SARS-CoV-2 sero-immunity and quality of life in children and adolescents in relation to infections and vaccinations: the IMMUNEBRIDGE KIDS cross-sectional study, 2022.

PURPOSE: The study evaluates the effects on sero-immunity, health status and quality of life of children and adolescents after the upsurge of the Omicron variant in Germany. METHODS: This multicenter cross-sectional study (IMMUNEBRIDGE Kids) was conducted within the German Network University Medicine (NUM) from July to October 2022. SARS-CoV-2- antibodies were measured and data on SARS-CoV-2 infections, vaccinations, health and socioeconomic factors as well as caregiver-reported evaluation on their children's health and psychological status were assessed. RESULTS: 497 children aged 2-17 years were included. Three groups were analyzed: 183 pre-schoolchildren aged 2-4 years, 176 schoolchildren aged 5-11 years and 138 adolescents aged 12-18 years. Positive antibodies against the S- or N-antigen of SARS-CoV-2 were detected in 86.5% of all participants (70.0% [128/183] of pre-schoolchildren, 94.3% of schoolchildren [166/176] and 98.6% of adolescents [136/138]). Among all children, 40.4% (201/497) were vaccinated against COVID-19 (pre-schoolchildren 4.4% [8/183], schoolchildren 44.3% [78/176] and adolescents 83.3% [115/138]). SARS-CoV-2 seroprevalence was lowest in pre-school. Health status and quality of life reported by the parents were very positive at the time of the survey (Summer 2022). CONCLUSION: Age-related differences on SARS-CoV-2 sero-immunity could mainly be explained by differences in vaccination rates based on the official German vaccination recommendations as well as differences in SARS-CoV-2 infection rates in the different age groups. Health status and quality of life of almost all children were very good independent of SARS-CoV-2 infection and/or vaccination. TRIAL REGISTRATION: German Registry for Clinical Trials Identifier Würzburg: DRKS00025546 (registration: 11.09.2021), Bochum: DRKS00022434 (registration:07.08.2020), Dresden: DRKS 00022455 (registration: 23.07.2020).

Estimating fine age structure and time trends in human contact patterns from coarse contact data: The Bayesian rate consistency model.

Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), large-scale social contact surveys are now longitudinally measuring the fundamental changes in human interactions in the face of the pandemic and non-pharmaceutical interventions. Here, we present a model-based Bayesian approach that can reconstruct contact patterns at 1-year resolution even when the age of the contacts is reported coarsely by 5 or 10-year age bands. This innovation is rooted in population-level consistency constraints in how contacts between groups must add up, which prompts us to call the approach presented here the Bayesian rate consistency model. The model can also quantify time trends and adjust for reporting fatigue emerging in longitudinal surveys through the use of computationally efficient Hilbert Space Gaussian process priors. We illustrate estimation accuracy on simulated data as well as social contact data from Europe and Africa for which the exact age of contacts is reported, and then apply the model to social contact data with coarse information on the age of contacts that were collected in Germany during the COVID-19 pandemic from April to June 2020 across five longitudinal survey waves. We estimate the fine age structure in social contacts during the early stages of the pandemic and demonstrate that social contact intensities rebounded in an age-structured, non-homogeneous manner. The Bayesian rate consistency model provides a model-based, non-parametric, computationally tractable approach for estimating the fine structure and longitudinal trends in social contacts and is applicable to contemporary survey data with coarsely reported age of contacts as long as the exact age of survey participants is reported.

Immunity Against SARS-CoV-2 in the German Population.

