There is ongoing uncertainty regarding transmission chains and the respective roles of healthcare workers (HCWs) and elderly patients in nosocomial outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in geriatric settings.covid; HCWs working in non-Covid-19 wards where outbreaks occurred: HCWoutbreak; patients with nosocomial Covid-19: patientnoso) by deriving the proportion of infections attributed to each case type across all posterior trees and comparing them to random expectations.We performed a retrospective cohort study including patients with nosocomial coronavirus disease 2019 (COVID-19) in four outbreak-affected wards, and all SARS-CoV-2 RT-PCR positive HCWs from a Swiss university-affiliated geriatric acute-care hospital that admitted both Covid-19 and non-Covid-19 patients during the first pandemic wave in Spring 2020. We combined epidemiological and genetic sequencing data using a Bayesian modelling framework, and reconstructed transmission dynamics of SARS-CoV-2 involving patients and HCWs, to determine who infected whom. We evaluated general transmission patterns according to case type  and 53 patients. The attack rates ranged from 10% to 19% for patients, and 21% for HCWs. We estimated that 16 importation events occurred with high confidence  that jointly led to up to 41 secondary cases; in six additional cases , importation was possible with a posterior probability between 10% and 50%. Most patient-to-patient transmission events involved patients having shared a ward , in contrast to those having shared a room . Transmission events tended to cluster by case type: patientnoso were almost twice as likely to be infected by other patientnoso than expected ; similarly, HCWoutbreak were more than twice as likely to be infected by other HCWoutbreak than expected . The proportion of infectors being HCWcovid was as expected as random. We found a trend towards a greater proportion of high transmitters (≥2 secondary cases) among HCWoutbreak than patientnoso in the late phases (28.6% vs. 11.8%) of the outbreak, although this was not statistically significant.During the study period (1 March to 7 May 2020), we included 180 SARS-CoV-2 positive cases: 127 HCWs . Nosocomial acquisition of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in geriatric institutions and long-term care facilities (LTCFs) may account for large proportions of all declared coronavirus disease 2019 (Covid-19) cases in many countries, and contribute substantially to morbidity and mortality . BecauseThe terms healthcare-associated, hospital-onset, and nosocomial Covid-19 reflect the uncertainty around defining and distinguishing community- versus healthcare-acquired Covid-19 cases . NeverthThe role of healthcare workers (HCWs) in nosocomial Covid-19 transmission dynamics is complex, as they can be victims and/or vectors of SARS-CoV-2 infection, and can acquire from or transmit to their peers and patients and the community . There iThe aim of this study was to reconstruct transmission dynamics in several nosocomial outbreaks of SARS-CoV-2 involving patients and HCWs in a Swiss university-affiliated geriatric hospital that admitted both Covid-19 and non-Covid-19 patients during the first pandemic wave in Spring 2020.We performed a retrospective cohort study of all patients with nosocomial Covid-19 in four outbreak-affected wards, and all SARS-CoV-2 RT-PCR positive HCWs from 1 March to 7 May 2020. This study is reported according to the STROBE  Strengt  consortium, has 196 acute-care and 100 rehabilitation beds. During the first pandemic wave, a maximum of 176 acute-care beds were dedicated to admitting geriatric patients with Covid-19 who were not eligible for escalation of therapy  . During noso’, the others were assumed to be community-acquired or ‘patientcommunity’. An outbreak was declared when ≥3 cases of HA-Covid-19 (HCWs and patients) with a possible temporal-spatial link were identified  in which nosocomial outbreaks occurred. HCWs worked in either one or another type of ward, except for one HCW who worked in both Covid and non-Covid wards . Six HCWs worked across multiple wards  and were attributed to Covid-wards for the analyses.We defined healthcare-associated (HA) Covid-19 by an onset of symptoms ≥5 days after admission in conjunction with a strong suspicion of healthcare transmission, in accordance with Swissnoso guidelines . We clasentified . HCWs weThe data used for this study were from the sources described previously . First, We produced an epidemic curve using dates of symptom onset; where these were unavailable , we imputed them with the median difference between date of symptom onset and date of nasopharyngeal swab.All Covid-19 cases in the outbreak were confirmed by RT-PCR on nasopharyngeal swabs. We performed SARS-CoV-2 whole-genome sequencing (WGS) using an amplicon-based sequencing method to produce RNA sequences, as previously described  and summWe performed sequence alignment with MUSCLE (v3.8.31). We employed MEGA X  using thWe performed descriptive statistics with medians and interquartile ranges (IQRs), and counts and proportions, as appropriate.outbreaker2  and incomplete reporting of infectious contacts . In addition, the model estimates the proportions of these contacts.Because formal contact tracing was limited during the study period, we constructed contact networks based on ward or room presence for patients based on their ward movements, and on HCW shifts obtained from HR. We defined a contact as simultaneous presence on the same ward on a given day (see Appendix 1). The manner by which R), which we stratified by epidemic phase (early or late with a cut-off on 9 April 2020) and case type .Using the reconstructed transmission trees, we determined the number of imported cases , and the number of secondary cases generated by the imports. Imported caseswere defined as those that do not have apparent ancestors among the cases included in the outbreak. We also calculated the number of secondary  infections for each case, that is, the individual reproductive number (fcase), which we compared with the random expectation considering the prevalence of each case type (see Appendix 1). To better understand the transmission pathways between and within wards, we also estimated (for outbreak and non-outbreak wards), the proportion of infections attributed to infectors in the same ward. We also constructed a matrix representing ward-to-ward transmission. Patient movements between wards were constructed using the implementation of the vistime package  as in the publication by https://www.R-project.org/).We assessed the role of each case type in transmission by estimating the proportion of infections attributed to the case type . The remaining 49 nosocomial cases represented 20.2% (49/242) of all patients hospitalised with Covid-19, and 81.7% (49/60) of nosocomial Covid-19 cases in the Geriatric Hospital. The ward-level attack rates ranged from 10% to 19% among patients. Moreover, 21% of all HCWs in the geriatric hospital had a PCR-positive test. The epidemic curve is shown in covid  than for HCWoutbreak .During the study period, we included a total of 180 SARS-CoV-2 positive cases: 127 HCWs of whom 91 HCWWe obtained SARS-CoV-2 sequences for 148 isolates of the 180 cases (82.2%), including 105 HCWs (82.7%) and 43 patients (81.1%). A rooted phylogenetic tree found substantial genetic diversity, with at least nine clusters and sub-clusters . One cluFrom the reconstructed trees, we identified 22 imports in total  with posterior support ≥10%. The 22 imported cases generated 41 secondary cases (posterior support ≥10%), with a median posterior support of 32.4% (IQR 17.0%–53.7%). When restricting to imports with ≥50% posterior support, there were 16 imported cases 16 , generating 35 secondary cases. There was some degree of uncertainty, reflected by circular transmission pathways, in determination of imported cases and their secondary cases. There were six transmission pairs  where there was uncertainty as to which of the cases was imported and which was a secondary case, that is, for each case in the pair there was a ≥10% probability of importation, but also ≥10% probability of being a secondary case of an imported case. Therefore, in total, 29 cases were ‘pure’ secondary cases of imported cases .covid, HCWoutbreak, patientnoso, and patientcommunity), ward, or ward type (outbreak or non-outbreak wards). There was less confidence in attribution of ancestry when considering by individual or ward, when compared with case type or ward type. The output from the ancestry reconstruction is shown in The epidemiological attribution of the presumptive ward on which patients became infected and that suggested by the model output (see Appendix 1) agree for 95% of the nosocomial cases. The modelling analysis modified the ward attribution for three patients.Among patient-to-patient transmission events, and across all posterior trees, 95.2% (95% CrI 84.2%–100%) involved patients who shared a ward during their hospital stay. In contrast, only 19.7% (95% CrI 6.7%–33.3%) of patient-to-patient transmissions involved patients who had shared a room. The model predicted that C107 infected C131 with a 72.5% probability although they did not share a ward , ranged from 0 to 9 , suggesting that the contribution of these groups to ongoing transmission decreased over the study period. Conversely, the proportion of