Association of epidemiological and clinical features with PCR cycle threshold values of SARS-CoV-2 infection: a cross-sectional study.

The cycle threshold (Ct) in quantitative real-time reverse-transcriptase polymerase chain reaction (qRT-PCR) is inversely correlated to the amount of viral nucleic acid or viral load and can be regarded as an indicator of infectivity. We examined the association of socio-demographic and clinical characteristics of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) polymerase chain reaction (PCR) positive cases with PCR cycle threshold (Ct) values at the time of diagnosis. SARS-CoV-2 cases reported between 12 October 2020 and 24 January 2021 in Regensburg were analyzed employing bivariate and multivariable methods. We included 3,029 SARS-CoV-2 cases (31% asymptomatic at diagnosis) and analyzed the association of case characteristics with Ct values in 2,606 cases. Among symptomatic patients, cough (38.0%), rhinitis (32.4%), headache (32.0), and fever/chills (29.9%) were the most frequent complaints. Ct values ≤20 were more frequent in symptomatic cases (20.9% vs. 11.3%), whereas Ct values >30 were more common in asymptomatic cases (32.6% vs. 18.0%). Ct values >20 and ≤30 were most common in symptomatic and asymptomatic cases (48.0% vs 40.7%). We observed lower median Ct values of E and N gene in symptomatic cases. In a random forest model, the total number of symptoms, respiratory symptoms, and age were most strongly associated with low Ct values. In conclusion, certain symptoms and age were associated with lower Ct values. Ct values can be used as a pragmatic approach in estimating infectivity at the first notification of a case and, thus, in guiding containment measures.

COVID-19-related deaths: a 2-year inter-wave comparison of mortality data from Germany.

PURPOSE: The severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) has caused substantial mortality worldwide. We investigated clinical and demographic features of COVID-19-related deaths that occurred between March 2020 and January 2022 in Regensburg, Germany. METHODS: We compared data across four consecutive time periods: March 2020 to September 2020 (period 1), October 2020 to February 2021 (period 2), March 2021 to August 2021 (period 3), and September 2021 to January 2022 (period 4). RESULTS: Overall, 405 deaths in relation to COVID-19 were reported. The raw case fatality ratio (CFR) was 0.92. In periods 1 to 4, the CFRs were 1.70%, 2.67%, 1.06%, and 0.36%. The age-specific CFR and mortality were highest in persons aged ≥ 80 years in period 2 while mortality in younger cases increased with time. The median age at death was 84 years and it varied slightly across periods. Around 50% of cases of death were previously hospitalized. In all time periods, the cause of death was mostly attributed to COVID-19. Over the four periods, we did not find significant changes in the distribution of sex and risk factors for severe disease. The most frequent risk factor was cardio-circulatory disease. CONCLUSION: In conclusion, the CFR decreased over time, most prominently for period 4. Mortality was considerable and younger cases were increasingly at risk.

Authors' response: Occupation and SARS-CoV-2 infection risk among workers during the first pandemic wave in Germany: potential for bias.