BACKGROUND: Early during the SARS-CoV-2 pandemic, national population-based seroprevalence surveys were conducted in some countries; however, this was not done in Germany. In particular, no seroprevalence surveys were planned for the summer of 2022. In the context of the IMMUNEBRIDGE project, the GUIDE study was carried out to estimate seroprevalence on the national and regional levels. METHODS: To obtain an overview of the population-wide immunity against SARS-CoV-2 among adults in Germany that would be as statistically robust as possible, serological tests were carried out using self-sampling dried blood spot cards in conjunction with surveys, one by telephone and one online. Blood samples were analyzed for the presence of antibodies to the S and N antigens of SARS-CoV-2. RESULTS: Among the 15 932 participants, antibodies to the S antigen were detected in 95.7%, and to the N antigen in 44.4%. In the higher-risk age groups of persons aged 65 and above and persons aged 80 and above, anti-S antibodies were found in 97,4% and 98.8%, respectively. Distinct regional differences in the distribution of anti-S and anti-N antibodies emerged. Immunity gaps were found both regionally and in particular subgroups of the population. High anti-N antibody levels were especially common in eastern German states, and high anti-S antibody levels in western German states. CONCLUSION: These findings indicate that a large percentage of the adult German population has formed antibodies against the SARS-CoV-2 virus. This will markedly lower the probability of an overburdening of the health care system by hospitalization and high occupancy of intensive care units due to future SARS-CoV-2 waves, depending on the viral characteristics of then prevailing variants.

Increase in Mental Disorders During the COVID-19 Pandemic-The Role of Occupational and Financial Strains.

BACKGROUND: Numerous studies have reported an increase in mental disorders during the COVID-19 pandemic, but the exact reasons for this development are not well understood. In this study we investigate whether pandemic-related occupational and financial changes (e.g., reduced working hours, working from home, financial losses) were associated with increased symptoms of depression and anxiety compared with the situation before the pandemic. METHODS: We analyzed data from the German National Cohort (NAKO) Study. Between May and November 2020, 161 849 study participants answered questions on their mental state and social circumstances. Their responses were compared with data from the baseline survey before the pandemic (2014-2019). Linear fixed-effects models were used to determine whether individual changes in the severity of symptoms of depression (PHQ-9) or anxiety (GAD-7) were associated with occupational/ financial changes (controlling for various covariates). RESULTS: The prevalence of moderate or severe symptoms of depression and anxiety increased by 2.4% and 1.5%, respectively, during the COVID-19 pandemic compared with the preceding years. The mean severity of the symptoms rose slightly. A pronounced increase in symptoms was observed among those who became unemployed during the pandemic (+ 1.16 points on the depression scale, 95% confidence interval [0.91; 1.41], range 0-27). Increases were also seen for reduced working hours with no short-time allowance, increased working hours, working from home, insecurity regarding employment, and financial strain. The deterioration in mental health was largely statistically explained by the occupational and financial changes investigated in the model. CONCLUSION: Depressive symptoms and anxiety disorders increased slightly in the study population during the first year of the COVID-19 pandemic. Occupational and financial difficulties were an essential contributory factor. These strains should be taken into account both in the care of individual patients and in the planning of targeted prevention measures.

Social contact patterns during the COVID-19 pandemic in 21 European countries - evidence from a two-year study.

BACKGROUND: Most countries have enacted some restrictions to reduce social contacts to slow down disease transmission during the COVID-19 pandemic. For nearly two years, individuals likely also adopted new behaviours to avoid pathogen exposure based on personal circumstances. We aimed to understand the way in which different factors affect social contacts - a critical step to improving future pandemic responses. METHODS: The analysis was based on repeated cross-sectional contact survey data collected in a standardized international study from 21 European countries between March 2020 and March 2022. We calculated the mean daily contacts reported using a clustered bootstrap by country and by settings (at home, at work, or in other settings). Where data were available, contact rates during the study period were compared with rates recorded prior to the pandemic. We fitted censored individual-level generalized additive mixed models to examine the effects of various factors on the number of social contacts. RESULTS: The survey recorded 463,336 observations from 96,456 participants. In all countries where comparison data were available, contact rates over the previous two years were substantially lower than those seen prior to the pandemic (approximately from over 10 to < 5), predominantly due to fewer contacts outside the home. Government restrictions imposed immediate effect on contacts, and these effects lingered after the restrictions were lifted. Across countries, the relationships between national policy, individual perceptions, or personal circumstances determining contacts varied. CONCLUSIONS: Our study, coordinated at the regional level, provides important insights into the understanding of the factors associated with social contacts to support future infectious disease outbreak responses.

