mteb/CovidQA huggingface dataset:
For the different categories, infection rate (EI/Exposed Residents (ER), in percentage) was calculated according to sex, age group, LOS and autonomy status. To investigate all-cause lethality and define the appropriate period for the 56-day monitoring (D 1 to 56 ), the all-cause lethality rate per 7-day interval (LR n /I n, n = 1 to 8 ) was calculated: (number of deaths during interval I n /(EI alive the first day of n th studied interval minus lost to follow-up EI during the interval I n ) � 100).
Seeing as successive clusters could occur within the same site, potentially influencing allcause lethality, the serial interval in days (SI d ) was calculated. The SI d was the time period between the onset of symptoms of the last case in initial outbreak (N) and the onset of symptoms of the first case in the following outbreak (N+1). Investigations were performed when SI d was shorter or equal to the length of the previous D 1 to 56 and the following parameters were evaluated for these specific situations: number of episodes, residents infected in both outbreaks, and death among the identified individuals.

Finally, according to the death rate and the impact of successive outbreaks, the number of 7-day intervals (N.I n ) to take into account was defined, and the all-cause lethality rate was analyzed during these periods (I 1 to N th .I n or D 1 to 7 � N ).
All-cause lethality rates were calculated with the following formula: (number of deaths from D 1 to 7 � N /(EI number at D 1 minus lost to follow-up among EI during the period D 1 to 7 � N ) � 100). Estimation of the turnover rate per 7-day interval among the infected residents was calculated on the base of the LOS (median in years) with the following formula: (proportion of discharged residents: 50.0% in the case of the median)/[(median LOS � 365)/7)]. The average rate of residents discharged per 7-day period was calculated: [(number of lost to follow up during the period D 1 to 7 � N /number of exposed and infected residents at D 1 )/N 7-day interval] � 100.
As some residents were included in several outbreaks during the surveillance, the observations were not completely independent; non-parametric tests were used as a result. In univariate analysis, Chi-square or Fisher exact tests (expected number of frequencies fewer than 5) were used to compare infection and lethality rates according to the studied parameters and the odds ratio was calculated by median-unbiased estimation. The Kruskal-Wallis test was used to compare median values. Confidence intervals for medians were calculated with bootstrap methods.
Covariate adjusted analyses were performed with two tables (2x2). The respective impact of each individual factor was tested with Mantel-Haenszel chi-squared tests. The equality of the stratum odds ratios was tested with the Woolf test of homogeneity. Finally, for each virus context, multiple tables (2x2) were generated and tested with confounding variables, effect modifiers or covariables. Statistical analyses were done using R for Mas OS X version R 3.4.1 software with RStudio version 1.0.153. A file is transmitted in the Supporting Information with all R codes and the packages used (S1 R Codes). Differences were considered significant at p � 0.05.
The French Data Protection Authority approved data collection and analysis (DE-2013-074) and the local ethics committee (Espace Local de Réflexion Ethique, Centre Hospitalier de Rouffach) approved the study protocol (ERLE-32). According to the French law for biomedical research and human experimentation, individual written consent was not required from the patients or their relatives for data collection. Each year, the referring local practitioner of the study coordinated with the doctors working in the nursing home. At the beginning of the surveillance period, information regarding participation in the study was displayed in the family vising area, including a document about their right to access and rectify personal data. After collection, data were rendered anonymous. No specific authorization was needed to retrospectively analyze anonymous data collected during routine care in the context of routine surveillance.
A total of 137 outbreaks were recorded in the 14 sites. RTI outbreaks were more frequent than GE outbreaks (76 outbreaks and 5862 exposed residents vs. 61 outbreaks and 4309 exposed residents, respectively). Overall, 7643 of the exposed residents were women and 2528 were men. The median age was 86.7 years old (interquartile range: 81.1-91.0 years).