non-transmitters among HCWcovid was stable at about 55%–60% across the early and the late phase. The proportions of high transmitters were higher among HCWoutbreak than either patientnoso or HCWcovid in the late phases (28.6% vs. 11.8% and 13.9%) of the outbreak. However, due to small numbers, these differences were not statistically significant. These trends were similar in the sensitivity analyses . This was true across all cases, but particularly among HCWs in outbreak wards  and patients . Conversely HCWoutbreak were significantly more likely than expected at random to become infected by other HCWoutbreak . Patients with nosocomial Covid-19 (patientnoso) were significantly more likely than expected at random to be infected by other patientnoso . Full results are shown in We found that cases were significantly less likely than expected at random to be infected by HCWs from COVID wards  than expected . This was driven in particular by transmission from staff or patients to other staff or patients in outbreak wards  . Within-This in-depth investigation of SARS-CoV-2 transmission between patients and HCWs in a geriatric hospital, including several nosocomial outbreaks, has provided many valuable insights on transmission dynamics. First, we showed that the combination of epidemiological and genetic data using sophisticated modelling enabled us to tease out overall transmission patterns. Second, we showed that transmission dynamics among HCWs differed according to whether they worked in Covid wards or in wards where outbreaks occurred. We found that HCW-to-HCW transmission in Covid wards was not higher than expected but the risk of transmission between HCWs in non-Covid wards was twofold higher than expected. Third, we identified excess patient-to-patient transmission events, most of which occurred within the same ward, but not necessarily the same room. Fourth, we identified multiple importation events that led to a substantial number of secondary cases or clusters; most were related to HCWs, but one was related to a patient with community-acquired Covid-19.These results are particularly important, as settings which care for elderly patients, such as geriatrics and rehabilitation clinics or LTCFs have high attack rates of SARS-CoV-2 for both patients and HCWs . In an iThe different transmission patterns between HCWs in Covid wards and outbreak wards  is intriguing. HCWs' behaviour may have affected transmission. Indeed, a previous study Most patient-to-patient transmission events involved patients who were hospitalised in the same ward, and were therefore in close proximity. We cannot exclude transmission from a ‘point-source’ or via a HCW’s contaminated hands . To date, we have little evidence to suggest that this is the case; indeed, the transmission patterns were robust to changes in the model assumptions. Mathematical models have suggested that single-room isolation of suspected cases could potentially reduce the incidence of nosocomial SARS-CoV-2 transmission by up to 35% . In the The current evidence does not support the use of real-time genomics for control of SARS-CoV-2 nosocomial outbreaks . NeverthOur study has several strengths. To the best of our knowledge, it is the only extensive outbreak investigation that employed WGS and sophisticated modelling to assess SARS-CoV-2 transmission in a geriatric acute-care hospital. We performed WGS on isolates from a high proportion (80%) of cases, including those from HCWs which has been previously shown to improve understanding of transmission dynamics . In addiDespite these strengths, some limitations must be acknowledged. First, we included only one sequence from a CA-Covid case. However the method we used to reconstruct who infected whom is able to cope with and identify missing intermediate cases, which allowed us to estimate that the overwhelming majority of cases (91.5%) was captured in our sample. Some misclassification may have occurred, for example, whether an imported cases is truly nosocomial or not. For example, the model predicted that patients C107 and C115 were imported cases. The predicted dates of infection were 14–22 March for case C107 and 8–24 March for case C115; their admission dates were 3 March and 18 March, respectively. The probability that infection occurred on or after date of admission for case C115 was 81%. So the fact that the cases are labelled as ‘imported’ does not preclude the fact that these were still nosocomial cases, simply that their infector was not identified in this outbreak. We did not collect data on adherence to Covid-specific IPC recommendations by HCWs in different wards. We were unable to relate the number of secondary infections with the population (ward) size; more complex models would be required to explain the underlying mechanisms in a context of fluctuating denominators, for example, due to ward closures, and so on. In addition, we performed these investigations during the first pandemic wave with a wild-type variant of SARS-CoV-2 in a susceptible population. For these reasons, the results may no longer be applicable in settings with high vaccination coverage and/or substantial natural immunity, or in later stages of the pandemic, also due to accrued experience in managing and preventing outbreaks. Nevertheless, the lessons learned may be useful in a large number of countries with slow vaccine roll-out due to vaccine hesitancy, particularly among HCWs where there is no vaccine mandate, or unequal access to vaccine supplies . FurtherIn conclusion, strategies to prevent nosocomial SARS-CoV-2 transmission in geriatric settings should take into account the potential for patient-to-patient transmission and the transmission dynamics between HCWs in non-Covid wards, which our study suggests may differ from those in dedicated Covid-19 wards. Congratulations on this useful and technically impressive paper demonstrating that phylogenetic and epidemiologic data can be used in a retrospective cohort to reconstruct that chain of events in terms of case importation into a high risk geriatrics ward. The conclusion that HCW  transmission in non-COVID wards was particularly important is critical for hospital epidemiologists. The methodology advances will hopefully push the field forward in terms of tracking outbreaks in various settings. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Wendy Garrett as the Senior Editor. The following individuals involved in the review of your submission have agreed to reveal their identity: Lulla Opatowski (Reviewer #1); Thi Pham (Reviewer #3).Thank you for submitting your article "Reconstruction of transmission chains of SARS-CoV-2 amidst multiple outbreaks in a geriatric acute-care hospital: a combined retrospective epidemiological and genomic study" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) Clarify and improve Figure 3.2) If possible, add any available details about transmissions between ward-sharing individuals in terms of distance between rooms and beds.3) Add discussion about which results may or may not be generalizable to other health care settings, as well as the issue of misclassifying the infection source.Reviewer #1 (Recommendations for the authors):– Abstract line 63, sentence starting with "the proportion of infectors…" should be clarified.– Because the analysed data provides from the first pandemic wave, several variables may have changed substantially over the studied period. In particular, as described in the methods section, sampling policy varied over time. Sampling was generally achieved differently in HCWs and patients. Were these two aspects accounted for in the statistical model? How does that affect uncertainty on estimates?– What is the level of certainty for provided dates of symptom onset? Were they extracted from medical records or collected in real-time? To what extent does the reconstruction of the transmission chain rely on this variable and was uncertainty considered on that variable?– Do all patients share rooms, and with how many others? What is this 44% as a proportion of those who do?– There is a long-tailed distribution of infection by each individual . How much do heavy spreaders influence the results e.g. would the results from Figure 5 all hold if individual superspreader events were removed from the dataset?– When calculating expectation in Figure 5, this is calculated by the prevalence of Covid-19 in each type of individual on a given ward. In our understanding, the ward was not considered here. Could the authors justify that choice? Would it be feasible to calculate an expectation which took into account the higher risk of transmission when sharing a room, and use this as the comparator against observed infections?– Page 11 line 231, the referenced figure doesn't indicate these probabilities. What is the source?– How was the date of April 9 fixed to define a cut-off between the early phase and late phase? Is it just based on the peak incidence? Or due to a change in screening practice?– The large outbreak size makes figure 3 quite hard to interpret for the reader. Would it be possible to improve it possibly by zooming on some specific cases or wards, and adding global statistics informing on the precision/quality of the inference of transmission routes?– How does the number of secondary infections relate to the population's sizes (i.e. at-risk patients and HCWs from the studied wards). Is that relationship expected?