Spatio-Temporal Patterns of the SARS-CoV-2 Epidemic in Germany.

Results from an explorative study revealing spatio-temporal patterns of the SARS-CoV-2/ COVID-19 epidemic in Germany are presented. We dispense with contestable model assumptions and show the intrinsic spatio-temporal patterns of the epidemic dynamics. The analysis is based on COVID-19 incidence data, which are age-stratified and spatially resolved at the county level, provided by the Federal Government's Public Health Institute of Germany (RKI) for public use. Although the 400 county-related incidence time series shows enormous heterogeneity, both with respect to temporal features as well as spatial distributions, the counties' incidence curves organise into well-distinguished clusters that coincide with East and West Germany. The analysis is based on dimensionality reduction, multidimensional scaling, network analysis, and diversity measures. Dynamical changes are captured by means of difference-in-difference methods, which are related to fold changes of the effective reproduction numbers. The age-related dynamical patterns suggest a considerably stronger impact of children, adolescents and seniors on the epidemic activity than previously expected. Besides these concrete interpretations, the work mainly aims at providing an atlas for spatio-temporal patterns of the epidemic, which serves as a basis to be further explored with the expertise of different disciplines, particularly sociology and policy makers. The study should also be understood as a methodological contribution to getting a handle on the unusual complexity of the COVID-19 pandemic.

Local socio-structural predictors of COVID-19 incidence in Germany.

Socio-economic conditions and social attitudes are known to represent epidemiological determinants. Credible knowledge on socio-economic driving factors of the COVID-19 epidemic is still incomplete. Based on linear random effects regression, an ecological model is derived to estimate COVID-19 incidence in German rural/urban districts from local socio-economic factors and popularity of political parties in terms of their share of vote. Thereby, records provided by Germany's public health institute (Robert Koch Institute) of weekly notified 7-day incidences per 100,000 inhabitants per district from the outset of the epidemic in 2020 up to December 1, 2021, are used to construct the dependent variable. Local socio-economic conditions including share of votes, retrieved from the Federal Statistical Office of Germany, have been used as potential risk factors. Socio-economic parameters like per capita income, proportions of protection seekers and social benefit claimants, and educational level have negligible impact on incidence. To the contrary, incidence significantly increases with population density and we observe a strong association with vote shares. Popularity of the right-wing party Alternative for Germany (AfD) bears a considerable risk of increasing COVID-19 incidence both in terms of predicting the maximum incidences during three epidemic periods (alternatively, cumulative incidences over the periods are used to quantify the dependent variable) and in a time-continuous sense. Thus, districts with high AfD popularity rank on top in the time-average regarding COVID-19 incidence. The impact of the popularity of the Free Democrats (FDP) is markedly intermittent in the course of time showing two pronounced peaks in incidence but also occasional drops. A moderate risk emanates from popularities of the Green Party (GRÜNE) and the Christian Democratic Union (CDU/CSU) compared to the other parties with lowest risk level. In order to effectively combat the COVID-19 epidemic, public health policymakers are well-advised to account for social attitudes and behavioral patterns reflected in local popularities of political parties, which are conceived as proper surrogates for these attitudes. Whilst causal relations between social attitudes and the presence of parties remain obscure, the political landscape in terms of share of votes constitutes at least viable predictive "markers" relevant for public health policy making.

Seroconversion rate and socio-economic and ethnic risk factors for SARS-CoV-2 infection in children in a population-based cohort, Germany, June 2020 to February 2021.