We thank van Tongeren et al for responding to our study on occupational disparities in SARS-CoV-2 infection risks during the first pandemic wave in Germany (1). The authors address the potential for bias resulting from differential testing between occupational groups and propose an alternative analytical strategy for dealing with selective testing. In the following, we want to discuss two aspects of this issue, namely (i) the extent and reasons of differential testing in our cohort and (ii) the advantages and disadvantages of different analytical approaches to study risk factors for SARS-CoV-2 infection. Our study relied on nationwide prospective cohort data including more than 100 000 workers in order to compare the incidence of infections between different occupations and occupational status positions. We found elevated infection risks in personal services and business administration, in essential occupations (including health care) and among people in higher occupational status positions (ie, managers and highly skilled workers) during the first pandemic wave in Germany (2). Van Tongeren's et al main concern is that the correlations found could be affected by a systematic bias because people in healthcare professions get tested more often than employees in other professions. A second argument is that better-off people could be more likely to use testing as they are less affected by direct costs (prices for testing) and the economic hardship associated with a positive test result (eg, loss of earnings in the event of sick leave). We share the authors' view that differential testing must be considered when analysing and interpreting the data. Thus, in our study, we examined the proportion of tests conducted in each occupational group as part of the sensitivity analyses (see supplementary figure S1, accessible at www.sjweh.fi/article/4037). As expected, testing proportions were exceptionally high in medical occupations (due to employer requirements). However, we did not observe systematic differences among non-medical occupations or when categorising by skill-level or managerial responsibility. This might be explained by several reasons. First, SARS-CoV-2 testing was free of charge during the first pandemic wave in Germany, but reporting a risk contact or having symptoms was a necessary condition for testing ( https://www.bundesgesundheitsministerium.de/coronavirus/chronik-coronavirus.html (accessed 5 September 2022). The newspaper article cited by van Tongeren et al is misleading as it refers to a calendar date after our study period. Second, different motivation for testing due to economic hardship in case of a positive test result is an unlikely explanation, because Germany has a universal healthcare system, including paid sick leave and sickness benefits for all workers (3). Self-employed people carry greater financial risks in case of sickness. We therefore included self-employment in the multivariable analyses to address this potential source of bias. While the observed inverse social gradient may be surprising, it actually matches with findings of ecological studies from Germany (4, 5), the United States (6, 7) as well as Spain, Portugal, Sweden, The Netherlands, Israel, and Hong Kong (8), all of which observed higher infection rates in wealthier neighbourhoods during the initial outbreak phase of the pandemic. One possible explanation is the higher mobility of managers and better educated workers, who are more likely to participate in meetings and engage in business travel and holiday trips like skiing. Given the increasing number of studies providing evidence for this hypothesis, we conclude that the inverse social gradient in our study likely reflects different exposure probabilities and is not a result of systematic bias. This also holds true for the elevated infection risks in essential workers, which is actually corroborated by a large body of research (9-11). Regarding differential likelihood of testing, van Tongeren et al state that "[i]t is relatively simple to address this problem by using a test-negative design" (1). As van Tongeren et al describe, this is a case-control approach only including individuals who were tested (without considering those who were not tested). However, the proposed analytical strategy can lead to another (more serious) selection bias if testing proportions and/or testing criteria differ between groups (12). This can be easily illustrated when comparing the results based on a time-incidence design with those obtained by a test-negative design as shown in table 1 (see PDF). Both approaches show similar results in terms of vertical occupational differences. Infection was more common if individuals had a high skill level or had a managerial position, but associations were stronger in the time-incidence design and did not reach statistical significance in the test-negative design (as indicated by the confidence intervals overlapping "1"). Unfortunately, the test-negative approach relies on a strongly reduced sample size and thus results in greater statistical uncertainty and loss of statistical power (13). In contrast, the test-negative design yields a different picture when estimating the association between essential occupation and infection risk: In this analysis, essential workers did not differ from non-essential workers in their chance of being infected with SARS-CoV-2 (the test-negative design even exhibits a lower chance for essential workers). This is rather counter-intuitive and is not in accordance with what we know about the occupational hazards of healthcare workers during the pandemic (14). The main problem is that proportions of positive tests are highly unreliable when testing proportions and/or testing criteria differ between groups. As essential workers were tested more often without being symptomatic (due to employer requirements), a lower proportion of positive tests in this group does not necessarily correspond to a lower risk of infection. Consequently, we are not convinced that the test-negative design should be the 'gold standard' for studying risk factors for SARS-CoV-2 infections (15). Especially problematic is the loss of statistical power (increasing the probability of a type II error) and the low validity of the test-positivity when test criteria and/or test proportions differ between groups. References 1. van Tongeren M, Rhodes S, Pearce N. Occupation and SARS-CoV-2 infection risk among workers during the first pandemic wave in Germany: potential for bias. Scand J Work Environ Health 2022;48(7):586-587. https://doi.org/10.5271/sjweh.4052. 2. Reuter M, Rigó M, Formazin M, Liebers F, Latza U, Castell S, et al. Occupation and SARS-CoV-2 infection risk among 108 960 workers during the first pandemic wave in Germany. Scand J Work Environ Health 2022;48:446-56. https://doi.org/10.5271/sjweh.4037. 3. Busse R, Blümel M, Knieps F, Bärnighausen T. Statutory health insurance in Germany: a health system shaped by 135 years of solidarity, self-governance, and competition. Lancet 2017;390:882-97. https://doi.org/10.1016/S0140-6736(17)31280-1. 4. Wachtler B, Michalski N, Nowossadeck E, Diercke M, Wahrendorf M, Santos-Hövener C, et al. Socioeconomic inequalities in the risk of SARS-CoV-2 infection - First results from an analysis of surveillance data from Germany. J Heal Monit 2020;5:18-29. https://doi.org/10.25646/7057. 5. Plümper T, Neumayer E. The pandemic predominantly hits poor neighbourhoods? SARS-CoV-2 infections and COVID-19 fatalities in German districts. Eur J Public Health 2020;30:1176-80. https://doi.org/10.1093/eurpub/ckaa168. 6. Abedi V, Olulana O, Avula V, Chaudhary D, Khan A, Shahjouei S, et al. Racial, Economic, and Health Inequality and COVID-19 Infection in the United States. J Racial Ethn Heal Disparities 2021;8:732-42. https://doi.org/10.1007/s40615-020-00833-4. 7. Mukherji N. The Social and Economic Factors Underlying the Incidence of COVID-19 Cases and Deaths in US Counties During the Initial Outbreak Phase. Rev Reg Stud 2022;52. https://doi.org/10.52324/001c.35255. 8. Beese F, Waldhauer J, Wollgast L, Pförtner T, Wahrendorf M, Haller S, et al. Temporal Dynamics of Socioeconomic Inequalities in COVID-19 Outcomes Over the Course of the Pandemic-A Scoping Review. Int J Public Health 2022;67:1-14. https://doi.org/10.3389/ijph.2022.1605128. 9. Nguyen LH, Drew DA, Graham MS, Joshi AD, Guo C-G, Ma W, et al. Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study. Lancet Public Heal 2020;5:e475-83. https://doi.org/10.1016/S2468-2667(20)30164-X. 10. Chou R, Dana T, Buckley DI, Selph S, Fu R, Totten AM. Epidemiology of and Risk Factors for Coronavirus Infection in Health Care Workers. Ann Intern Med 2020;173:120-36. https://doi.org/10.7326/M20-1632. 11. Stringhini S, Zaballa M-E, Pullen N, de Mestral C, Perez-Saez J, Dumont R, et al. Large variation in anti-SARS-CoV-2 antibody prevalence among essential workers in Geneva, Switzerland. Nat Commun 2021;12:3455. https://doi.org/10.1038/s41467-021-23796-4. 12. Accorsi EK, Qiu X, Rumpler E, Kennedy-Shaffer L, Kahn R, Joshi K, et al. How to detect and reduce potential sources of biases in studies of SARS-CoV-2 and COVID-19. Eur J Epidemiol 2021;36:179-96. https://doi.org/10.1007/s10654-021-00727-7. 13. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd Editio. New York: Routledge; 2013. https://doi.org/10.4324/9780203771587. 14. The Lancet. The plight of essential workers during the COVID-19 pandemic. Lancet 2020;395:1587. https://doi.org/10.1016/S0140-6736(20)31200-9. 15. Vandenbroucke JP, Brickley EB, Pearce N, Vandenbroucke-Grauls CMJE. The Evolving Usefulness of the Test-negative Design in Studying Risk Factors for COVID-19. Epidemiology 2022;33:e7-8. https://doi.org/10.1097/EDE.0000000000001438.

