Abstract
Right from the start of the COVID pandemic in January 2020, the entire tourism sector was put under immense pressure because of its assumed role in SARS-CoV-2 transmission and infection dynamics. Based on reports of single superspreading events in the early days of the pandemic, the hotel industry appeared in a bad light that impaired a strategic risk-assessment of existing transmission risks between tourists and employees.
We prospectively analysed samples of 679 employees of 21 hotels and restaurants from July 2020 to December 2020, a time during which more than 1.5 million tourists visited the Lübeck/Ostholstein Baltic Sea vacation area in Northern Germany. Employees were tested up to three times for an acute SARS-CoV-2 infection (PCR from nasopharyngeal swabs) and the presence of SARS-CoV-2 specific antibodies, and were asked to complete a short questionnaire.
Despite the massive increase in tourist influx, no significant increase in SARS-CoV-2 cases was observed amongst employees of the tourism sector from July to September 2020. In a cluster-outbreak analysis of 104 study participants of one single hotel in the Lübeck/Ostholstein region in October 2020 being employed in the low-wage sector “housekeeping” could be determined as major risk factor for becoming infected.
In conclusion, in a low incidence setting, touristic activities are safe under COVID-related hygiene measures for both the local population and employees of the tourism sector. Whereas, the field of work is a potential risk factor for increased infection dynamics.
Keywords: SARS-CoV-2, Infection risk, Hospitality employees
SARS-CoV-2, Infection risk, Hospitality employees
1. Introduction
With the emergence of the COVID-19 pandemic in January 2020, the whole tourism sector was engulfed in the abyss through global lockdown measures and travel restrictions. In particular, single events like the outbreak in Ischgl/Austria in March 2020 with a high number of infected persons from different countries [1] attracted enormous public attention and emerged as a tremendous burden in later discussions about how tourism contributes to high SARS-CoV-2 infection incidences across countries.
While the impact of increased mobility and returning travelers from high SARS-CoV-2 incidence countries on the local and nationwide incidences became obvious in numerous studies [2], the risk of tourist activities in settings with a high standard of infection control and hygiene measures as implemented under COVID conditions has not been investigated in detail. This is even more striking as the economic burden of the complete lockdown in tourism lasting for months in most European countries is immense.
The United Nations World Tourism Organization (UNWTO) already published an action plan dealing with the COVID-19 crisis and proposing strategies on “managing the crisis and mitigating the impact”, “providing stimulus and accelerating recovery” and “preparing for tomorrow” in April 2020 [3]. However, concrete exit strategies were either not implemented or differed significantly between countries and lacked evidence-based data [4]. Efforts undertaken by our group to generate such data basis to develop a risk assessment were hampered by different actors of the tourism sector probably due to economic concerns in case of employees tested positive.
Employees in the tourism sector originate from and interact with a large number of people from different countries and regions and are thus suspected to have an increased risk of SARS-CoV-2 infections during the pandemic. Hence, resident's discrimination increased during the COVID-19 pandemic for fear of an increased infection risk [5]. Surprisingly, even though tourism has a significant impact on countries' income, still little is known about the potential risk for employees and residents that derive from tourist activities during the pandemic.
In mid-May 2020, tourism and gastronomy in Schleswig-Holstein in Northern Germany were reopened under defined COVID-conditions. These conditions included hygiene and distance rules including regular hand disinfection and wearing face masks, as well as the implementation of signpost systems in tourist areas to avoid high contact densities and the recording of address data for potential contact tracing [6].
The aim of the underlying survey was to investigate if tourist activities during the peak season would lead to increased frequency of COVID-19 outbreaks among employees in the tourism sector. Here we will present one outbreak scenario. We were able to analyze a single hotel in detail to show possible influencing factors for the spread among these employees. We therefore repeatedly tested employees of the tourism sector in a highly frequented tourist region at the Lübeck/Ostholstein Baltic Sea in Northern Germany during the summer season 2020 for acute SARS-CoV-2 infection and the presence of SARS-CoV-2-specific antibodies. In that region, 7-day incidences on a population level ranged from below 30/100.000 population until September 2020 to a sharp increase in SARS-CoV-2 infections with up to 150/100.000 population after fall break in October 2020. Although we could not observe an increase of SARS-CoV-2 infections over time among the employees that would have exceeded the incidence in the resident population, potential risk factors for cluster outbreaks in this particular employee group could be identified by evaluation of one event in a single hotel with >100 employees, with a total of 38 SARS-CoV-2 positive employees.