Effect of risk status for severe COVID-19 on individual contact behaviour during the SARS-CoV-2 pandemic in 2020/2021-an analysis based on the German COVIMOD study.

BACKGROUND: One of the primary aims of contact restriction measures during the SARS-CoV-2 pandemic has been to protect people at increased risk of severe disease from the virus. Knowledge about the uptake of contact restriction measures in this group is critical for public health decision-making. We analysed data from the German contact survey COVIMOD to assess differences in contact patterns based on risk status, and compared this to pre-pandemic data to establish whether there was a differential response to contact reduction measures. METHODS: We quantified differences in contact patterns according to risk status by fitting a generalised linear model accounting for within-participant clustering to contact data from 31 COVIMOD survey waves (April 2020-December 2021), and estimated the population-averaged ratio of mean contacts of persons with high risk for a severe COVID-19 outcome due to age or underlying health conditions, to those without. We then compared the results to pre-pandemic data from the contact surveys HaBIDS and POLYMOD. RESULTS: Averaged across all analysed waves, COVIMOD participants reported a mean of 3.21 (95% confidence interval (95%CI) 3.14,3.28) daily contacts (truncated at 100), compared to 18.10 (95%CI 17.12,19.06) in POLYMOD and 28.27 (95%CI 26.49,30.15) in HaBIDS. After adjusting for confounders, COVIMOD participants aged 65 or above had 0.83 times (95%CI 0.79,0.87) the number of contacts as younger age groups. In POLYMOD, this ratio was 0.36 (95%CI 0.30,0.43). There was no clear difference in contact patterns due to increased risk from underlying health conditions in either HaBIDS or COVIMOD. We also found that persons in COVIMOD at high risk due to old age increased their non-household contacts less than those not at such risk after strict restriction measures were lifted. CONCLUSIONS: Over the course of the SARS-CoV-2 pandemic, there was a general reduction in contact numbers in the German population and also a differential response to contact restriction measures based on risk status for severe COVID-19. This differential response needs to be taken into account for parametrisations of mathematical models in a pandemic setting.

Age-specific contribution of contacts to transmission of SARS-CoV-2 in Germany.

Current estimates of pandemic SARS-CoV-2 spread in Germany using infectious disease models often do not use age-specific infection parameters and are not always based on age-specific contact matrices of the population. They also do usually not include setting- or pandemic phase-based information from epidemiological studies of reported cases and do not account for age-specific underdetection of reported cases. Here, we report likely pandemic spread using an age-structured model to understand the age- and setting-specific contribution of contacts to transmission during different phases of the COVID-19 pandemic in Germany. We developed a deterministic SEIRS model using a pre-pandemic contact matrix. The model was optimized to fit age-specific SARS-CoV-2 incidences reported by the German National Public Health Institute (Robert Koch Institute), includes information on setting-specific reported cases in schools and integrates age- and pandemic period-specific parameters for underdetection of reported cases deduced from a large population-based seroprevalence studies. Taking age-specific underreporting into account, younger adults and teenagers were identified in the modeling study as relevant contributors to infections during the first three pandemic waves in Germany. For the fifth wave, the Delta to Omicron transition, only age-specific parametrization reproduces the observed relative and absolute increase in pediatric hospitalizations in Germany. Taking into account age-specific underdetection did not change considerably how much contacts in schools contributed to the total burden of infection in the population (up to 12% with open schools under hygiene measures in the third wave). Accounting for the pandemic phase and age-specific underreporting is important to correctly identify those groups of the population in which quarantine, testing, vaccination, and contact-reduction measures are likely to be most effective and efficient. Age-specific parametrization is also highly relevant to generate informative age-specific output for decision makers and resource planers.

Borrelia multiplex: a bead-based multiplex assay for the simultaneous detection of Borrelia specific IgG/IgM class antibodies.