Health Care Workers' Sick Leave due to COVID-19 Vaccination in Context With SARS-CoV-2 Infection and Quarantine-A Multicenter Cross-Sectional Survey.

Background: Reactogenicity of coronavirus disease 2019 (COVID-19) vaccines can result in inability to work. The object of this study was to evaluate health care workers' sick leave after COVID-19 vaccination and to compare it with sick leave due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and quarantine leave. Methods: A multicenter cross-sectional survey was conducted at Regensburg University Medical Center and 10 teaching hospitals in South-East Germany from July 28 to October 15, 2021. Results: Of 2662 participants, 2309 (91.8%) were fully vaccinated without a history of SARS-CoV-2 infection. Sick leave after first/second vaccination occurred in 239 (10.4%) and 539 (23.3%) participants. In multivariable logistic regression, the adjusted odds ratio for sick leave after first/second vaccination compared with BNT162b2 was 2.26/3.72 for mRNA-1237 (95% CI, 1.28-4.01/1.99-6.96) and 27.82/0.48 for ChAdOx1-S (95% CI, 19.12-40.48/0.24-0.96). The actual median sick leave (interquartile range [IQR]) was 1 (0-2) day after any vaccination. Two hundred fifty-one participants (9.4%) reported a history of SARS-CoV-2 infection (median sick leave [IQR] 14 [10-21] days), 353 (13.3%) were quarantined at least once (median quarantine leave [IQR], 14 [10-14] days). Sick leave due to SARS-CoV-2 infection (4642 days) and quarantine leave (4710 days) accounted for 7.7 times more loss of workforce than actual sick leave after first and second vaccination (1216 days) in all fully vaccinated participants. Conclusions: Sick leave after COVID-19 vaccination is frequent and is associated with the vaccine applied. COVID-19 vaccination should reduce the much higher proportion of loss of workforce due to SARS-CoV-2 infection and quarantine.