– We may have missed this: (apologies if so): were HCWs cohorted by wards or were there HCWs shared among wards? How may that affect the results here?Reviewer #2 (Recommendations for the authors):This paper addresses an important question about the dynamics of SARS-CoV-2 in a geriatric acute-care hospital. This reviewer's comments address primarily manuscript clarity.General Comments:Patients should not be equated with their conditions. Please replace Covid-19 and non-Covid 19 patients with patients who have Covid-19 and patients who do not have Covid-19 throughout the manuscript.Compare takes "with" not "to".The manuscript includes many "there is," "there are," "it is," xxx. These statements are in passive voice and are usually wordier and more convoluted than a similar statement in active voice. This reviewer suggests revising most or all of these statements. For example, the following sentence could be revised as shown."The reservoir of SARS-CoV-2 in healthcare environments may contribute to amplifying the pandemic [5], and as such, it is important to understand transmission dynamics in these settings.""Because the reservoir of SARS-CoV-2 in healthcare environments may contribute to amplifying the pandemic [5], we need to better understand transmission dynamics in these settings."Statements like "in order to" and "as well as" have extra words that don't add to the meaning. They can be shortened to "to" and "and".Specific CommentsLine 67: Is the word "unexpectedly" needed? This reviewer was confused by that statement. I would assume HCWs knowing that they were caring for patients with Covid-19 might be more in adherence with PPE use and hand hygiene than those on non-Covid-19 units.Lines 93-95: The meaning of "in nosocomial Covid-19" is vague and should be clarified. Does this mean HCWs' role in transmitting SARS-CoV-2 within healthcare facilities is complex?Does it mean HCWs role in the epidemiology of nosocomial SARS-CoV-2 infection iscomplex? This reviewer does not like the word "victim"  in this setting. This reviewer thinks the last part of this sentence will work better if revised to: "as HCWs can acquire SARS-CoV-2 in the community or from their peers and patients and they can transmit SARS-CoV-2 to peers and patients .MethodsThe authors switch frequently between active and passive voice. This reviewer suggests picking one and sticking with it. This reviewer prefers an active voice for clarity and brevity.Line 162: This reviewer does not understand what the authors mean by "trajectories" in this sentence.Lines 165-6: Revise to something like: "conservative in that it accounts for non-infectious contacts and incomplete reporting of infectious contacts and it estimates the proportion of the total contacts in these categories."Lines 18-91: Suggested revision. A post hoc epidemiological analysis of the 53 patients found that 4 of these patients likely acquired Covid-19 in the community (CA-Covid-19). The 49 nosocomial cases represented 20.2% (49/242) of all patients with COVID-19, and 81.7% (49/60) of nosocomial Covid-19 cases identified in the Geriatric Hospital. The ward-level attack rates ranged from 10 to 19% among patients and 21% of all HCWs in the geriatric..."Line 203: This reviewer does not understand this sentence given that the verb appears to be "shows".Lines: 273-5: Suggest revising to something like: "Fourth, we identified multiple importation events that led to a substantial number of secondary cases or clusters. Most transmission events were related to HCWs, but one (?? or some) were related to a patient with community-acquired Covid-19." Note: the authors did not make clear how many transmissions were related to this patient.Lines 282-292: Suggest revising to something like: "HCWs' behavior may have affected transmission. Indeed, Ottolenghi et al., found that HCWs caring for Covid-19 patients were concerned about becoming infected while caring for patients, and therefore may apply IPC measures more rigorously than when caring for patients who do not have Covid-19 [23]. HCWs working in non-Covid wards may not have felt threatened by Covid-19 patients on other wards. HCWs in non-Covid wards also may have underestimated the transmission risk from their peers to a greater extent than those working in Covid wards, and thus did not maintain physical distancing well. In addition, we found a higher mean duration (2.9 days) of presenteeism despite symptoms compatible with Covid-19 among HCWs working in non-Covid wards than for those working in Covid wards (1.6 days), which gives credence to the abovementioned possible explanations for the different transmission patters. Other factors  also may have affected transmission patterns.This reviewer wonders if the following sentence is necessary? "HCWs working in non-Covid wards may not have felt threatened by Covid-19 patients on other wards."Line 294: This author is wondering how the same ward is close proximity when patients are not in the same room. How would the patients have contact with each other?Lines 294-309: Suggested revisions: We cannot exclude transmission from a "point-source" or via an HCW's contaminated hands . To date, we have little evidence to suggest that this is the case; indeed, the transmission patterns were robust to changes in the model assumptions. Mathematical models have suggested that single-room isolation of suspected cases could potentially reduce the incidence of nosocomial SARS-CoV-2 transmission by up to 35% [24]. In the outbreaks we describe, symptomatic patients were identified promptly, with a median delay of 0 days between symptom onset and their first positive test. However, these precautions may not be sufficient as patients may transmit the virus when they are pre-symptomatic [25]. Thus, infection prevention teams may need to identify patients at high risk of developing nosocomial Covid-19 [26] if single rooms are not available for all exposed patients . For example, Mo et al., found that exposure to community-acquired cases who were identified and segregated or cohorted was associated with half the risk of infection compared with exposure to hospital-acquired cases or HCWs who may be asymptomatic [27]. One possible explanation for this finding is that patients with CA-Covid may have passed the peak of infectiousness when they are admitted, whereas patients with HA-Covid cases have frequent unprotected contact with HCWs and other patients during their period of peak infectiousness.Line 310: Suggested edit: The current evidence does not support the use of real-time genomics for control of SARS-CoV-2 nosocomial outbreaks [28].Lines 324-328: Suggested edits: Despite these strengths, our study had some limitations. First, we included only one sequence from a CA-Covid. However, the method we used to reconstruct who infected who is able to cope with and identify missing intermediate cases, which allowed us to estimate that our sample included the overwhelming majority of cases (91.5%). In addition, we performed these investigations during the first pandemic wave in a susceptible population...."Line 334: Replace "in the case of" with "during".Lines 336-338: The main point of the conclusion is not clear. What does it mean to take into account the complex interplay between HCWs in dedicated Covid-19 wards vs non-Covid wards? The way this is worded makes it sound like the HCW on the two different kinds of wards are interacting, which likely is not what the authors intended.Table 1: This reviewer doesn't understand what is meant by "Onset of symptoms before swab date, n (%)". Is this the number of people who were symptomatic before they were tested?Table 2: Should HCWs be written out in the title? Readers in the US will likely not know what a logopedist is. We will call this person a speech therapist. Perhaps the authors could include a footnote defining this term. Did the authors address the longer time from onset of symptoms to testing for HCWs on non-Covid wards compared with those on Covid wards in the results and discussion?Table 3: Dose "Secondary onward transmission" refer to transmission from the case in the first column? Consider putting NA (not applicable) in the blank cells.Line 515: Suggested edit: "Histograms displaying the distributions of secondary cases...."Line 525: Is something missing between "... patient community and "proportion"?Reviewer #3 (Recommendations for the authors):Methods:The authors present the IPC measures on page 6 in the main text and in the supplementary material. However, there seems to be no information on how HCWs adhered to the recommendations that were not mandatory and whether adherence differed between wards. This is probably because the authors did not have any information on that. This is a limitation and should be explained more explicitly or highlighted in the discussion.It's not clear to me whether the HCWs only worked in either COVID or non-COVID wards during the study period. Please clarify in the Methods section.On page 7, line 139: The authors state that they substituted the missing date of symptom onsets with the median difference between symptom onset date and swab date? The epidemic curve in Figure 1 shows the incidence on the y-axis but does the x-axis (date of symptom onset) make sense if not all of the cases were symptomatic?Page 8, line 169 and Figure 4: How was the epidemic phase determined?Results:At the beginning of