IntroductionSocio-economic and ethnic background have been discussed as possible risk factors for SARS-CoV-2 infections in children. Improved knowledge could lead to tailored prevention strategies and help improve infection control.AimWe aimed to identify risk factors for SARS-CoV-2 infections in children in the first and second wave of the pandemic.MethodsWe performed an observational population-based cohort study in children (6 months-18 years) scheduled for legally required preventive examination and their parents in a metropolitan region in Germany. Primary endpoint was the SARS-CoV-2 seroconversion rate during the study period. Risk factors assessed included age, pre-existing medical conditions, socio-economic factors and ethnicity.ResultsWe included 2,124 children and their parents. Seroconversion rates among children in all age groups increased 3-4-fold from June 2020 to February 2021. Only 24 of 58 (41%) seropositive children reported symptoms. In 51% of infected children, at least one parent was also SARS-CoV-2-positive. Low level of parental education (OR = 3.13; 95% CI: 0.72-13.69) non-significantly increased the risk of infection. Of the total cohort, 38.5% had a migration background, 9% of Turkish and 5% of Middle Eastern origin, and had the highest risk for SARS-CoV-2 infections (OR = 6.24; 95% CI: 1.38-28.12 and OR = 6.44 (95% CI: 1.14-36.45) after adjustment for other risk factors.ConclusionIn the second half of 2020, seroprevalence for SARS-CoV-2 in children increased especially in families with lower-socioeconomic status. Culture-sensitive approaches are essential to limit transmission and could serve as a blueprint for vaccination strategies.

Longitudinal Rise in Seroprevalence of SARS-CoV-2 Infections in Children in Western Germany-A Blind Spot in Epidemiology?

SARS-CoV-2 infection rates in children and adolescents are often underestimated due to asymptomatic or oligosymptomatic infections. Seroprevalence studies can reveal the magnitude of "silent" infections in this age group and help to assess the risk of infection for children but also their role in spreading the disease. In total, 2045 children and their parents from the Ruhr region were finally included after the exclusion of drop-outs. Seroconversion rates among children of all age groups increased from 0.5% to 8% during the study period and were about three to fourfold higher than the officially registered PCR-based infection rates. Only 41% recalled symptoms of infection; 59% were asymptomatic. In 51% of the infected children, at least one parent also developed SARS-CoV-2 antibodies. Depending on local incidences, the rates of seroconversion rose to different levels during the study period. Although the dynamics of infection within the study cohort mirrors local incidence, the figure of SARS-CoV-2 infections in children and adolescents appears to be high. Reported contact with SARS-CoV-2-infected individuals in the same household carries a high risk of infection.

A Kinetic Response Model for Standardized Regression Analyses of Inflammation-Triggered Hypothermic Body Temperature-Time Courses in Mice.

LPS is frequently used to induce experimental endotoxic shock, representing a standard model of acute inflammation in mice. The resulting inflammatory response leads to hypothermia of the experimental animals, which in turn can be used as surrogate for the severity of systemic inflammation. Although increasingly applied as a humane endpoint in murine studies, differences between obtained temperature-time curves are typically evaluated at a single time point with t-tests or ANOVA analyses. We hypothesized that analyses of the entire temperature-time curves using a kinetic response model could fit the data, which show a temperature decrease followed by a tendency to return to normal temperature, and could increase the statistical power. Using temperature-time curves obtained from LPS stimulated mice, we derived a biologically motivated kinetic response model based on a differential equation. The kinetic model includes four parameters: (i) normal body temperature (T n ), (ii) a coefficient related to the force of temperature autoregulation (r), (iii) damage strength (p 0), and (iv) clearance rate (k). Kinetic modeling of temperature-time curves obtained from LPS stimulated mice is feasible and leads to a high goodness-of-fit. Here, modifying key enzymes of inflammatory cascades induced a dominant impact of genotypes on the damage strength and a weak impact on the clearance rate. Using a likelihood-ratio test to compare modeled curves of different experimental groups yields strongly enhanced statistical power compared to pairwise t-tests of single temperature time points. Taken together, the kinetic model presented in this study has several advantages compared to simple analysis of individual time points and therefore may be used as a standard method for assessing inflammation-triggered hypothermic response curves in mice.

Exploring COVID-19 Daily Records of Diagnosed Cases and Fatalities Based on Simple Nonparametric Methods.