2. Methods
2.1. Study design and ethics
During the period from July 2020 to December 2020 a total of 679 employees of 21 hotels and restaurants were recruited for a prospective cohort study. Participants were tested up to three times for an acute SARS-CoV-2 infection and the presence of SARS-CoV-2 specific antibodies. All study participants provided written informed consent. The study was approved by the local ethics committee (University of Lübeck, Az 20–150).
2.2. Specimen collection
A nasopharyngeal swab (CITOSWAB®) for detection of an acute SARS-CoV-2 infection was taken by trained personnel. The swab was first inserted into the nose up to the nasopharynx and pulled out again with rotating movements followed by a smear of the posterior pharyngeal wall orally using the same swab without touching the tongue. At the posterior pharyngeal wall, the smear was swabbed and then withdrawn. The smear was then placed in a swab tube without buffer solution and immediately sent to the laboratory at room temperature for further processing.
In order to identify previous infections with SARS-CoV-2, SARS-CoV-2 specific antibodies were determined in the blood of study participants. Capillary blood was collected from the fingertip with MiniCollect® tubes by using a lancet and a pipette. Afterwards, tubes were centrifuged and we collected at least 45 μL serum. Serum was stored at 4 °C for later antibody testing.
During sampling, strict compliance with recommended hygiene regulations was ensured. All persons taking samples were equipped with face shields, FFP2 masks, disposable gloves and protective gowns. The disposable gloves were changed after each contact with a study participant.
2.3. SARS-CoV-2 testing
Dry swabs for SARS-CoV-2 mRNA quantification were transferred to Vacuette® 2 mL Virus Stabilization Tubes (Fa. Greiner Bio-One GmbH/Germany). The isolation of the virus RNA by magnetic bead technology and the real time PCR testing of the E- and the ORF1-gene region was performed using the Cobas® SARS-CoV-2 test on Cobas® 6800 and 8800 systems (Roche Diagnostics/US) by a DIN EN ISO 15189 accredited laboratory (Fa. LADR Central Lab Dr. Kramer & Colleagues/Germany). For antibody testing we performed the Anti-SARS-CoV-2-NCP-ELISA (IgG) and Anti-SARS-CoV-2 -ELISA (IgG) according to the manufacturer instructions (EUROIMMUN AG, Lübeck, Germany) by determining the modified nucleocapsid protein and the S1-domain of the spike protein respectively. Samples above the cut-off of 1.1 were defined as IgG-positive. Values between 0.8 and 1.1 were considered borderline according to the manufacturer.
2.4. Questionnaire and definitions
Study participants were asked to complete a short questionnaire including personal details (name, date of birth, field of work). Additional data was obtained on the type of accommodation (e.g. living with colleagues). We exclusively used a questionnaire in German. In cases of insufficient participants' language skills translation into English language was performed by the study personnel or co-workers into the particular native language. No participant had to be excluded due to language abilities.
2.5. Statistics
For the assembly of data charts and figures, Microsoft Excel® (2016) was used. Besides the descriptive charts, the obtained data were tested for significant differences. Fisher's exact test was used for pairwise comparison. Adjusting for multiple comparisons was done by Holm. We performed a binary logistic regression model to prove how variables “age”, “sex”, “field of work”, “living in residential cohorts”, “having children below 14 years” contribute to explain the outcome (dependent) variable “infection with SARS-CoV-2” [19]. As a complementary analysis, we run the same model also with exchanging “living in residential cohorts” by the variable “household members”. Those two variables were not computed together, as they are not independent from each other. In a first round we performed analysis based on integer dummy coding of independent variables. Ranking of the dummy coding was made on the basis of relative infection rate per factor level within a variable. Subsequently, we ran the binary logistic regression with changing the overall significant independent variable to the original factors to identify the respective factor levels significantly contributing to the model. We used the factor with the lowest relative infection rate as reference in this model. In all statistical analyses p-values below p ≤ 0.05 were considered significant.
3. Results
3.1. SARS-CoV-2 incidences in Schleswig-Holstein
In the summer of 2020, average incidences of SARS-CoV-2 infections in Schleswig-Holstein, and specifically in the vacation area of Lübeck/Ostholstein on the Baltic Sea, were lower than the national average in Germany (Figure 1). After relaxation of travel restrictions, a strong increase in guest arrivals in Schleswig-Holstein was observed [7]. Initially, the number of tourists remained below those of 2018/19, but from August to October 2020 the number of guest arrivals were comparable to the pre-COVID era (Suppl. Figure 1). Despite the massive increase of tourists during the summer period in the Lübeck/Ostholstein region, no significant increase of SARS-CoV-2 incidences was observed.