the Results section, the authors write that post hoc epidemiological analysis showed that were likely 4 patients that had a community-acquired infection. What exactly did the post-hoc epidemiological analysis entail? Later on , they present their analysis of the imported cases. How are these related?Discussion:Could the authors discuss whether misclassifications  even after the analysis is still possible and what impact it could have on their results?SupplementPage 5: Could the authors elaborate on "global influence" for imported cases? Essential revisions:1) Clarify and improve Figure 3.We agree with Reviewer #1 that the initial Figure 3 was difficult to read. We have now created an interactive chart where one can hover over points and zoom in selected areas. We have also removed the colours, which did not have a particular meaning. Following a suggestion from Reviewer #1, we have decided to move this new interactive figure as Figure 1 – Supplementary figure 1, and have replaced it with a more informative figure demonstrating the extent to which our model is able to infer of transmission routes , see new Figure 3).covid”, “HCWoutbreak”, “patientnoso”, “patientcommunity”), C ancestor’s ward, and D ancestor’s ward type .Legend for New Figure 3: Distribution of posterior support of maximum posterior ancestry for all cases, according to identity of A individual ancestor, B ancestor’s case type  If possible, add any available details about transmissions between ward-sharing individuals in terms of distance between rooms and beds.2 (standard deviation 6.5 m2) and 27.8 m2 (standard deviation 0.2 m2) for 2-bed and 3-bed rooms, respectively.” Unfortunately, we do not have precise measurements on distance between beds, but in general, beds next to each other were separated by approximately 1.5 – 2 metres.We have added information about room sizes in the Appendix (page 3): “Indeed, the room sizes (for rooms that were shared) were small, being 23.1 mWhen responding to a related comment from from Reviewer #1, we noticed an error in the pre-processing stage of contact data (n = 839 contacts [4%] were included that should not have been). We therefore re-ran all our analyses after correcting this error. This has led to minor changes in our numeric results but has not altered our main findings and conclusions. We apologise for this inconvenience, and are grateful to have had the opportunity to double-check our work as a result of the high-quality in-depth reviews.3) Add discussion about which results may or may not be generalizable to other health care settings, as well as the issue of misclassifying the infection source.We have added discussion points on the lack of generalisability of our results: “In addition, we performed these investigations during the first pandemic wave with a wild-type variant of SARS-CoV-2 in a susceptible population. For these reasons, the results may no longer be applicable in settings with high vaccination coverage and/or substantial natural immunity, or in later stages of the pandemic with different variants, also due to accrued experience in managing and preventing outbreaks. Nevertheless, the lessons learned may be useful in a large number of countries with slow vaccine roll-out due to vaccine hesitancy, particularly among HCWs where there is no vaccine mandate, or unequal access to vaccine supplies [33]. Furthermore, nosocomial outbreaks of SARS-CoV-2 still occur despite high vaccination coverage . Also, these valuable lessons may be applicable for nosocomial outbreak control in the case of future pandemics due to respiratory viruses with characteristics similar to SARS-CoV-2.” Part in italics was already in the text.outbreaker2) to quantify the uncertainty in determining the ancestry, as it operates within a Bayesian framework. Indeed, in most cases  there are very few patients for whom the ancestry is identified with 100% posterior probability. For example, the model predicted that patients C107 and C115 were most likely imported cases with posterior probabilities of 100% and 57.5%, respectively. The predicted dates of infection were in the range March 14-22 for case C107 and March 8-24 for case C115, which was after their admission dates . The probability that infection occurred on or after date of admission for case C115 was 81%. Therefore the model acknowledges the uncertainty in the classification of these cases as imported. We have modified the section on imported cases (under the heading “imported cases”), and added a sentence after referencing the new Figure 3 to highlight the uncertainty in the model.Misclassifications may still occur, and it is even part of the method .Reviewer #1 (Recommendations for the authors):– Abstract line 63, sentence starting with "the proportion of infectors…" should be clarified.covid was as expected as random”. We hope that it is now clearer.We agree that the sentence was unclear, and have modified it to “The proportion of infectors being HCW– Because the analysed data provides from the first pandemic wave, several variables may have changed substantially over the studied period. In particular, as described in the methods section, sampling policy varied over time. Sampling was generally achieved differently in HCWs and patients. Were these two aspects accounted for in the statistical model? How does that affect uncertainty on estimates?outbreaker2, does allow for undetected cases. It assumes a constant case reporting probability, which is estimated. In our study, this reporting probability estimated to be 91.5% , which confirms that case ascertainment was very high.The reviewer raises an interesting and important point. Screening policies were indeed different between patients and HCWs. For patients, weekly screening surveys were performed in non-Covid wards from April 07, 2020 until May 30, 2020 . In contrast, HCWs from outbreak wards were encouraged to undergo PCR testing, even if asymptomatic, between April 09 and April 16, 2020. These aspects were not explicitly accounted for in the statistical model. However, we are confident that the intensity of screening we performed during that period, led to a good case ascertainment. We note that the method we rely on, Outbreaker2 also estimates, for each observed case, the probability that one or more cases were unobserved in the transmission chain leading to their closest observed ancestry. We compared the posterior distribution of this number of “missed” generations between the early and late phases of the epidemic . We have plotted the distribution of the number of missed generations across posterior trees, stratified by the phase. This suggests very little difference in the distribution of missed generations between the two phases. We have included this figure as Figure 3 – Supplementary Figure 2.– What is the level of certainty for provided dates of symptom onset? Were they extracted from medical records or collected in real-time? To what extent does the reconstruction of the transmission chain rely on this variable and was uncertainty considered on that variable?The dates of symptom onset were collected prospectively in real-time through several sources, minimising risk of bias. For patients, data were generated by the prospective surveillance of hospitalised patients with Covid-19 mandated by the Swiss Federal Office of Public Health, and for HCWs from both the hospital’s department of Occupational Health and from the prospective cohort study of outpatient Covid-19 testing in the hospital. We have clarified this in the “Data sources” section: “Dates of symptom onset were available for both patients and HCWs each source, respectively.”The date of symptom onset is a main input of the model, as it is the starting point from which the date of infection is inferred. Although we did not directly account for potential uncertainty in the date of onset, our model considers whole distributions for the serial interval and incubation period which would allow absorbing a small level of uncertainty in onset dates, assuming such uncertainty is similar to those in studies which estimated the serial interval and incubation period.– Do all patients share rooms, and with how many others? What is this 44% as a proportion of those who do?Out of all 43 patients, there were 5 patients (9.4%) who did not share a room with another Covid patient; this number increases to 11 patients (20.7%) if a “significant” room sharing is considered . Of the 319 days where a room was shared, 256 days (80.3%) involved a room being shared by 2 patients, and 63 (19.7%) by 3 patients. The proportion of patient-to-patient transmissions that involved patients sharing a room was calculated for each posterior transmission tree (by determining whether the ancestor/infector had ever shared a room with the infectee), with the denominator being all patient-to-patient transmissions.When responding to this comment we noticed an error in the pre-processing stage of contact data . We re-ran all the analyses after correcting this error. This has led to minor changes in our numeric results does not alter our major findings and conclusions. We apologise for this inconvenience, and thank the referee for their in-depth review which has given us the opportunity to double-check our work.