Occupation and SARS-CoV-2 infection risk among 108 960 workers during the first pandemic wave in Germany.

OBJECTIVE: The aim of this study was to identify the occupational risk for a SARS-CoV-2 infection in a nationwide sample of German workers during the first wave of the COVID-19 pandemic (1 February-31 August 2020). METHODS: We used the data of 108 960 workers who participated in a COVID follow-up survey of the German National Cohort (NAKO). Occupational characteristics were derived from the German Classification of Occupations 2010 (Klassifikation der Berufe 2010). PCR-confirmed SARS-CoV-2 infections were assessed from self-reports. Incidence rates (IR) and incidence rate ratios (IRR) were estimated using robust Poisson regression, adjusted for person-time at risk, age, sex, migration background, study center, working hours, and employment relationship. RESULTS: The IR was 3.7 infections per 1000 workers [95% confidence interval (CI) 3.3-4.1]. IR differed by occupational sector, with the highest rates observed in personal (IR 4.8, 95% CI 4.0-5.6) and business administration (IR 3.4, 95% CI 2.8-3.9) services and the lowest rates in occupations related to the production of goods (IR 2.0, 95% CI 1.5-2.6). Infections were more frequent among essential workers compared with workers in non-essential occupations (IRR 1.95, 95% CI 1.59-2.40) and among highly skilled compared with skilled professions (IRR 1.36, 95% CI 1.07-1.72). CONCLUSIONS: The results emphasize higher infection risks in essential occupations and personal-related services, especially in the healthcare sector. Additionally, we found evidence that infections were more common in higher occupational status positions at the beginning of the pandemic.

Contribution of High Viral Loads, Detection of Viral Antigen and Seroconversion to Severe Acute Respiratory Syndrome Coronavirus 2 Infectivity.

BACKGROUND: From a public health perspective, effective containment strategies for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) should be balanced with individual liberties. METHODS: We collected 79 respiratory samples from 59 patients monitored in an outpatient center or in the intensive care unit of the University Hospital Regensburg. We analyzed viral load by quantitative real-time polymerase chain reaction, viral antigen by point-of-care assay, time since onset of symptoms, and the presence of SARS-CoV-2 immunoglobulin G (IgG) antibodies in the context of virus isolation from respiratory specimens. RESULTS: The odds ratio for virus isolation increased 1.9-fold for each log10 level of SARS-CoV-2 RNA and 7.4-fold with detection of viral antigen, while it decreased 6.3-fold beyond 10 days of symptoms and 20.0-fold with the presence of SARS-CoV-2 antibodies. The latter was confirmed for B.1.1.7 strains. The positive predictive value for virus isolation was 60.0% for viral loads >107 RNA copies/mL and 50.0% for the presence of viral antigen. Symptom onset before 10 days and seroconversion predicted lack of infectivity with negative predictive values of 93.8% and 96.0%. CONCLUSIONS: Our data support quarantining patients with high viral load and detection of viral antigen and lifting restrictive measures with increasing time to symptom onset and seroconversion. Delay of antibody formation may prolong infectivity.

Results of WICOVIR Gargle Pool PCR Testing in German Schools Based on the First 100,000 Tests.

Background: Opening schools and keeping children safe from SARS-CoV-2 infections at the same time is urgently needed to protect children from direct and indirect consequences of the COVID-19 pandemic. To achieve this goal, a safe, efficient, and cost-effective SARS-CoV-2 testing system for schools in addition to standard hygiene measures is necessary. Methods: We implemented the screening WICOVIR concept for schools in the southeast of Germany, which is based on gargling at home, pooling of samples in schools, and assessment of SARS-CoV-2 by pool rRT-PCR, performed decentralized in numerous participating laboratories. Depooling was performed if pools were positive, and results were transmitted with software specifically developed for the project within a day. Here, we report the results after the first 13 weeks in the project. Findings: We developed and implemented the proof-of-concept test system within a pilot phase of 7 weeks based on almost 17,000 participants. After 6 weeks in the main phase of the project, we performed >100,000 tests in total, analyzed in 7,896 pools, identifying 19 cases in >100 participating schools. On average, positive children showed an individual CT value of 31 when identified in the pools. Up to 30 samples were pooled (mean 13) in general, based on school classes and attached school staff. All three participating laboratories detected positive samples reliably with their previously established rRT-PCR standard protocols. When self-administered antigen tests were performed concomitantly in positive cases, only one of these eight tests was positive, and when antigen tests performed after positive pool rRT-PCR results were already known were included, 3 out of 11 truly positive tests were also identified by antigen testing. After 3 weeks of repetitive WICOVIR testing twice weekly, the detection rate of positive children in that cohort decreased significantly from 0.042 to 0.012 (p = 0.008). Interpretation: Repeated gargle pool rRT-PCR testing can be implemented quickly in schools. It is an effective, valid, and well-received test system for schools, superior to antigen tests in sensitivity, acceptance, and costs.