Figure 1.
SARS-CoV-2 incidences in Schleswig-Holstein, specifically in the City of Lübeck and the district of Ostholstein, including lockdowns, dates of testing. Data modified from Robert Koch-Institute COVID-19 Dashboard (https://experience.arcgis.com/experience/478220a4c454480e823b17327b2bf1d4; 21.06.2021).
3.2. Study cohort of employees in the tourism sector
The overall study group included 679 participants. 61.9% (n = 420) were female with a mean age of 39.2 years. Participants households consisted of 2.4 persons on average. In total, 19.0% of the participants reported living with children younger than 14 years and 16.5% lived together with at least one colleague from work (Table 1).
Table 1.
Demographic data of the overall study group and the outbreak group separated by infected and non-infected participants (residential cohorts = 3 or more colleagues living together).
| Study group | Outbreak group |
||
|---|---|---|---|
| Infected | Non-infected | ||
| n (%) | 679 (100) | 38 (100) | 66 (100) |
| Gender, female; n (%) | 420 (61.9) | 21 (55.2) | 40 (60.6) |
| Age (mean ± SE) | 39.2 (±14.2) | 41.7 (±14.3) | 40.6 (±14.2) |
| Household members (average number) | 2.4 | 2.7 | 2.2 |
| Children <14 years; n (%) of households | 129 (19.0) | 2 (5.3) | 13 (19.7) |
| Living in residential cohorts; n (%) | Not available | 21 (55.3) | 21 (31.8) |
| Working in housekeeping; n (%) | 116 (17.1) | 12 (11.5) | 11 (10.6) |
Participants' nationality is shown in Figure 2A. Most participants mentioned “service” (25.9%) as their field of work followed by “housekeeping” (17.1%), “kitchen/scullery” (16.1%) and “reception” (13.0%) (Figure 2B).
Figure 2.
Countries of origin classified by continents (A; ∗ Europe except Poland and Germany) and field of work (B) of the overall study cohort (n, total numbers; %).
3.3. SARS-CoV-2 testing for acute and previous infections
By the end of September 2020, 443 participants were included in the study and tested at least once. Until then, there was no evidence of acute SARS-CoV-2 infections in the study group and only a few positive antibody tests (n = 2 positive, n = 2 borderline) were detected, indicating a previous infection. Since the positive antibody tests were neither temporally related to each other nor could a local clustering be observed, we did not assume an undetected SARS-CoV-2 outbreak in the study group by then.
3.4. Characterization of the cluster outbreak
In October 2020, we observed a SARS-CoV-2 outbreak among employees that were part of our study cohort in a large hotel in the district of Ostholstein. Since overall infection rates in the local population were still low and no further acute infections were detected among other participants of our study cohort, this gave us the opportunity to determine risk factors for cluster outbreaks in this particular setting. The outbreak group included a total of 104 study participants (Table 1), who were all employed by one single hotel and that were tested after the 18th of October 2020. Within this group, a total of 38 participants were tested SARS-CoV-2 positive by PCR and/or antibody testing (“infected”) within subsequent tests. The remaining 66 participants were tested negative for the presence of SARS-CoV-2 infection and served as the control group to identify risk factors for SARS-CoV-2 transmissions.
Within the group of infected study participants, 55.2% were female, the mean age was 41.7 years (Table 1). More than half of the infected individuals were from either Germany (36.8%) or Poland (31.6%; Figure 3A). The most frequently mentioned fields of work were "housekeeping" (31.6%), "kitchen/scullery" (21.1%) and "service" (21.1%) (Figure 3B). One participant tested positive stated travelling to Lower Saxony in a period of two weeks prior testing, all other participants negated such activities.
Figure 3.
Countries of origin classified by continents (A; ∗ Europe except Poland and Germany) and field of work (B) of the SARS-CoV-2 positive employees of the outbreak group (n, total numbers; %).
Particular attention was paid to the housing conditions of employees in order to identify possible risk factors in a cluster outbreak. Based on postal addresses that were provided by the study participants, employees sharing a household could be identified. Cases of three or more colleagues living together were considered as residential cohort.