Containment strategies to combat epidemics such as SARS-CoV-2/COVID-19 require the availability of epidemiological parameters, e.g., the effective reproduction number. Parametric models such as the commonly used susceptible-infected-removed (SIR) compartment models fitted to observed incidence time series have limitations due to the time-dependency of the parameters. Furthermore, fatalities are delayed with respect to the counts of new cases, and the reproduction cycle leads to periodic patterns in incidence time series. Therefore, based on comprehensible nonparametric methods including time-delay correlation analyses, estimates of crucial parameters that characterise the COVID-19 pandemic with a focus on the German epidemic are presented using publicly available time-series data on prevalence and fatalities. The estimates for Germany are compared with the results for seven other countries (France, Italy, the United States of America, the United Kingdom, Spain, Switzerland, and Brazil). The duration from diagnosis to death resulting from delay-time correlations turns out to be 13 days with high accuracy for Germany and Switzerland. For the other countries, the time-to-death durations have wider confidence intervals. With respect to the German data, the two time series of new cases and fatalities exhibit a strong coherence. Based on the time lag between diagnoses and deaths, properly delayed asymptotic as well as instantaneous fatality-case ratios are calculated. The temporal median of the instantaneous fatality-case ratio with time lag of 13 days between cases and deaths for Germany turns out to be 0.02. Time courses of asymptotic fatality-case ratios are presented for other countries, which substantially differ during the first half of the pandemic but converge to a narrow range with standard deviation 0.0057 and mean 0.024. Similar results are obtained from comparing time courses of instantaneous fatality-case ratios with optimal delay for the 8 exemplarily chosen countries. The basic reproduction number, R0, for Germany is estimated to be between 2.4 and 3.4 depending on the generation time, which is estimated based on a delay autocorrelation analysis. Resonances at about 4 days and 7 days are observed, partially attributable to weekly periodicity of sampling. The instantaneous (time-dependent) reproduction number is estimated from the incident (counts of new) cases, thus allowing us to infer the temporal behaviour of the reproduction number during the epidemic course. The time course of the reproduction number turns out to be consistent with the time-dependent per capita growth.

Mathematical basis for the assessment of antibiotic resistance and administrative counter-strategies.

Diversity as well as temporal and spatial changes of the proportional abundances of different antibiotics (cycling, mixing or combinations thereof) have been hypothesised to be an effective administrative control strategy in hospitals to reduce the prevalence of antibiotic-resistant pathogens in nosocomial or community-acquired infections. However, a rigorous assessment of the efficacy of these control strategies is lacking. The main purpose here is to present a mathematical framework for the assessment of control stategies from a processual stance. To this end, we adopt diverse measures of heterogeneity and diversity of proportional abundances based on the concept of entropy from other fields and adapt them to the needs in assessing the impact of variations in antibiotic consumption on antibiotic resistance. Thereby, we derive a family of diversity measures whose members exhibit different degrees of complexity. Most important, we extent these measures such that they account for the assessment of temporal changes in heterogeneity including otherwise undetected diversity-invariant permutations of antibiotics consumption and prevalence of resistant pathogens. We apply a correlation analysis for the assessment of associations between changes of heterogeneities on the antibiotics and on the pathogen side. As a showcase, which serves as a proof-of-principle, we apply the derived methods to records of antibiotic consumption and prevalence of antibiotic-resistant germs from University Hospital Dresden (cf. supplement "DiebnerEtAl_Data-Supplement"). Besides the quantification of heterogeneities of antibiotics consumption and antibiotic resistance, we show that a reduction of prevalence of antibiotic-resistant germs correlates with a temporal change of similarity with respect to the first observation of antibiotics consumption, although heterogeneity remains approximately constant. Although an interventional study is pending, our mathematical framework turns out to be a viable concept for the assessment and optimisation of control strategies intended to reduce antibiotic resistance.