– There is a long-tailed distribution of infection by each individual . How much do heavy spreaders influence the results e.g. would the results from Figure 5 all hold if individual superspreader events were removed from the dataset?casef) attributed to each case type , i.e., the results from Figure 5, but excluding posterior transmission trees where there is at least one infector with ≥5 secondary cases. In We thank the Reviewer for this interesting observation and question. Altogether, the proportion of super-spreading events (defined as generating ≥5 secondary cases) among all posterior trees is very low (0.4%). The majority of posterior trees (54.1%) have no infectors with ≥5 secondary cases. The proportion of posterior trees with 1, 2, and ≥3 infectors with ≥5 secondary cases (super-spreaders) are 37.3%, 7.9% and 0.7%, respectively. The overwhelming majority of posterior trees (88.6%) do not have infectors who are the plausible ancestors of ≥6 infectees. For these reasons, we anticipated the influence of the superspreading events across all posterior transmission trees to be negligible. To confirm this, we performed a sensitivity analysis, estimating the proportion of transmissions . We have added that the referenced figure shows the ward movements, hoping this makes it clearer.– How was the date of April 9 fixed to define a cut-off between the early phase and late phase? Is it just based on the peak incidence? Or due to a change in screening practice?The epidemic phase was determined on the basis of several factors. First, many preventive measures were implemented around that date . Second, approximately half of the cases occurred in each phase of the outbreak. Third, the overall trend in the epidemic curve can be interpreted as increasing in the first phase, and decreasing in the second phase.– The large outbreak size makes figure 3 quite hard to interpret for the reader. Would it be possible to improve it possibly by zooming on some specific cases or wards, and adding global statistics informing on the precision/quality of the inference of transmission routes?We agree with Reviewer 1 that Figure 3 was difficult to read. We have now created an interactive version of this figure where one can hover over points and zoom in selected areas. We have also removed the colours, which did not have a particular meaning. We have decided to move this new interactive figure as Figure 3 – Supplementary Figure 1, and have replaced it with a more informative figure demonstrating the extent to which our model is able to infer of transmission routes , see new Figure 3).– How does the number of secondary infections relate to the population's sizes (i.e. at-risk patients and HCWs from the studied wards). Is that relationship expected?The Reviewer raises a valid and interesting point. Generally, and under normal circumstances, the ward sizes are comparable, as are the number of HCWs working in each ward. However, the number of patients in a ward is constantly fluctuating due to admissions/discharges as well as ward closures. Similarly, the number of HCWs fluctuates due to sick leave or annual leave. Therefore, the wards are not “closed systems”, and as the denominator fluctuates, more complex models would be required in order to explain the underlying mechanisms. We have added a sentence in the discussion to acknowledge this limitation:“We were unable to relate the number of secondary infections with the population (ward) size; more complex models would be required to explain the underlying mechanisms in a context of fluctuating denominators, e.g., due to ward closures, etc.”– We may have missed this: (apologies if so): were HCWs cohorted by wards or were there HCWs shared among wards? How may that affect the results here?We thank the Reviewer for pointing this out. HCWs did work exclusively in one ward type or another. We have clarified this in the Methods (“Definitions” section):“HCWs worked in either one or another type of ward, except for one HCW who worked in both Covid and non-Covid wards . Six HCWs worked across multiple wards  and were attributed to Covid-wards for the analyses.”Reviewer #2 (Recommendations for the authors):This paper addresses an important question about the dynamics of SARS-CoV-2 in a geriatric acute-care hospital. This reviewer's comments address primarily manuscript clarity.General Comments:Patients should not be equated with their conditions. Please replace Covid-19 and non-Covid 19 patients with patients who have Covid-19 and patients who do not have Covid-19 throughout the manuscript.Compare takes "with" not "to".We absolutely agree with the reviewer that patients should not be equated with their conditions. Nevertheless, we feel that the use of “patients who have Covid-19” may unnecessarily lengthen the manuscript and complicate reading. Also, we believe that the use of “Covid-19 patients” is neither disrespectful or stigmatising, and has been used in official documents from highly respected institutions . Nevertheless, should the Editor feel the same way as the Reviewer, we would be happy to reword.We have corrected “compared to” with “compared with”.The manuscript includes many "there is," "there are," "it is," xxx. These statements are in passive voice and are usually wordier and more convoluted than a similar statement in active voice. This reviewer suggests revising most or all of these statements. For example, the following sentence could be revised as shown."The reservoir of SARS-CoV-2 in healthcare environments may contribute to amplifying the pandemic [5], and as such, it is important to understand transmission dynamics in these settings.""Because the reservoir of SARS-CoV-2 in healthcare environments may contribute to amplifying the pandemic [5], we need to better understand transmission dynamics in these settings."We thank the Reviewer for their comment, which we have taken into consideration wherever possible. We do, however, defer to the Editor and the Editorial team for further editorial and wording matters.Statements like "in order to" and "as well as" have extra words that don't add to the meaning. They can be shortened to "to" and "and".We understand the Reviewer’s point, and have modified accordingly.Specific CommentsLine 67: Is the word "unexpectedly" needed? This reviewer was confused by that statement. I would assume HCWs knowing that they were caring for patients with Covid-19 might be more in adherence with PPE use and hand hygiene than those on non-Covid-19 units.We understand the Reviewer’s point, and indeed, in 2022 it is conventional wisdom to make the assumption raised. However, at the time these outbreaks occurred, we did not think there would be such a stark difference in terms of cross-infections amongst HCWs in Covid vs. non-Covid wards. Indeed, the finding is unexpected. For this reason, should the Editor agree, we prefer to keep the word “unexpectedly”.Lines 93-95: The meaning of "in nosocomial Covid-19" is vague and should be clarified. Does this mean HCWs' role in transmitting SARS-CoV-2 within healthcare facilities is complex?Does it mean HCWs role in the epidemiology of nosocomial SARS-CoV-2 infection iscomplex? This reviewer does not like the word "victim"  in this setting. This reviewer thinks the last part of this sentence will work better if revised to: "as HCWs can acquire SARS-CoV-2 in the community or from their peers and patients and they can transmit SARS-CoV-2 to peers and patients .We thank the Reviewer for their comment. We have modified the sentence “in nosocomial Covid-19” to “in nosocomial Covid-19 transmission dynamics”, and hope that the sentence is clearer. We understand that the reviewer does not like the word victim; however, we believe that its use is justified in the present context, and unlike many health conditions, we feel that in the case of Covid-19 in most societies around the world there is no particular stigma associated with it. We also respectfully refer the Reviewer to the book entitled “The Patient as Victim and Vector: Ethics and Infectious Disease” by Margaret P. Battin, Leslie P. Francis, Jay A. Jacobson, and Charles B. Smith .MethodsThe authors switch frequently between active and passive voice. This reviewer suggests picking one and sticking with it. This reviewer prefers an active voice for clarity and brevity.We thank the Reviewer for these suggestions that we have implemented.Line 162: This reviewer does not understand what the authors mean by "trajectories" in this sentence.We meant “ward movements”, which has now replaced “trajectories”.Lines 165-6: Revise to something like: "conservative in that it accounts for non-infectious contacts and incomplete reporting of infectious contacts and it estimates the proportion of the total contacts in these categories."We have replaced the sentence to the following, hoping that the Reviewer finds it clearer:outbreaker2 handles these contacts is conservative in that it allows for non-infectious contacts to occur  and incomplete reporting of infectious contacts . In addition, the model estimates the proportions of the proportions of these contacts.”