Virus investigations (respectively 389 samples for RTI and 143 for GE with all the detailed results in S1-S4 Tables) confirmed a considerable number of norovirus-related GE outbreaks (34/61) and influenza-related RTI outbreaks (46/76). For GE outbreaks, 2524 residents were in a NoV+ context versus 1785 in a NoV-context, and for RTI outbreaks, 3479 residents were in a Flu+ context versus 2383 in a Flu-context.
For GE surveillance in the NoV+ context, there were 1093 EI residents versus 1431 ENI residents, whereas in the NoV-context, there were 583 EI residents versus 1202 ENI residents. Therefore, the infection rate was higher in the NoV+ context (43.3%) than in the NoV-context (32.7%, (odds ratio (OR): 0.63, 95% confidence interval (CI): 0.56-0.72), p < 0.001).

For RTI surveillance, the rates of infection were similar with and without confirmed influenza: 31.5% (N = 1095 EI residents /3479 exposed residents) vs. 30.5% (N = 728 EI residents/ 2383 exposed residents, OR: 0.96, CI: 0.85-1.07, p = 0.47). Moreover, infection rate in the NoV + context was higher than the three other contexts: NoV-(OR: 0.63, CI: 0.56-0.72), Flu+ (OR: 0.60, CI:0.54-0.67) and Flu-(OR: 0.58, CI: 0.51-0.65).
In univariate analysis, certain individual characteristics were associated with significant variations in the infection rate (S5 Table) . The infection rate increased with age (except in the Flucontext) and, decreased with LOS during GE outbreaks. The covariate adjusted analysis revealed specific significant effect modification according to sex (NoV+) and LOS (NoV-) (S6 Table) . In analyses stratified according to virus and sex, age adjusted for LOS remained significant for Flu+ and NoV+ outbreaks (males). In NoV-context, the effect modification of LOS remained significant (Table 1) . Finally, when autonomy was included and adjusted for age (virus, sex, LOS stratification), the less autonomous residents (female/LOS<4 years/age<86/ GIR 1-2) were affected more severely by Flu+ outbreaks with specific effect modification according to age (S7 Table) . The study of lethality rates in infected residents over the 56 days after onset indicated that there were significant variations for RTI
but no change for GE (Table 2 ). Significant differences appeared after 28 days in the context of Flu+ outbreaks and other RTI outbreaks.
The analysis of successive or simultaneous clusters in the same institutions was performed when the time period between the onset of symptoms of the last case in outbreak N and the onset of symptoms of the first case in outbreak N+1 was �56 days (S8 Table) . 44 of the 137 outbreaks (32.12%) were identified, and 194 of the 3499 exposed and infected residents contracted multiple infections. The percentage of exposed and infected residents implicated in more than one virus stratification was 11.09% ((194 � 2)/3499). Moreover, two deceased residents were included in the NoV-Na and Flu lethality analyses because death occurred within 56 days for both infections. The analysis of virus stratification of the 44 outbreaks showed the absence of successive clusters for the same category. The same analysis for the first four 7-day intervals (Days 1 to 28) showed the respective values: 26 outbreaks (18.98%), 117 residents ((6.69% ((117 � 2)/3499)), one dead resident.
Finally, according to the higher lethality impact during the first four 7-day intervals and to limit the impact of successive clusters in the same site, all cause lethality rates were studied according to individual parameters for the four 7 days intervals with the respective number of According to the surveillance type (GE or RTI), the lethality rates differed significantly: 1.6% versus 3.4% (respectively NoV+ and NoV-contexts, OR: 2.24, CI: 1.16-4.39, p = 0.02) and 8.3% versus 5.6% (respectively Flu+ and Flu-, OR: 0.67, CI: 0.45-0.97, p = 0.04).
In univariate analysis (S9 Table) , low autonomy status in the NoV+, Flu+ and Flu-contexts was most significantly associated with increased all-cause lethality, and age was associated with higher lethality in the Flu+ context. In the adjusted analysis, no significant statistical differences were identified in GE outbreaks. For RTI episodes, the adjusted analysis showed that autonomy had a significant impact when adjusted for sex, age or LOS (Flu+ and Flu-NA) and that age had a significant impact when adjusted for sex, autonomy or LOS (Flu+) (S10 Table) .