Although more than half of the infected participants lived in such residential cohorts (n = 21, 55.3%), our data indicated that living in a residential cohort was not a risk factor for the cluster outbreak (binary logistic regression model, p = 0.6835). Whereas, field of work was a risk for the cluster outbreak (binary logistic regression model, p = 0.0095, Suppl. Table 1).
We further investigated which of the study participants' field of work was a risk factor for SARS-CoV-2 transmission, based on the assumption that many employees in the hotel and catering industry work together in sometimes confined spaces and that maintaining distance as a protective measure against SARS-CoV-2 transmission is more difficult than in other professions. While comparing the different working conditions of the infected participants using “Management” as a reference level in a factor-based binary logistic regression, we could show that “housekeeping” is a risk factor in comparison (p < 0.05; Figure 4).
Figure 4.
Comparison of different field of work in SARS-CoV-2 infected and non-infected employees of the outbreak group. Housekeeping as significant factor level of the binary logistic regression compared to management as reference with the lowest number of positivity.
104 employees of this particular hotel were part of the outbreak group. At the first testing date in September 2020 we tested 48 persons. None of these were tested positive via PCR or had positive antibody results back then. In October 2020 another test round was initiated because an outbreak was supposed amongst the employees on the basis of positive antigen-tests. In total, 89 employees of the hotel were tested. 33 showed positive PCR-tests, four of these had also a positive antibody test. Three participants had positive antibodies but negative PCR in this study. However, these participants had already been tested positive by PCR by the responsible health authority four days before they were included in our study. For this reason, they were considered part of the outbreak group. Two other participants from the hotel were also tested positive for SARS-CoV-2 by the responsible health authorities, but did not show up for testing in October because of the mandated isolation. Since they were tested negative for antibodies in September but positive in December, they were also included in the outbreak group.
Due to the second nationwide lockdown starting in early November 2020 (Figure 1) and the associated hotel and restaurant closures only 48 participants (46.2%) of the outbreak group participated at another testing point in December 2020, seven weeks after the cluster outbreak. SARS-CoV-2 specific antibodies were detected in all samples of initially infected participants who were retested in December 2020 (n = 14) but no antibodies could be detected in samples of the non-infected outbreak group at this point (n = 34).
4. Discussion
More than 2.5 years after the start of the COVID-19 pandemic, detailed information on the infection risks of tourist activities driving the pandemic is still missing. While the relevance of enhanced mobility and cross-border tourism between countries with highly different incidences became quite early obvious in the pandemic [8, 9], the individual infection risk for employees in the tourism sector and its potential role in SARS-CoV-2 spread and transmission have not been assessed in detail.
Global tourism has been exposed to a number of crises before the COVID-19 pandemic. After the terror attack in the US on September 11th, 2001 there was a massive decline in worldwide travel [10], similarly during the SARS-CoV-1 pandemic in 2003 [11]. However, no crisis in the past has led to such a long-term decline of global tourism as COVID-19 has done [3, 12]. The Baltic Sea in Northern Germany is one of the country's major summer destinations, attracting several million national and international tourists every year. In 2019, over 8.9 million tourists visited Schleswig-Holstein. The number of overnight stays by tourists in 2019 was about 35,975,000 [7]. Its relevance for the local economy is immense, with a total of 4.7 billion euros adding value that is attributed directly or indirectly to the tourism sector per year, and with a share of 5.9 percent of the gross domestic product in 2019 [13]. Due to lockdown measures during the pandemic, the number of guest arrivals decreased to 6,217,000 (−30.3%) and the number of overnight stays to 28,925,000 (−19.6%) [7].
Because of the high awareness and concern that tourism might significantly contribute to infection dynamics in a so far non-vaccinated population in 2020, we investigated the impact of mass tourism on infection rates among employees of the tourism sector from July to December 2020 at the Baltic Sea in Northern Germany. We observed that under general restrictions such as wearing face masks, keeping distance to others and additional indoor hygiene rules, no increase in infection rates among employees and residents was observed over time when the background incidences were low. This is in line with a large population-based study on more than 3000 residents of the Lübeck city region, showing that enhanced mobility and tourism did not increase the numbers of SARS-CoV-2 positive cases over the same time-period [14].
Furthermore, it can be assumed that there was no undetected outbreak within the study group, as there were only isolated positive antibody findings outside the outbreak group. Since this was a completely unvaccinated group, positive antibody findings indicate contact with the virus.