BACKGROUND: Lyme borreliosis (LB) is the most common tick-borne infectious disease in the northern hemisphere. The diagnosis of LB is usually made by clinical symptoms and subsequently supported by serology. In Europe, a two-step testing consisting of an enzyme-linked immunosorbent assay (ELISA) and an immunoblot is recommended. However, due to the low sensitivity of the currently available tests, antibody detection is sometimes inaccurate, especially in the early phase of infection, leading to underdiagnoses. METHODS: To improve upon Borrelia diagnostics, we developed a multiplex Borrelia immunoassay (Borrelia multiplex), which utilizes the new INTELLIFLEX platform, enabling the simultaneous dual detection of IgG and IgM antibodies, saving further time and reducing the biosample material requirement. In order to enable correct classification, the Borrelia multiplex contains eight antigens from the five human pathogenic Borrelia species known in Europe. Six antigens are known to mainly induce an IgG response and two antigens are predominant for an IgM response. RESULTS: To validate the assay, we compared the Borrelia multiplex to a commercial bead-based immunoassay resulting in an overall assay sensitivity of 93.7% (95% CI 84.8-97.5%) and a specificity of 96.5% (95%CI 93.5-98.1%). To confirm the calculated sensitivity and specificity, a comparison with a conventional 2-step diagnostics was performed. With this comparison, we obtained a sensitivity of 95.2% (95% CI 84.2-99.2%) and a specificity of 93.0% (95% CI 90.6-94.7%). CONCLUSION: Borrelia multiplex is a highly reproducible cost- and time-effective assay that enables the profiling of antibodies against several individual antigens simultaneously.

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.

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.

Providing laypeople with results from dynamic infectious disease modelling studies affects their allocation preference for scarce medical resources-a factorial experiment.

BACKGROUND: Allocation of scarce medical resources can be based on different principles. It has not yet been investigated which allocation schemes are preferred by medical laypeople in a particular situation of medical scarcity like an emerging infectious disease and how the choices are affected by providing information about expected population-level effects of the allocation scheme based on modelling studies. We investigated the potential benefit of strategic communication of infectious disease modelling results. METHODS: In a two-way factorial experiment (n = 878 participants), we investigated if prognosis of the disease or information about expected effects on mortality at population-level (based on dynamic infectious disease modelling studies) influenced the choice of preferred allocation schemes for prevention and treatment of an unspecified sexually transmitted infection. A qualitative analysis of the reasons for choosing specific allocation schemes supplements our results. RESULTS: Presence of the factor "information about the population-level effects of the allocation scheme" substantially increased the probability of choosing a resource allocation system that minimized overall harm among the population, while prognosis did not affect allocation choices. The main reasons for choosing an allocation scheme differed among schemes, but did not differ among those who received additional model-based information on expected population-level effects and those who did not. CONCLUSIONS: Providing information on the expected population-level effects from dynamic infectious disease modelling studies resulted in a substantially different choice of allocation schemes. This finding supports the importance of incorporating model-based information in decision-making processes and communication strategies.

Individual social contact data and population mobility data as early markers of SARS-CoV-2 transmission dynamics during the first wave in Germany-an analysis based on the COVIMOD study.

BACKGROUND: The effect of contact reduction measures on infectious disease transmission can only be assessed indirectly and with considerable delay. However, individual social contact data and population mobility data can offer near real-time proxy information. The aim of this study is to compare social contact data and population mobility data with respect to their ability to reflect transmission dynamics during the first wave of the SARS-CoV-2 pandemic in Germany. METHODS: We quantified the change in social contact patterns derived from self-reported contact survey data collected by the German COVIMOD study from 04/2020 to 06/2020 (compared to the pre-pandemic period from previous studies) and estimated the percentage mean reduction over time. We compared these results as well as the percentage mean reduction in population mobility data (corrected for pre-pandemic mobility) with and without the introduction of scaling factors and specific weights for different types of contacts and mobility to the relative reduction in transmission dynamics measured by changes in R values provided by the German Public Health Institute. RESULTS: We observed the largest reduction in social contacts (90%, compared to pre-pandemic data) in late April corresponding to the strictest contact reduction measures. Thereafter, the reduction in contacts dropped continuously to a minimum of 73% in late June. Relative reduction of infection dynamics derived from contact survey data underestimated the one based on reported R values in the time of strictest contact reduction measures but reflected it well thereafter. Relative reduction of infection dynamics derived from mobility data overestimated the one based on reported R values considerably throughout the study. After the introduction of a scaling factor, specific weights for different types of contacts and mobility reduced the mean absolute percentage error considerably; in all analyses, estimates based on contact data reflected measured R values better than those based on mobility. CONCLUSIONS: Contact survey data reflected infection dynamics better than population mobility data, indicating that both data sources cover different dimensions of infection dynamics. The use of contact type-specific weights reduced the mean absolute percentage errors to less than 1%. Measuring the changes in mobility alone is not sufficient for understanding the changes in transmission dynamics triggered by public health measures.