“The manner by which Lines 18-91: Suggested revision. A post hoc epidemiological analysis of the 53 patients found that 4 of these patients likely acquired Covid-19 in the community (CA-Covid-19). The 49 nosocomial cases represented 20.2% (49/242) of all patients with COVID-19, and 81.7% (49/60) of nosocomial Covid-19 cases identified in the Geriatric Hospital. The ward-level attack rates ranged from 10 to 19% among patients and 21% of all HCWs in the geriatric..."We thank the Reviewer for these suggestions that we have implemented.Line 203: This reviewer does not understand this sentence given that the verb appears to be "shows".We have modified this sentence in order for it to be grammatically correct, and hopefully easier to understand:“There is also a large cluster  with several subclusters includes 19 HCWs, nine patients, and three community cases; ward movements for the patients are shown in Appendix Figure 1”Lines: 273-5: Suggest revising to something like: "Fourth, we identified multiple importation events that led to a substantial number of secondary cases or clusters. Most transmission events were related to HCWs, but one (?? or some) were related to a patient with community-acquired Covid-19." Note: the authors did not make clear how many transmissions were related to this patient.We thank the Reviewer for these suggestions that we have implemented. We would like to point out that the number of secondary transmissions related to the patient with community-acquired Covid-19 was mentioned in the Results, under the heading “Imported cases”, which, incidentally, has been amended to better reflect the uncertainty associated with the reconstruction of the transmission chains:“Imported CasesFrom the reconstructed trees, we identified 22 imports in total  with posterior support ≥10%. The 22 imported cases generated 41 secondary cases (posterior support ≥10%), with a median posterior support of 32.4% (IQR 17.0-53.7%). When restricting to imports with ≥50% posterior support, there were 16 imported cases 16 , generating 35 secondary cases. There was some degree of uncertainty, reflected by circular transmission pathways, in determination of imported cases and their secondary cases. There were 6 transmission pairs  where there was uncertainty as to which of the cases was imported and which was a secondary case, i.e. for each case in the pair there was a ≥10% probability of importation, but also ≥10% probability of being a secondary case of an imported case. Therefore, in total, 29 cases were “pure” secondary cases of imported cases (Table 3).”Lines 282-292: Suggest revising to something like: "HCWs' behavior may have affected transmission. Indeed, Ottolenghi et al., found that HCWs caring for Covid-19 patients were concerned about becoming infected while caring for patients, and therefore may apply IPC measures more rigorously than when caring for patients who do not have Covid-19 [23]. HCWs working in non-Covid wards may not have felt threatened by Covid-19 patients on other wards. HCWs in non-Covid wards also may have underestimated the transmission risk from their peers to a greater extent than those working in Covid wards, and thus did not maintain physical distancing well. In addition, we found a higher mean duration (2.9 days) of presenteeism despite symptoms compatible with Covid-19 among HCWs working in non-Covid wards than for those working in Covid wards (1.6 days), which gives credence to the abovementioned possible explanations for the different transmission patters. Other factors  also may have affected transmission patterns.This reviewer wonders if the following sentence is necessary? "HCWs working in non-Covid wards may not have felt threatened by Covid-19 patients on other wards."We thank the Reviewer for taking the time to make these detailed suggestions. We have largely changed the paragraph accordingly:“HCWs' behaviour may have affected transmission. Indeed, Ottolenghi et al., found that HCWs caring for Covid-19 patients were concerned about becoming infected while caring for patients, and therefore may apply IPC measures more rigorously than when caring for patients who do not have Covid-19 [23]. HCWs working in non-Covid wards may not have felt threatened by Covid-19 patients who were in principle allocated to other wards, and thus not in their direct care. HCWs in non-Covid wards also may have underestimated the transmission risk from their peers to a greater extent than those working in Covid wards, and thus may not have maintained physical distancing well. In addition, we found a higher mean duration (2.9 days) of presenteeism despite symptoms compatible with Covid-19 among HCWs working in non-Covid wards than for those working in Covid wards (1.6 days), which gives credence to the abovementioned possible explanations for the different transmission patterns. Other factors  also may have affected transmission patterns.”Line 294: This author is wondering how the same ward is close proximity when patients are not in the same room. How would the patients have contact with each other?The architectural layout of the hospital means that there are 2 wings per floor, each with a central corridor, and 2 wards per wing (without separation). On either side of the central corridor are patient rooms and/or offices . Therefore, the wards themselves are crowded areas, and it is sufficient for a patient to step outside their room in the corridor to potentially come in contact with another patient. We have added this information in the Appendix, alongside data on room sizes, under the heading “Ward architecture and room sizes in the geriatric hospital”.Lines 294-309: Suggested revisions: We cannot exclude transmission from a “point-source” or via an HCW’s contaminated hands . To date, we have little evidence to suggest that this is the case; indeed, the transmission patterns were robust to changes in the model assumptions. Mathematical models have suggested that single-room isolation of suspected cases could potentially reduce the incidence of nosocomial SARS-CoV-2 transmission by up to 35% [24]. In the outbreaks we describe, symptomatic patients were identified promptly, with a median delay of 0 days between symptom onset and their first positive test. However, these precautions may not be sufficient as patients may transmit the virus when they are pre-symptomatic [25]. Thus, infection prevention teams may need to identify patients at high risk of developing nosocomial Covid-19 [26] if single rooms are not available for all exposed patients . For example, Mo et al., found that exposure to community-acquired cases who were identified and segregated or cohorted was associated with half the risk of infection compared with exposure to hospital-acquired cases or HCWs who may be asymptomatic [27]. One possible explanation for this finding is that patients with CA-Covid may have passed the peak of infectiousness when they are admitted, whereas patients with HA-Covid cases have frequent unprotected contact with HCWs and other patients during their period of peak infectiousness.We thank the Reviewer for taking the time to make these detailed suggestions, which we have implemented in the manuscript.Line 310: Suggested edit: The current evidence does not support the use of real-time genomics for control of SARS-CoV-2 nosocomial outbreaks [28].We thank the Reviewer for their suggestion. We have also taken the opportunity to include a reference to the preprint by Stirrup et al., (medRxiv 2022.02.10.22270799) which reports the COG-UK HOCI study.Lines 324-328: Suggested edits: Despite these strengths, our study had some limitations. First, we included only one sequence from a CA-Covid. However, the method we used to reconstruct who infected who is able to cope with and identify missing intermediate cases, which allowed us to estimate that our sample included the overwhelming majority of cases (91.5%). In addition, we performed these investigations during the first pandemic wave in a susceptible population...."We thank the Reviewer for these suggestions, some of which we have implemented.Line 334: Replace "in the case of" with "during".We believe that “in the case of” is a better fit, as although it is likely that there will be another pandemic, it is still an unknown.Lines 336-338: The main point of the conclusion is not clear. What does it mean to take into account the complex interplay between HCWs in dedicated Covid-19 wards vs non-Covid wards? The way this is worded makes it sound like the HCW on the two different kinds of wards are interacting, which likely is not what the authors intended.We have replaced the conclusion by the following sentence, hoping it is clearer:“In conclusion, strategies to prevent nosocomial SARS-CoV-2 transmission in geriatric settings should take into account the potential for patient-to-patient transmission and the transmission dynamics between HCWs in non-Covid wards, which our study suggests may differ from those in dedicated Covid-19 wards.”Table 1: This reviewer doesn't understand what is meant by "Onset of symptoms before swab date, n (%)". Is this the number of people who were symptomatic before they were tested?Yes, the Reviewer is correct.Table 2: Should HCWs be written out in the title? Readers in the US will likely not know what a logopedist is. We will call this person a speech therapist. Perhaps the authors could include a footnote defining this term. Did the authors address the longer time from onset of symptoms to testing for HCWs on non-Covid wards compared with those on Covid wards in the results and discussion?HCWs was written out in full. Logopedist was replaced by “speech therapist”.The longer time from onset of symptoms to testing has been added in the Results section. We had already brought up the issue in the third paragraph of the Discussion.Table 3: Dose "Secondary onward transmission" refer to transmission from the case in the first column? Consider putting NA (not applicable) in the blank cells.We have replaced “Secondary onward transmission” by “Secondary onward transmission by imported case” in order to make it clearer. Blank cells were replaced by N/A.Line 515: Suggested edit: "Histograms displaying the distributions of secondary cases...."Thank you for this suggestion which we have taken into account.Line 525: Is something missing between "... patient community and "proportion"?Apologies as the phrase “proportion of infections attributed to the type of case” was accidentally inserted. We hope that after deletion the sentence is clearer.Reviewer #3 (Recommendations for the authors):Methods:The authors present the IPC measures on page 6 in the main text and in the supplementary material. However, there seems to be no information on how HCWs adhered to the recommendations that were not mandatory and whether adherence differed between wards. This is probably because the authors did not have any information on that. This is a limitation and should be explained more explicitly or highlighted in the discussion.We agree with the Reviewer that this is a limitation of our study. We have added this in the Discussion:“We did not collect data on adherence to Covid-specific IPC recommendations by HCWs in different wards.”