Non-canonical Caspase-1 Signaling Drives RIP2-Dependent and TNF-α-Mediated Inflammation In Vivo.

Pro-inflammatory caspase-1 is a key player in innate immunity. Caspase-1 processes interleukin (IL)-1ß and IL-18 to their mature forms and triggers pyroptosis. These caspase-1 functions are linked to its enzymatic activity. However, loss-of-function missense mutations in CASP1 do not prevent autoinflammation in patients, despite decreased IL-1ß production. In vitro data suggest that enzymatically inactive caspase-1 drives inflammation via enhanced nuclear factor κB (NF-κB) activation, independent of IL-1ß processing. Here, we report two mouse models of enzymatically inactive caspase-1-C284A, demonstrating the relevance of this pathway in vivo. In contrast to Casp1-/- mice, caspase-1-C284A mice show pronounced hypothermia and increased levels of the pro-inflammatory cytokines tumor necrosis factor alpha (TNF-α) and IL-6 when challenged with lipopolysaccharide (LPS). Caspase-1-C284A signaling is RIP2 dependent and mediated by TNF-α but independent of the NLRP3 inflammasome. LPS-stimulated whole blood from patients carrying loss-of-function missense mutations in CASP1 secretes higher amounts of TNF-α. Taken together, these results reveal non-canonical caspase-1 signaling in vivo.

Metabolism is the tie: The Bertalanffy-type cancer growth model as common denominator of various modelling approaches.

Cancer or tumour growth has been addressed from a variety of mathematical modelling perspectives in the past. Examples are single variable growth models, reaction diffusion models, compartment models, individual cell-based models, clonal competition models, to name only a few. In this paper, we show that the so called Bertalanffy-type growth model is a macroscopic model variant that can be conceived as an optimal condensed modelling approach that to a high degree preserves complexity with respect to the aforementioned more complex modelling variants. The derivation of the Bertalanffy-type model is crucially based on features of metabolism. Therefore, this model contains a shape parameter that can be interpreted as a resource utilisation efficiency. This shape parameter reflects features that are usually captured in much more complex models. To be specific, the shape parameter is related to morphological structures of tumours, which in turn depend on metabolic conditions. We, furthermore, show that a single variable variant of the Bertalanffy-type model can straightforwardly be extended to a multiclonal competition model. Since competition is crucially based on available shared or clone-specific resources, the metabolism-based approach is an obvious candidate to capture clonal competition. Depending on the specific context, metabolic reprogramming or other oncogene driven changes either lead to a suppression of cancer cells or to an improved competition resulting in outgrowth of tumours. The parametrisation of the Bertalanffy-type growth model allows to account for this observed variety of cancer characteristics. The shape parameter, conceived as a classifier for healthy and oncogenic phenotypes, supplies a link to survival and evolutionary stability concepts discussed in demographic studies, such as opportunistic versus equilibrium strategies.

Concepts in mature T-cell lymphomas - highlights from an international joint symposium on T-cell immunology and oncology.

Growing attention in mature T-cell lymphomas/leukemias (MTCL) is committed to more accurate and meaningful classifications, improved pathogenetic concepts and expanded therapeutic options. This requires considerations of the immunologic concepts of T-cell homeostasis and the specifics of T-cell receptor (TCR) affinities and signaling. Scientists from various disciplines established the CONTROL-T research unit and in an international conference on MTCL they brought together experts from T-cell immunity, oncology, immunotherapy and systems biology. We report here meeting highlights on the covered topics of diagnostic pitfalls, implications by the new WHO classification, insights from discovered genomic lesions as well as TCR-centric concepts of cellular dynamics in host defense, auto-immunity and tumorigenic clonal escape, including predictions to be derived from in vivo imaging and mathematical modeling. Presentations on novel treatment approaches were supplemented by strategies of optimizing T-cell immunotherapies. Work packages, that in joint efforts would advance the field of MTCL more efficiently, are identified.