Negative SARS-CoV-2 PCR or rapid antigen test result and the subsequent risk of being infectious: a mathematical simulation study.

BACKGROUND: A considerable proportion of SARS-CoV-2 transmission occurs from asymptomatic and pre-symptomatic cases. Therefore, different polymerase chain reaction (PCR)- or rapid antigen test (RAT)-based approaches are being discussed and applied to identify infectious individuals that would have otherwise gone undetected. In this article, we provide a framework to estimate the time-dependent risk of being infectious after a negative SARS-CoV-2 test, and we simulate the number of expected infectious individuals over time in populations who initially tested negative. METHODS: A Monte Carlo approach is used to simulate asymptomatic infections over a 10-days period in populations of 1000 individuals following a negative SARS-CoV-2 test. Parameters representing the application of PCR tests or RATs are utilized, and SARS-CoV-2 cumulative 7-day incidences between 25 and 200 per 100,000 people are considered. Simulation results are compared to case numbers predicted via a mathematical equation. RESULTS: The simulations showed a continuous increase in infectious individuals over time in populations of individuals who initially tested SARS-CoV-2 negative. The interplay between false negative rates of PCR tests or RATs, and the time that has passed since testing determines the number of infectious individuals. The simulated and the mathematically predicted number of infectious individuals were comparable. However, Monte Carlo simulations highlight that, due to random variation, theoretically observed infectious individuals can considerably exceed predicted case numbers even shortly after a test was conducted. CONCLUSIONS: This study demonstrates that the number of infectious individuals in a screened group of asymptomatic people can be effectively reduced, and this effect can be described mathematically. However, the false negative rate of a test, the time since the negative test and the underlying SARS-CoV-2 incidence are critical parameters in determining the observed subsequent number of cases in tested population groups.

The impact of the Covid-19 pandemic on breast cancer early detection and screening.

The COVID-19 pandemic affects mortality and morbidity, with disruptions expected to continue for some time, with access to timely cancer-related services a concern. For breast cancer, early detection and treatment is key to improved survival and longer-term quality of life. Health services generally have been strained and in many settings with population breast mammography screening, efforts to diagnose and treat breast cancers earlier have been paused or have had reduced capacity. The resulting delays to diagnosis and treatment may lead to more intensive treatment requirements and, potentially, increased mortality. Modelled evaluations can support responses to the pandemic by estimating short- and long-term outcomes for various scenarios. Multiple calibrated and validated models exist for breast cancer screening, and some have been applied in 2020 to estimate the impact of breast screening disruptions and compare options for recovery, in a range of international settings. On behalf of the Covid and Cancer Modelling Consortium (CCGMC) Working Group 2 (Breast Cancer), we summarize and provide examples of such in a range of settings internationally, and propose priorities for future modelling exercises. International expert collaborations from the CCGMC Working Group 2 (Breast Cancer) will conduct analyses and modelling studies needed to inform key stakeholders recovery efforts in order to mitigate the impact of the pandemic on early diagnosis and treatment of breast cancer.

Exploring beyond clinical routine SARS-CoV-2 serology using MultiCoV-Ab to evaluate endemic coronavirus cross-reactivity.