In Table 3 , the specific effects of age or autonomy were tested. Significant OR age adjusted for autonomy were: Flu+/age �86 years (compared with the <86 group), 1.97 (1.19-3.25). OR autonomy adjusted for age were for GIR 3-6 (compared with GIR 1-2): Flu+, 0.41 (0.24-0.69); Flu-, 0.42 (0.20, 0.90).
Finally, despite the low number of residents and deaths per category, and consequently the limited robustness of the results, autonomy adjusted for age with stratification according to virus, sex and LOS showed that the effects were higher among subgroups of less autonomous residents (female or male/LOS<4 years/GIR 1-2) in Flu+ outbreaks, and there was also higher mortality in the small subgroup of autonomous men with LOS � 4 years (higher mortality) (S11 Table) .

In the Flu+ context, data regarding vaccination status and oseltamivir prescriptions were available but not used in this study.

In the present study, surveillance data obtained during GE and RTI outbreaks in nursing homes were used to construct stratified analyses and to identify specific infection and all-cause lethality rates according to the residents' individual characteristics.
The infection rates observed here were similar to those found in previous studies of NoV and Influenza outbreaks (odds of being infected during a Flu+ outbreak were around 40% less than during a NoV+ outbreak). Reported infection rates were close to 30.0% in influenza outbreaks and 40.0% in NoV outbreaks [18] [19] [20] .
Older age appeared to increase the likelihood of GE and influenza infection, with increasing rates among older residents. Age is a well-known factor for influenza and norovirus severity in the elderly and in nursing homes [21, 22] . For NoV, the highest incidence estimates (5-year age strata) was found in the �85 year-category (approximately 800 men and for 1,400 women per 100,000 inhabitants). In our study, univariate analysis (NoV+) showed that the odds of being infected were 1.5 to 1.6 times higher if a resident was older than 85. Moreover, an adjusted analysis of GE outbreaks highlighted different effects among subgroups of residents according to sex and LOS. Indeed, multiple and/or repeated exposure to GE viruses while institutionalized may lead to susceptibility or possible increased immunity in some residents [23] . For the sex variable, two factors could explain the effect: a possible selection bias with men reporting mild infections less than women (particularly in the <86
years subgroup) or that male susceptibility was different (age, LOS, immunity,. . .). A German study from 2013 also reported a greater impact in women [21] . Moreover, when age analysis was stratified by sex and LOS, no Epidemic impacts in nursing homes significant impact was observed in women; the only significant differences were fewer infections in men in the <86-subgroup (except in the NoV-with LOS <4 years).
For the RTI outbreaks, sex and LOS variables did not have a significant effect. In residents older than 86, the odds of being infected in Flu+ context were 1.5 times higher for women and 1.7 for men. In univariate analysis, contrary to the other virus contexts where odds ratios were rarely above 2, residents over 95 years old had increased odds of infection of � 2.8 compared with the 70-year-old category, and for the 100 year-old group the odds were approximately 3.8. In the Flu+ context, autonomy adjusted for age (virus, sex and LOS stratification) revealed a possible increase in infection rates among less autonomous residents. A previous study found that when elderly residents were exposed to the A(H3N2) virus, there were higher rates of infection and reinfection, and more significant effects on the institution than with other influenza types/subtypes. In the community, the relative illness ratio (RIR) in the [22, 24] .
The incidence of influenza infection and the associated risks were well described by age group, but the specific impact according to age was not studied. The results of this work highlighted the specific age distribution of influenza illnesses among the nursing home residents and the more significant impact among the older residents. This specific susceptibility could be a critical factor in the institutional exposure and dissemination of influenza and could partly explain the high infection impact in the elderly institutional population.