.It's not clear to me whether the HCWs only worked in either COVID or non-COVID wards during the study period. Please clarify in the Methods section.We thank the Reviewer for pointing this out. HCWs did work exclusively in one ward type or another. We have clarified this in the Methods (“Definitions” section):“HCWs worked in either one or another type of ward, except for one HCW who worked in both Covid and non-Covid wards . Six HCWs worked across multiple wards  and were attributed to Covid-wards for the analyses.”On page 7, line 139: The authors state that they substituted the missing date of symptom onsets with the median difference between symptom onset date and swab date? The epidemic curve in Figure 1 shows the incidence on the y-axis but does the x-axis (date of symptom onset) make sense if not all of the cases were symptomatic?outbreaker2 model uses date of symptom onset as the starting point from which the date of infection is inferred statistically. Should the Editor request that we construct an epidemic curve using date of swab or removing the patients with the missing date of symptom onset, we will gladly do it. We can also highlight in the epidemic curve the 8 asymptomatic cases, should the Editor and/or the Reviewer request this.The Reviewer has rightly pointed out that the Epidemic curve is based on symptom onset and that for the asymptomatic cases  there is no date available. It is common practice to use date of symptom onset to construct epidemic curves, and this explains our choice. So yes, the epidemic curve includes the asymptomatic cases, and we have clarified this in the legend of Figure 1 (“Includes 8 asymptomatic cases for whom date of onset was inferred (c.f. text).”). We could have, instead, used the date of the first positive sample. While using the date of the first positive sample might ensure that there were no missing data points, it does provide a completely different picture of the progression of the outbreak. For example, the earliest positive swab was on March 11, 2020, whereas the earliest onset of symptoms was 6 days earlier on March 05, 2020. An additional reason is that the Page 8, line 169 and Figure 4: How was the epidemic phase determined?The epidemic phase was determined on the basis of several factors. First, many preventive measures were implemented around that date . Second, approximately half of the cases occurred in each phase of the outbreak. Third, the overall trend in the epidemic curve can be interpreted as increasing in the first phase, and decreasing in the second phase. We have added justification for this in the Appendix.Results:At the beginning of the Results section, the authors write that post hoc epidemiological analysis showed that were likely 4 patients that had a community-acquired infection. What exactly did the post-hoc epidemiological analysis entail? Later on , they present their analysis of the imported cases. How are these related?We thank the Reviewer for their careful reading of our paper. The post hoc epidemiological analysis consisted of a chart review performed by the investigators at the start of the study, as opposed to conclusions that were arrived at during the management of the outbreak. The classification of cases as being imported is based on the model output, and they are defined as those that do not have apparent ancestors among the cases included in the outbreak. We have included this information in the manuscript, hoping that this has been clarified.Discussion:Could the authors discuss whether misclassifications  even after the analysis is still possible and what impact it could have on their results?outbreaker2) to quantify the uncertainty in determining the ancestry, as it operates within a Bayesian framework. Indeed, in most cases  there are very few patients for whom the ancestry is identified with 100% posterior probability. For example, the model predicted that patients C107 and C115 were most likely imported cases with posterior probabilities of 100% and 57.5%, respectively. The predicted dates of infection were March 14-22 for case C107 and March 8-24 for case C115, which was after their admission dates . The probability that infection occurred on or after date of admission for case C115 was 81%. Therefore the model acknowledges the uncertainty in the classification of these cases. We have modified the section on imported cases (under the heading “imported cases”), and added a sentence after referencing the new Figure 3 to highlight the uncertainty in the model.The Reviewer rightly points out that misclassifications may still occur. It is even part of the method .SupplementPage 5: Could the authors elaborate on "global influence" for imported cases?The global influence is a way of measuring the impact of a single observation on the log-likelihood of a parameter. During a preliminary run of the model, cases are removed in turn (with a leave one out approach) and the “global influence” of each case is measures as the extent to which removing it affects the genetic log-likelihood. Cases with high global influence  are considered to be genetic outliers, and classified as imported cases. This approach is described in more detail in the original outbreaker manuscript : e1003457). We have added these details in the Appendix.


Machine Learning for Health (ML4H) has demonstrated efficacy in computer imaging and other self-contained digital workflows, but has failed to substantially impact routine clinical care. This is no longer because of poor adoption of Electronic Health Records Systems (EHRS), but because ML4H needs an infrastructure for development, deployment and evaluation within the healthcare institution. In this paper, we propose a design pattern called a Clinical Deployment Environment (CDE). We sketch the five pillars of the CDE: (1) real world development supported by live data where ML4H teams can iteratively build and test at the bedside (2) an ML-Ops platform that brings the rigour and standards of continuous deployment to ML4H (3) design and supervision by those with expertise in AI safety (4) the methods of implementation science that enable the algorithmic insights to influence the behaviour of clinicians and patients and (5) continuous evaluation that uses randomisation to avoid bias but in an agile manner. The CDE is intended to answer the same requirements that bio-medicine articulated in establishing the translational medicine domain. It envisions a transition from “real-world” data to “real-world” development. Bold claims and huge investments suggest Machine Learning (ML) will transform healthcare . High imWidespread adoption of electronic health records (EHR) might be thought a sufficient prerequisite for this ambition. Yet while EHR adoption is growing at pace , those MIn contrast, the EHR is in constant flux. Both the data and the data model are updating. New wards open, staffing patterns are adjusted and from time to time major incidents  disrupt everything. There are multiple interacting users, and eventually there will be multiple interacting algorithms, and organizations will face the ML equivalent of poly-pharmacy . AlgoritA future that sees ML4H generate value from the EHR requires an alternative design pattern. TREs excel at meeting the needs of population health scientists but they do not have the full complement of features required to take an ML4H algorithm from bench-to-bedside. Using drug development as an an analogy, a TRE is custom made for drug discovery not translational medicine .development and deployment.In this paper, we describe the functional requirements for a Clinical Deployment Environment (CDE) for translational ML4H. These requirements map closely to the classical components of translational medicine, but differ in that algorithms will require ongoing stewardship even after a successful deployment. The CDE is an infrastructure that manages algorithms with the same regard that is given to medicines (pharmacy) and machines . Moreover, the value of ML4H will not just be from externally developed blockbuster