Although no enhanced infection dynamics were observed among tourists, residents and employees of the tourism sector over a period of more than three months (July–September 2020), the first appearance of an acutely infected employee in a single hotel in October 2020 dramatically changed infection rates among the employees of this hotel. Thus, we identified the working condition (housekeeping) as the main risk factor for SARS-CoV-2 transmission leading to a specific cluster outbreak. This factor characterizes in particular a group of people with low socioeconomic status, which has been shown as one of the general risk factors for enhanced SARS-CoV-2 transmission across larger studies [15]. This might be supported by the fact that within the SARS-CoV-2 positive employees, only one person indicated “management” as field of work, although the overall number in the complete outbreak group is low (n = 12, 11.5%; Suppl. Figure 2). In general, this finding is not restricted to the tourism sector but has evolved as one of the predictors for pandemic hotspots in larger cities and for specific working conditions [16, 17].
The study has several limitations that have to be addressed, most of them owed to the special situation of a pandemic. For example, recruitment of study participants in the tourism sector after the 1st lockdown in Germany that lasted over seven weeks until mid-May 2020, was severely hampered by economic considerations of the hotel industry sector which resulted in refusals for employees to participate in this study. This is even more impactful as it is known that test acceptance in general differs in different population groups and that positive amplification sometimes is needed to convince people of the benefits of early SARS-CoV-2 detection [18]. In addition, retesting of participants included later in the course of investigation could not be performed because of new lockdown measures in the end of 2020.
Because of the limited PCR testing capacities in Germany in the summer of 2020, we exclusively focused on employees of the tourism sector but not on tourists or residents of the tourist regions, for whom we relied on the official public health surveillance data that was issued daily by the Robert-Koch-Institute/Berlin. No further contact tracing was done in this study, neither among tourists nor among the local population. On the one hand, corona warning apps were already available at the time of the outbreak. On the other hand, due to protective measures as keeping distance and wearing masks etc. contact tracing was not officially ordered. Afterwards, the local health department confirmed that there were no increased numbers of infections associated with the outbreak.
We hypothesized that testing of employees who are in direct contact with tourists at a defined tourist site would offer the opportunity to detect SARS-CoV-2 hotspots at an early stage and precede the reporting of positively tested tourists in their hometowns. From July to September 2020, infection dynamics in the tourist area Lübecker Bucht/Ostholstein did not differ from the general incidences in Germany, despite massive tourist activities. A detailed summary on how SARS-CoV-2 incidences developed during that time in Northern Germany is found in Klein et al., in which a population-based testing strategy was investigated [14].
However, our study highlights that the hygiene measures that have been introduced by the Federal State of Schleswig-Holstein and controlled by the local public health authorities were efficient in preventing SARS-CoV-2 transmissions. The large outbreak occurring in a single hotel in October 2020 overlapped with a much more profound effect on SARS-CoV-2 infection dynamics that was credited to travelers from high-incidence regions across Europe returning to Germany. As a consequence of the overall low SARS-CoV-2 incidences during the summer of 2020 with only a few hospitalized COVID-19 patients, social restrictions were almost completely suspended during that time, resulting in a fulminant incline in the SARS-CoV-2 incidences by fall 2020 and a complete 2nd lockdown of the local tourism sector that lasted until May 2021.
Declarations
Author contribution statement
Henrike Thiessen; Nadja Käding; Benjamin Gebel: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.
Max Borsche; Marc Ehlers; Johann Rahmöller; Stefan Taube; Jan Kramer: Contributed reagents, materials, analysis tools or data.
Simon Graspeuntner: Analyzed and interpreted the data.
Laura Kirchhoff: Performed the experiments.
Christine Klein; Alexander Katalinic: Conceived and designed the experiments.
Jan Rupp: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.
Funding statement
This work was supported by Federal Ministry of Education and Research (BMBF) within the B-FAST program (AP6 risk settings).
Professor Jan Rupp was supported by Ministry of Education, Science and Cultural Affairs of the Federal State of Schleswig-Holstein, Germany.
Data availability statement
Data associated with this study has been deposited at SSRN under the accession number ID 3949485.
Declaration of interest's statement
The authors declare no conflict of interest.
Additional information
No additional information is available for this paper.
Acknowledgements
We thank Thorsten Niemann for his excellent technical assistance in performing the SARS-CoV-2 antibody measurements.