Process-based approach to modeling recurrent-event data explicated on the basis of occurrences of tooth losses in two different prosthetic treatment concepts.

BACKGROUND: In studies comparing different prosthetic treatment concepts the repeated loss of teeth was chosen as the primary outcome. The resulting data appear to represent a data structure of recurrent events. However, the application of an existing method for recurrent events is far from straightforward. Often only the first event or the final state is analyzed using Kaplan-Meier survival statistics, thereby giving a great deal of information away. METHODS: The paper presents a strategy for the analysis of recurrent data using a previously published study on the influence of different prosthetic treatment concepts for the shortened dental arch on tooth loss. A method based on cumulative sample history functions of recurrent events was adjusted for tooth loss. The shapes of these cumulative functions suggest a time dependency of the recurrence rate. To keep the model as simple as possible, a tripartite Poisson process (which assumes piecewise time-independent rates) was fitted to the cumulative mean functions stratified by treatment. RESULTS: Within the middle interval of the three-phasic process, the treatment effects differ significantly, which is interpreted as a delay of tooth loss due to the use of one type of prosthesis (fixed) compared with the other (removable). CONCLUSIONS: An analysis based on cumulative history functions is based on process, therefore, temporally changing characteristics are better captured than in methods for survival analyses. The presented approach offers useful new insight into the temporal behavior of ongoing tooth loss after prosthetic treatment. TRIAL REGISTRATION: The trial has been registered at controlled-trials.com under ISRCTN97265367 (registration date 4 April 2008).

An evolutionary stability perspective on oncogenesis control in mature T-cell populations.

Here we present a mathematical model for the dynamics of oncogenesis control in mature T-cell populations within the blood and lymphatic system. T-cell homeostasis is maintained by clonal competition for trophic niches (survival signals stimulated through interactions with self-antigens bound to major histocompatibility molecules), where a clone is defined as the set of T cells carrying the same antigen specific T-cell receptor (TCR). We analytically derive fitness functions of healthy and leukemic clone variants, respectively, that capture the dependency of the stability of the healthy T-cell pool against leukemic invaders on clonal diversity and kinetic parameters. Similar to the stability of ecosystems with high biodiversity, leukemic mutants are suppressed within polyclonal T-cell populations, i.e., in the presence of a huge number of different TCRs. To the contrary, for a low clonal diversity the leukemic clone variants are able to invade the healthy T-cell pool. The model, therefore, describes the experimentally observed phenomenon that preleukemic clone variants prevail in quasi-monoclonal experimental settings (in mice), whereas in polyclonal settings the healthy TCR variants are able to suppress the outgrowth of tumours. Between the two extremal situations of mono- and polyclonality there exists a range of coexistence of healthy and oncogenic clone variants with moderate fitness (stability) each. A variation of cell cycle times considerably changes the dynamics within this coexistence region. Faster proliferating variants increase their chance to dominate. Finally, a simplified niche variation scheme illustrates a possible mechanism to increase clonal T-cell diversity given a small niche diversity.

A simulating cognitive system with adaptive capability.

Dedicated to the memory of Michael Conrad, this paper builds on his seminal ideas expressed in his famous book Adaptability, as well as in his later works. We investigate a recently published adaptive system for the instantaneous recognition of dynamics with respect to its adaptability to the Lorenz system. The system consists of a pool of internal dynamical elements. These elements are defined through a set of parameter values that encode for a specific dynamics behavior. If the system is now faced with an unknown external dynamics-unknown with respect to the parameter-it is capable not only to recognize the dynamics but also to adapt to the correct dynamics, which in turn leads to a simulation capability. The system impressively quickly follows the sudden qualitative changes of the external dynamics. The adaptation works even quicker when the correct dynamics are already represented within the internal pool. This leads to the idea of memorizing the represented dynamics within the pool, whereby the elements that correspond to rarely externally presented dynamics can be given free for the adaptation and memorization of more frequently presented dynamics.