 (January -August 2021) (supplementary Figure S7A). 23 A comparison of the incidence rates of SARS-CoV-2 cases in staff and the general 24 population reveals higher numbers in staff for November 2020 -February 2021 25 (supplementary Figure S8A). This is likely a combination of higher infection rates in 26 healthcare workers [14], and the high number of tests (positive rate in staff 2.3%). However, 27 the positive rate in the general population was estimated at 18%, indicating a higher rate of 28 Figures S7A/B). Furthermore, with healthcare workers 1 being preferentially vaccinated starting January 2021 vaccination rates in staff were higher 2 than in the general population (supplementary Figure S7C/D). Strikingly, in the third wave 3 (March -Mai 2021), when infection dynamics were comparable to the second wave, 4 incidence rates among staff dropped below those seen in the general population (positive 5 rates <0.2% and 10-15%, respectively). Statistical analysis shows that the relative incidence 6 in staff was significantly lower in the third wave than in the second wave ( Figure 6A). The 7 proportion of HA infections in patients classified by ECDC criteria in the second vs. third 8 wave shows a significant reduction of HA cases ( Figure 6B, Supplementary Figure S8B). However, we also show that sequencing data alone, without incorporating epidemiologic 20 data, are insufficient to identify nosocomial outbreaks. Genetic similarity might also indicate 21 common genotypes in the general population, thereby genetically related cases could have 22 been introduced independently. Alignment with local sequences is essential to distinguish 23 independent entries from transmissions in the hospital, which is particularly important if the 24 rate of community transmission is high. 25 (3) In contrast to studies from the first phase of the pandemic, we do not find an increased 26 risk of HCW infection on COVID wards; instead, we find HCW infections predominantly on 27 non-COVID wards. Similar to previous studies [3, 5, 13, 15], we show that transmission 28 among staff and from patients to staff frequently occurred but without further transmission. 1 However, unlike a previous study describing healthcare workers on COVID wards at high risk 2 for nosocomial SARS-CoV-2 infection [5], we find mainly nosocomial transmissions occurring 3 on non-COVID wards. Only cluster 3 suggests patient-to-staff transmission occurring on a 4 COVID ward. Differences, besides different health care systems and containment regimens 5 in the countries, are primarily the periods of study conduction. While Sikkens et al studied 6 transmission events in the first pandemic wave with a low rate of community transmission; 7 our studies focused on the second wave with a moderate -high rate of community 8 transmission and thus, a higher risk of infection by personnel outside of COVID wards. 9 (4) Our study compares the number of healthcare-associated infections in two distinct 10 episodes with the introduction of two critical interventions, regular weekly screening of 11 healthcare workers with patient contact and preferential vaccination of healthcare workers. In 12 particular, the impact of testing has only been modeled to this point [15]. During the study, When interpreting our data several limitations have to be considered. We performed the 3 study at one single center. Regulations and measures regarding testing of patients and staff, 4 isolation of patients, and rules for medical staff during break hours were not uniform in every 5 federal state in Germany, and in addition, facilities themselves could introduce additional 6 measures. Thus, the results cannot be generalized in a federal state such as Germany. 7 In addition to technical limitations in sequencing determined by viral load and RNA integrity, 8 not all samples were available for sequencing (similar to previous studies [3, 13, 15]), and we 9 did not collect local sequences during the study period. However, the alignment with local 10 and regional sequences from GISAID excluded independent entries for most clusters (e.g., 11 clusters 1, 3, 4, 5, 13, 18). Given the limitation that we did not sequence all samples during 12 high community transmission from week 45 and later, we agree to have an incomplete 13 dataset and might miss index cases and contacts. 14 In summary, this study shows how sequencing data improve to uncover transmission routes 15  including contact data, suspected infection sources, and sequencing data. The staff cases #6 21 and #7 in cluster 3 have PCs to patient cases #2 and 4 and between each other. 22 Transmission from patient to staff is highly likely due to the sequence identity (#7 identical, 23 #6 +1 mutation) and the time distance. Staff case #8 is likely also connected. All patient 24 cases are community-associated and were traced back to a shared household (nursing 25 facility), which explains the low divergence of their sequences. Hamburg. Second and third waves are defined by a 7-day incidence rate above 50 and the 2 low point between the two waves. Statistical comparison by Mann-Whitney-test provides 3 significantly reduced incidences in staff for the third wave compared to the second wave 4 (p=0.0014). B: Percentages of healthcare-associated SARS-CoV-2 infections in patients 5 from September 2020 until end of August 2021. HA: Healthcare-associated infections 6 including probable healthcare-associated (pHA) and definite healthcare-associated infections 7 (dHA). Second and third wave definition as in A. Statistical comparison by Mann-Whitney-8 test provides significantly reduced HA patient proportion for the third wave compared to the 9 second wave (p = 0.0169).