Appendix A. Supplementary data
The following is the supplementary data related to this article:
References
- 1.Kreidl P., Schmid D., Maritschnik S., et al. Emergence of coronavirus disease 2019 (COVID-19) in Austria. Wien Klin. Wochenschr. 2020;132(21-22):645–652. doi: 10.1007/s00508-020-01723-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Chang S., Pierson E., Koh P.W., et al. Mobility network models of COVID-19 explain inequities and inform reopening. Nature. 2021;589(7840):82–87. doi: 10.1038/s41586-020-2923-3. [DOI] [PubMed] [Google Scholar]
- 3.UNWTO . World Tourism Organization; 2020. Supporting Jobs and Economies through Travel and Tourism – A Call for Action to Mitigate the Socio-Economic Impact of COVID-19 and Accelerate Recovery. [Google Scholar]
- 4.Kreiner N.C., Ram Y. National tourism strategies during the Covid-19 pandemic. Ann. Tourism Res. 2020:103076. doi: 10.1016/j.annals.2020.103076. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Tse S., Tung V.W.S. Residents' discrimination against tourists. Ann. Tourism Res. 2020:103060. doi: 10.1016/j.annals.2020.103060. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Landesverordnung zur Bekämpfung des Coronavirus SARS-CoV-2 (Corona-Bekämpfungsverordnung – Corona-BekämpfVO). Vom 05. Juni 2020. Gesetz und Verordnungsblatt für Schleswig-Holstein; 2020. p. 25. [Google Scholar]
- 7.Schleswig-Holstein SAfHu . 2020. Tourismus in Schleswig-Holstein 2019; p. 27. [Google Scholar]
- 8.Monod M., Blenkinsop A., Xi X., et al. Age groups that sustain resurging COVID-19 epidemics in the United States. Science. 2021;(6536):371. doi: 10.1126/science.abe8372. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Popa A., Genger J.W., Nicholson M.D., et al. Genomic epidemiology of superspreading events in Austria reveals mutational dynamics and transmission properties of SARS-CoV-2. Sci. Transl. Med. 2020;12(573) doi: 10.1126/scitranslmed.abe2555. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Goodrich J. September 11, 2001 attack on America: a record of the immediate impacts and reactions in the USA travel and tourism industry. Tourism Manag. 2002;23(6):573–580. [Google Scholar]
- 11.McKercher B., Chon K. The over-reaction to SARS and the collapse of asian tourism. Ann. Tourism Res. 2004;31(3):716–719. doi: 10.1016/j.annals.2003.11.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Gössling S., Scott D., Hall C.M. Pandemics, tourism and global change: a rapid assessment of COVID-19. J. Sustain. Tourism. 2021;29(1):1–20. [Google Scholar]
- 13.Schleswig-Holstein SuGf . 2020. Sparkassen-Tourismusbarometer Schleswig-Holstein. [Google Scholar]
- 14.Klein C, Borsche M, Balck A., et al. One-year surveillance of SARS-CoV-2 transmission of the ELISA cohort: A model for population-based monitoring of infection risk. Sci Adv. 2022;8(15) doi: 10.1126/sciadv.abm5016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Varela A.R., Florez L.J.H., Tamayo-Cabeza G., et al. Factors associated with SARS-CoV-2 infection in Bogota, Colombia: results from a large epidemiological surveillance study. Lancet Reg. Health Am. 2021:100048. doi: 10.1016/j.lana.2021.100048. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Whittle R.S., Diaz-Artiles A. An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City. BMC Med. 2020;18(1):271. doi: 10.1186/s12916-020-01731-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Ursachi C.S., Munteanu F.D., Cioca G. The safety of slaughterhouse workers during the pandemic crisis. Int. J. Environ. Res. Publ. Health. 2021;18(5):1–40. doi: 10.3390/ijerph18052633. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Green M.A., Garcia-Finana M., Barr B., et al. Evaluating social and spatial inequalities of large scale rapid lateral flow SARS-CoV-2 antigen testing in COVID-19 management: an observational study of Liverpool, UK (November 2020 to January 2021) Lancet Reg. Health Eur. 2021;6:100107. doi: 10.1016/j.lanepe.2021.100107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.R Core Team . R Foundation for Statistical Computing; Vienna, Austria: 2020. R: A Language and Environment for Statistical Computing.https://www.R-project.org/ URL. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data associated with this study has been deposited at SSRN under the accession number ID 3949485.