models, but will also derive from specific and local solutions. Our vision of a CDE therefore enables both 1.Real World Development2.ML-Ops for Health3.Responsible AI in practice4.Implementation science5.Continuous evaluationWe describe these pillars below alongside figures and vignettes reporting early local experience in our journey building this infrastructure.Our CDE is supported by five pillars:1.Real-world data (RW-Data) means the use of observational data at scale augmented by linking across multiple data sources to generate insights simply not available from isolated controlled clinical trials . The FDAGiven the sensitive nature of health data, these initiatives depend on expanding investment into TREs . TREs arin situ , but wetitution .via different languages and technologies), ownership is often split between two different teams. One team prepares the raw data and develops the model, and another prepares the live data and deploys the model. We argue instead that the same team should be able develop and deploy. This should accelerate iteration, reduce cost and increase quality  Firstly, data updates must match the cadence of clinical decision making. For most inpatient and acute care pathways, decisions are in real-time (minutes or hours) at the bedside or in the clinic. (2) Secondly, development using live data must be sandboxed and so the clinical system responsible for care delivery is protected (3) thirdly, privacy must be managed such that teams are able to develop end-user applications that inevitably display patient identifiable information (PII) alongside the model outputs: an anonymous prediction is of little use to a clinician. (4) Fourthly, attention must be paid to developer ergonomics. Where development and deployment steps are separated physically (the TRE paradigm) or functionally  is appropriate where the algorithms operate within a constant and predictable environment (e.g. code embedded within a cardiac pacemaker). But, as already argued, ML4H models working with the EHR are likely to find themselves operating in a significantly more complex landscape. This inconstant environment where algorithms themselves may only have temporary utility has parallels to the commercial environment exploited so successfully by the tech giants.These companies have cultivated an approach to model deployment called “ML-Ops”. This combines the practices of “DevOps” (a portmanteau of Software Development plus IT operations)  that focThis constant adjustment of algorithms based on their continuously measured quality and performance needs a workforce as well as a technology stack. Just as the safe delivery of medicines to the bedside is the central activity of a hospital pharmacy team, the safe delivery of algorithms will require the development of similarly skilled and specialized practitioners, and we should expect to see clinical ML-Ops departments in the hospital of the future. Others have made similar proposals and labeled this as “algorithmic stewardship” or “AI-QI” , 27. Sim3.Pillars 1 and 2 should engender well designed and well engineered algorithms, but they do not protect against the unintentional harm that AI may induce. Algorithms can only learn from a digital representation of the world that representation in turn cannot encode moral or ethical standards. Unfair outcomes, discrimination against sub-populations and bias are all reported shortcomings . In a dy3.1.We argue that model explainability  methods need to be prioritized to help systematize and coordinate the processes of model troubleshooting by developers, risk-management by service providers, and system-checks by auditors –35. Most3.2.via a human-in-the-loop oversight. This may even permit less interpretable models to operate when implemented in conjunction with an effective fail-safe system.Prediction models that map patient data to medically meaningful classes are forced to predict without the option to flag users when the model is unsure of an answer. To address this problem, there is good evidence that methods such as Bayesian deep learning and various uncertainty estimates  can prov3.3.via calibration drift detection and model recalibration (via Pillar 2 (ML-Ops) will be required to overcome what would otherwise be a learning process encumbered by regulatory barriers   embedded in the EHR  , 53 or v the EHR .effectiveness (T2) rather than just efficacy (T1)  .Investigations to define how system can influence behavior will need specialist support and tooling. This might require tools embedded within the user interface to evaluate and monitor user interaction, and capture user feedback , or direDespite the oft cited risks of alert fatigue with Clinical Decision Support Systems (CDSS) , there i5.via a randomized controlled trial (RCT). Randomization handles unanticipated bias and ML4H should hold itself to the same standard but of 350,000 studies registered on ClinicalTrials.gov in 2020, just 358 evaluated ML4H, and only 66 were randomized  . But in This time the EHR may provide an advantage instead of just additional complexity. Since ML4H algorithms must be implemented through some form of direct or indirect CDSS, then the next logical step is to randomize the deployment of those alerts. This in itself is not novel. Randomized deterministic alerts from CDSS are part of the standard evaluation toolkit for quality improvement initiatives in at NYU Langone , and forThe final pillar in our CDE uses the same approach for the probabilistic insights derived from ML4H. Excellent patient and public involvement, and ethical guidance, will be required to distinguish those algorithms that require per patient point-of-care consent from those that can use opt-out or cluster methods. But we think that latter group is large for two reasons. Firstly, patients are exposed to varying treatment regimes by dint of their random interaction with different clinicians based on geography  and time (staff holidays and shift patterns etc.). This routine variation in practice is summarized as the 60-30-10 problem: 60% of care follows best practice; 30% is wasteful or ineffective and 10% is harmful . This hiSupplementary Material).At our own institution, we have extended this ethical and safety case one step further, and we are piloting a study design where the randomization is non-mandatory: a nudge not an order . The cliEmbedded randomized digital evaluation should permit rapid evidence generation, and build the trust needed to support the implementation described under Pillar 4.We have described a template for a Clinical Deployment Environment that supports the translation of ML4H algorithms from bench to bedside. Although the requirements differ, the objective is similar to that for drug development. A similar approach to phasing has previously been proposed for (biomarker) prediction models .in vitro. Metrics used to evaluate candidates, such binding affinity or other pharmacokinetic properties, describe the properties of the molecule . Phase 3 ML4H trials will require integration of data from multiple centers for algorithms acting on specific decisions but inevitably adapted to their local data environment.development deliberately seeks to merge the steps. But the parallel is drawn to highlight the effort necessary to see ML4H have an impact on the clinical and operational decision making in the workplace. Heretofore this effort has been hugely underestimated.The phases of drug development are not meant to be matched 1:1 to the pillars of the CDE described here: in fact, our argument for “real-world” Even this analogy stops short of the full task of deployment. With drug development, the universities and the pharmaceutical industry go on to take advantage of a supply chain to deliver the drug to the hospital with the necessary quality control and monitoring. Those prescribing and administering the drug have spent years in training, and are supported by pharmacists and medication safety experts. And even after the drug is administered, observation and long term follow-up continue to identify side-effects and long term hazards.is within (not as) a medical device, but is only just being envisioned where the data driving ML4H comes from the EHR. This distinction needs to be made else the disillusionment with the promise of ML4H will continue. The technology does have the potential to change how we deliver health but the methodology alone is insufficient. The impressive demonstrations of the power of AI and ML to beat humans in games, and predict protein structures does not mean that these tools are ready for wide spread deployment.That network of expertise and infrastructure is largely in place where software But we should not be pessimistic. As per author William Gibson, it is clear that “The Future Has Arrived — It”s Just Not Evenly Distributed.” Beyond healthcare, machine learning has already demonstrated that it can reliably create value . It is nThe Five Pillars outlined here are a sketch of that redistribution. They are born from our local experience Pillars 1, 2 and 5) and our wider observations (Pillars 3 and 4). They fundamentally are an argument for a professionalization of ML4H, and a caution against the “get-rich quick” headlines in the popular and scientific press (, 2 and 5All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication. All authors contributed to the article and approved the submitted version.