Clinical and epidemiological data of COVID-19 from Regensburg, Germany: a retrospective analysis of 1084 consecutive cases.

BACKGROUND: COVID-19 is a syndrome caused by the recently emerged SARS-CoV-2. We collected clinical and epidemiologic data in an almost complete cohort of SARS-CoV-2 positive individuals from Regensburg, Germany, from March 2020 to May 2020. METHODS: Analysis of a retrospectively documented cohort of consecutive COVID-19 cases recorded between March 7, 2020 and May 24, 2020 as part of an infection control investigation program, with prospective follow-up interviews gathering information on type and duration of symptoms and COVID-19 risk factors until June 26, 2020. RESULTS: Of 1089 total cases, 1084 (99.5%) cases were included. The incidence during the time period was 315.4/100,000, lower than in the superordinate government district Oberpfalz (468.5/100,000) and the overall state of Bavaria (359.7/100,000). The case fatality ratio (CFR) was 2.1%. Among fatal cases, the mean age was 74.4 years and 87% presented with known risk factors, most commonly chronic heart disease, chronic lung disease, kidney disease, and diabetes mellitus. 897 cases (82.7%) showed at least one symptom, most frequently cough (45%) and fever (41%). Further, 18% of cases suffered from odour/taste disorder. 17% of total cases reported no symptoms. The median duration of general illness was 10 days. During follow-up, 8.9% of 419 interviewed cases reported at least one symptom lasting at least 6 weeks, and fatigue was the most frequent persistent symptom. DISCUSSION: We report data on type and duration of symptoms, and clinical severity of nearly all (99.5%) patients with SARS-CoV-2 recorded from March 2020 to May 2020 in Regensburg. A broad range of symptoms and symptom duration was seen, some of them lasting several weeks in a considerable number of cases. The case-fatality ratio was 2.1%. Asymptomatic cases may be underrepresented due to the nature of the study.

Effect of a strict hygiene bundle for the prevention of nosocomial transmission of SARS-CoV-2 in the hospital: a practical approach from the field.

BACKGROUND: During the novel coronavirus disease (COVID-19) pandemic it is crucial for hospitals to implement infection prevention strategies to reduce nosocomial transmission to the lowest possible number. This is all the more important because molecular tests for identifying SARS-CoV-2 infected patients are uncertain, and the resources available for them are limited. In this view, a monocentric, retrospective study with an interventional character was conducted to investigate the extent to which the introduction of a strict hygiene bundle including a general mask requirement and daily screening for suspicious patients has an impact on the SARS-CoV-2 nosocomial rate in the pandemic environment. METHODS: All inpatients from a maximum care hospital in Regensburg (Bavaria) between March 1st and June 10th 2020 were included. Patient with respiratory symptoms were tested for SARS-CoV-2 at admission, patients were managed according to a standard hygiene protocol. At the end of March a strict hygiene bundle was introduced including a general mask obligation and a daily clinical screening of inpatients for respiratory symptoms. Nosocomial infection rate for COVID-19 and the risk for infection transmission estimated by the nosocomial incidence density before and after introduction the hygiene bundle were compared. The infection pressure for the hospital during the entire observational period was characterized by the infection reports in the region in relation to the number of hospitalized COVID-19 patients and the number of infected employees. RESULTS: In fact, after the introduction of a strict hygiene bundle including a general mouth and nose protection obligation and a daily clinical screening of suspicious patients, a significant reduction of the nosocomial rate from 0.28 to 0.06 (p = 0.026) was observed. Furthermore, the risk of spreading hospital-acquired infections also decreased dramatically from 0.0007 to 0.00018 (p = 0.031; rate ratio after/before 0.25 (95%CI 0.06, 1.07) despite a slow decrease of the hospital COVID 19-prevalence and an increase of infected employees. CONCLUSION: The available data underline that a strict hygiene bundle seem to be associated with a decrease of nosocomial SARS-CoV-2 transmission in the pandemic situation.