In this work, autonomy status was not the main factor associated with infection (no significant impact in GE and in Flu-contexts). However, in Flu+ outbreaks, a high level of dependency was associated with a higher risk of falling ill. This observation implies that staff could play a role in the spread of infection (highly dependent and less mobile residents are less likely to contaminate themselves) or that the more active residents may be less fragile and/or have a greater involvement in the recommended infection control measures. Finally, improving compliance with personal hygiene measures both for nursing staff and residents might be expected to have a beneficial effect on infection rates. Previous studies identified higher NoV infection rates in highly dependent individuals, but the results were not adjusted for age and LOS to take into account the potential correlation with the autonomy status [20] .
Lethality is difficult to assess in nursing homes because death is frequent. Our GE and RTI episodes occurred during the winter seasons, and there are possible interactions between outbreaks and increased mortality at this time of the year [25] . The all-cause lethality rate of the infected residents in our study reflected global mortality including GE and RTI outbreaks and the global epidemiological context. Not surprisingly, a higher all-cause lethality rate was observed in the influenza contexts, as reported in previous studies [25] [26] [27] . Age and autonomy had similar effects in the different contexts, but in GE and to a lesser degree in Flu-outbreaks, the relatively small number of deaths could have limited the power of the statistical tests. In nursing homes, residents are generally discharged due to death. The number of residents lost to follow up was low (0.14% in the first 28 days), so the 7-day interval turnover rate calculated on the base of the median LOS provides a
good indication of the average case fatality rate. The lethality rate for NoV+ outbreaks was similar to the estimated 7-day interval turnover rate (1.6%) indicating that this context had a limited impact on the death rate.
The all-cause lethality rate was most affected by age and autonomy. Both individual characteristics were significant in the Flu+ outbreaks, and autonomy adjusted for age was significant in the Flu-episodes. The influence of age on mortality in a context of influenza has already been described: a very high mortality rate (831/100,000 inhabitants) was reported in persons 90 years of age and older compared with those aged 65-69 years (23/100,000 inhabitants) [28] . In our univariate analysis, the higher risk was observed in the �90 group whose risk of death was at least 2.6 higher than the <70 group. When adjusted for autonomy, the impact of age was not significant in more autonomous residents in the Flu+ context and not at all in the Flucontext. The opposite analysis (autonomy adjusted for age) showed higher global impact in the less autonomous group (Flu-) or only in the �86 age group (Flu+). Age and autonomy are a reflection of resident's level of frailty. Clinical frailty scores were
not used in this study, but in a previous study of patients with critical illness, they were associated with greater mortality, regardless of age [29] . This suggests that in addition to age, autonomy can be a valuable indicator for the assessment of outbreak impact in outbreak surveillance. Other studies have suggested that age and certain comorbidities are independent risk factors for the influenza mortality rate or that mortality increase according to the number of risk factors [28, 30] . Comorbidities and underlying diseases of various severities could reflect overall frailty and consequently the risk of death. In nursing homes, information about autonomy and age are easier to collect and interpret than data on comorbidities. These various approaches should be evaluated and compared in the goal of optimizing risk assessment among nursing home residents.
The present work has two main limitations. First, the virus information was incomplete (limited identification, mainly influenza rapid tests for the RTI). Consequently, some episodes in the levels with no available identification may also have been associated with influenza or norovirus, and multiple contaminations could have been underestimated or not taken into account. Moreover, vaccination and oseltamivir prescriptions were recorded but not included because the influenza genotype was not determined and identification was limited.