The humoral immune response to SARS-CoV-2 is a benchmark for immunity and detailed analysis is required to understand the manifestation and progression of COVID-19, monitor seroconversion within the general population, and support vaccine development. The majority of currently available commercial serological assays only quantify the SARS-CoV-2 antibody response against individual antigens, limiting our understanding of the immune response. To overcome this, we have developed a multiplex immunoassay (MultiCoV-Ab) including spike and nucleocapsid proteins of SARS-CoV-2 and the endemic human coronaviruses. Compared to three broadly used commercial in vitro diagnostic tests, our MultiCoV-Ab achieves a higher sensitivity and specificity when analyzing a well-characterized sample set of SARS-CoV-2 infected and uninfected individuals. We find a high response against endemic coronaviruses in our sample set, but no consistent cross-reactive IgG response patterns against SARS-CoV-2. Here we show a robust, high-content-enabled, antigen-saving multiplex assay suited to both monitoring vaccination studies and facilitating epidemiologic screenings for humoral immunity towards pandemic and endemic coronaviruses.

[COVID-19: Knowledge, risk perception and strategies for handling the pandemic]. / COVID-19: Wissensstand, Risikowahrnehmung und Umgang mit der Pandemie.

INTRODUCTION: In order to stem the spread of an epidemic, widespread adherence to safety measures and their acceptance within the German population are of key importance. This survey examines the levels of knowledge and the perception of risk within the population and analyses implementation and adherence to the recommended and legally mandated safety measures in the early phase of the COVID-19 pandemic. METHODS: In March 2020, participants registered on the HeReCa-Online-Panel from Saxony-Anhalt, Berlin and Schleswig Holstein were invited to complete a 65-question survey. RESULTS: 1048 respondents answered the questionnaire, which amounts to a response of 3.5%. 83% of respondents stated that they felt themselves to be well-informed or very well-informed concerning COVID-19 and the coronavirus. The majority of respondents reported fears for the well-being of family members (60%) or the health of the German population as a whole (45%); 79% reported concerns regarding adverse economic impacts. 79% of respondents have implemented individual protective measures, such as reducing social contacts and maintaining the recommended physical distance in public spaces. Most respondents regarded the government-mandated safety measures as predominantly reasonable and appropriate. CONCLUSIONS: In the early phase of the pandemic, most people kept themselves informed about of COVID-19 and started to take individual measures for risk reduction. Acceptance of governmental measures to stem the spread of the pandemic was high.

Epidemiological Measures in the Context of the COVID-19 Pandemic.

BACKGROUND: The various epidemiological indicators used to communicate the impact of COVID-19 have different strengths and limitations. METHODS: We conducted a selective literature review to identify the indicators used and to derive appropriate definitions. We calculated crude and age-adjusted indicators for selected countries. RESULTS: The proportion of deaths (case fatality proportion [CFP]; number of deaths/ total number of cases) is commonly used to estimate the severity of a disease. If the CFP is used for purposes of comparison, the existence of heterogeneity in the detection and registration of cases and deaths has to be taken into account. In the early phase of an epidemic, when case numbers rise rapidly, the CFP suffers from bias. For these reasons, variants have been proposed: the "confirmed CFP" (number of deaths/total number of confirmed cases), and the "delay-adjusted CFP," which considers the delay between infection with the disease and death from the disease. The indicator mortality (number of deaths/total population) has at first sight the advantage of being based on a defined denominator, the total population. During the outbreak of a disease, however, the cumulative deaths rise while the total population remains stable. The phase of the epidemic therefore has to be considered when using this indicator. In this context, R0 and R(t) are important indicators. R0 estimates the maximum rate of spread of a disease in a population, while R(t) describes the dynamics of the epidemic at a given time. Age-adjusted analysis of the CFP shows that the differences between countries decrease but do not dis - appear completely. If the test strategies depend on age or symptom severity, however, the bias cannot be entirely eliminated. CONCLUSION: Various indicators of the impact of the COVID-19 epidemic at population level are used in daily communication. Considering the relevance of the pandemic and the importance of relevant communications, however, the strengths and the limitations of each parameter must be considered carefully.