[SARS-CoV-2/COVID-19-epidemiology and prevention]. / SARS-CoV-2/COVID-19 ­ Epidemiologie und Prävention.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread globally since December 2019. A first wave is visible up to the end of June 2020 in many regions. This article presents a review of the current knowledge on the epidemiology and prevention. The SARS-CoV­2 predominantly replicates in the upper and lower respiratory tracts and is particularly transmitted by droplets and aerosols. The estimate for the basic reproduction number (R0) is between 2 and 3 and the median incubation period is 6 days (range 2-14 days). As with the related SARS-CoV and Middle East respiratory syndrome (MERS-CoV), superspreading events play an important role in the dissemination. A high proportion of infections are uncomplicated but moderate or severe courses develop in 5-10% of infected persons. Pneumonia, cardiac involvement and thromboembolisms are the most frequent manifestations leading to hospitalization. Risk factors for a complicated course are high age, hypertension, diabetes mellitus and chronic cardiovascular and pulmonary diseases as well as immunodeficiency. Currently, the estimation for the infection fatality rate (IFR) is between 0.5% and 1% across all age groups. Outbreaks were limited in many regions with bundles of various measures for reduction of social contacts. The incidence for the first wave in Germany can be estimated as 0.4-1.8% and excess mortality could not be observed.

Changing epidemiology of COVID-19.

Background: We analyzed the epidemiology of COVID-19 in Regensburg after the first wave ended in June 2020 and compared it with patients' characteristics and symptoms in late summer/early autumn 2020. Methods: Retrospective analysis of epidemiological data from Regensburg (city/county) on age and initial symptoms as reported during case investigation for containment. Observed periods: March 7, 2020 to June 6, 2020 and August 12, 2020 to October 9, 2020. Results: The proportion of asymptomatic persons who tested positive for SARS-COV-2 in the second period was 55% (286 of 520 cases), whereas during the first wave from March to June 2020 this percentage was 14.4% (169 of 1,170 cases). A comparison of typical symptoms shows that the most common symptoms of COVID-19 in the first wave (cough, fever and generally feeling ill) were less often reported in the second period: cough 14% vs. 42%, fever 17% vs. 38%, general signs of illness 14% vs. 22% in the second vs. first period, respectively overall cases were younger in the second period, the median age of asymptomatic cases was comparable in both periods. The case fatality rate for the first period was 2.1%, in the second it was 0.2%. Discussion: The epidemiological situation in the second period is different from that during the first wave. We observed a considerable proportion of questionable cases in August/September 2020 (asymptomatic cases, high ct values, often only detection of one gene). False positive cases/non-contagious cases have to be taken into account for this period. On-demand or free-of-charge testing for asymptomatic persons will lower the positive predictive value of tests and place a high burden on finite capacities.

Epidemiology of SARS-CoV-2.

PURPOSE: SARS-CoV-2 is a recently emerged ß-coronavirus. Here we present the current knowledge on its epidemiologic features. METHODS: Non-systematic review. RESULTS: SARS-CoV-2 replicates in the upper and lower respiratory tract. It is mainly transmitted by droplets and aerosols from asymptomatic and symptomatic infected subjects. The consensus estimate for the basis reproduction number (R0) is between 2 and 3, and the median incubation period is 5.7 (range 2-14) days. Similar to SARS and MERS, superspreading events have been reported, the dispersion parameter (kappa) is estimated at 0.1. Most infections are uncomplicated, and 5-10% of patients are hospitalized, mainly due to pneumonia with severe inflammation. Complications are respiratory and multiorgan failure; risk factors for complicated disease are higher age, hypertension, diabetes, chronic cardiovascular, chronic pulmonary disease and immunodeficiency. Nosocomial and infections in medical personnel have been reported. Drastic reductions of social contacts have been implemented in many countries with outbreaks of SARS-CoV-2, leading to rapid reductions. Most interventions have used bundles, but which of the measures have been more or less effective is still unknown. The current estimate for the infection's fatality rate is 0.5-1%. Using current models of age-dependent infection fatality rates, upper and lower limits for the attack rate in Germany can be estimated between 0.4 and 1.6%, lower than in most European countries. CONCLUSIONS: Despite a rapid worldwide spread, attack rates have been low in most regions, demonstrating the efficacy of control measures.