Secondly, the deaths of uninfected residents were not recorded in this protocol even though such data would have provided valuable information about the global epidemiological context.
In conclusion, specific susceptibility patterns were observed among exposed residents. In this cohort of nursing homes, infection rates varied according to virus, sex, length of stay and age, and there were major differences in lethality depending on virus, age and autonomy score. The collected data were easy to record and could be used to improve the characterization of seasonal outbreaks in nursing homes, whose residents are particularly vulnerable. Finally, as the average age and dependency level of residents continues to increase, subsequently increasing the risk of infection and death, health care staff will have to be increasingly vigilant during seasonal outbreaks and targeted interventions should be implemented.

Supporting information S1 Data. (CSV) S1 R Codes. (R) S1 Table. (XLSX) S10 Table. (XLSX) S11 Table. (XLSX)
Critical care response to a hospital outbreak of the 2019-nCoV infection in Shenzhen, China

https://doi.org/10.1186/s13054-020-2786-x

SHA: 6a93283b499ae5bc6aaf29f14e701dc8f25138ea

Authors: Liu, Yong; Li, Jinxiu; Feng, Yongwen
Date: 2020
DOI: 10.1186/s13054-020-2786-x
License: cc-by

Abstract: nan
Text: The main challenge may include (1) early identification of outbreak, (2) rapid expansion of patients, (3) high risk of nosocomial transmission, (4) unpredictability of size impacted, and (5) lack of backup resource. These challenges have caused severe shortage of healthcare workers, medical materials, and beds with isolation. The Spring Festival holiday has greatly aggravated the shortage of human resources and heavy traffic flow due to the vacation of healthy workers and factory workers, which further magnified the risk of transmission. The key point is to discriminate the infectious disease outbreak from regular clustering cases of flu-like diseases at early stage. There is a trade-off between false alarm causing population panic and delayed identification leading to social crisis.
Early identification of 2019-nCoV infection presents a major challenge for the frontline clinicians. Its clinical symptoms largely overlap with those of common acute respiratory illnesses, including fever (98%), cough (76%), and diarrhea (3%), often more severe in older adults with pre-existing chronic comorbidities [1] . Usually, the laboratory abnormalities include lymphocytopenia and hypoxemia [1] . The initial chest radiographs may vary from minimal abnormality to bilateral ground-glass opacity or subsegmental areas of consolidation [1] . In addition, asymptomatic cases and lack of diagnosis kits result in delayed or even missed diagnosis inevitable and makes many other patients, visitors, and healthcare workers exposed to the 2019-nCoV infection.
Critical care response to the outbreak of coronavirus should happen not only at the level of hospital, but also at the level of the city which is dominated by the government. At the early stage, the size of the patients' population is not beyond the capability of local infectious diseases hospital (IDH). The general hospital is responsible for fever triage, identifying suspected cases, and transferring to the local IDH. Such a plan is mandatory for every hospital. Shenzhen city has established a preexisting Infectious Disease Epidemic Plan (IDEP), which has facilitated managing and containing local outbreak of the 2019-nCoV. In case the patient load exceeds the hospital capability of the IDH, new IDHs should be considered either by building a temporary new IDH or reconstructing an existing hospital. Wuhan, the epicenter of the outbreak, is racing against time to build two specialized hospitals for nCoV patients, namely Huoshenshan and Leishenshan hospital, whereas a different strategy
has been undertaken in Shenzhen city by reconstructing an existing hospital to become an IDH with capability of 800 beds.
2019-nCoV patients should be admitted to singlebedded, negative pressure rooms in isolated units with intensive care and monitoring [2] . Clinical engineering should have plans to reconstruct standard rooms [2] . Retrofitting the rooms with externally exhausted HEPA filters may be an expedient solution. Also, the general hospital may consider procedures such as suspending elective surgeries, canceling ambulatory clinics and outpatient diagnostic procedures, transferring patients to other institutions, and restricting hospital visitors [2] . More importantly, because the hospitals' ability to respond to the outbreak largely depends on their available ICU beds, the plan to increase ICU bed capacity needs to be determined.
Caring for 2019-nCoV patients represents a substantial exposure risk for ICU staff because of the following reasons: highly contagious with multiple transmission route, high exposure dose, long daily contact hours, and ICU stay. The basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9) [3] , or as high as between 3.6 and 4.0 [4] . The 2019-nCoV is proved to be transmitted by respiratory droplets, contact, and fecal-oral, even transmission through the eye is possible [5, 6] . The higher viral load and aerosol-generating procedures, such as noninvasive ventilation, magnify the exposure and transmission risk [2, 7, 8] . Moreover, virus shedding can be prolonged and last for > 3 weeks according to some literature and our unpublished data [2] . Healthcare providers and those in contact with infected patients should utilize contact, droplet, and airborne precautions with N95 respirator. Strict infection prevention and control practices have been implemented and audited in
our units following the infection prevention and control plan published by China's National Health Committee (CNHC). In addition, wellequipped fever clinic as triage station with trained staff knowing 2019-nCoV case definitions is established. For suspected 2019-nCoV infection, several key points are crucial procedures: recording a detailed history, standardizing pneumonia workup, obtaining lower respiratory tract specimens [2, 8] , and implementing droplet isolation to break the transmission chain in the healthcare setting [2] .