Die Entwicklung des Geldvermögens der privaten Haushalte in Deutschland

Diese Studie untersucht, wie sich Spartätigkeit und Geldvermögen deutscher Privathaushalte seit 1960 entwickelt haben. Mittels Regressionsanalysen läßt sich herausfinden, daß der Vermögensbestand einkommens- und altersabhängig ist. Es Wird gezeigt, daß Einkommen und Ersparnis kointegriert sind, weshalb ein Fehlerkorrekturmodell aufgestellt wird. Bei einzelnen Anlageformen können im Zeitverlauf starke Schwankungen m der Ersparnisbildung festgestellt werden, die u.a. durch Renditeaspekte erklärt werden. -- The study surveys, how savings and monetary assets of private households m Germany developed since 1960. Linear regressions indicate that one person's assets depend on its income and age. lt is shown furthermore that income and savings are co-integrated, which leads to an error correction model. Savings in different categories of investment show great fluctuations during time, which is explained by their yields.

A review of mathematical models of influenza A infections within a host or cell culture: lessons learned and challenges ahead

Most mathematical models used to study the dynamics of influenza A have thus far focused on the between-host population level, with the aim to inform public health decisions regarding issues such as drug and social distancing intervention strategies, antiviral stockpiling or vaccine distribution. Here, we investigate mathematical modeling of influenza infection spread at a different scale; namely that occurring within an individual host or a cell culture. We review the models that have been developed in the last decades and discuss their contributions to our understanding of the dynamics of influenza infections. We review kinetic parameters (e.g., viral clearance rate, lifespan of infected cells) and values obtained through fitting mathematical models, and contrast them with values obtained directly from experiments. We explore the symbiotic role of mathematical models and experimental assays in improving our quantitative understanding of influenza infection dynamics. We also discuss the challenges in developing better, more comprehensive models for the course of influenza infections within a host or cell culture. Finally, we explain the contributions of such modeling efforts to important public health issues, and suggest future modeling studies that can help to address additional questions relevant to public health

The impact of population size on the evolution of asexual microbes on smooth versus rugged fitness landscapes

<p>Abstract</p> <p>Background</p> <p>It is commonly thought that large asexual populations evolve more rapidly than smaller ones, due to their increased rate of beneficial mutations. Less clear is how population size influences the level of fitness an asexual population can attain. Here, we simulate the evolution of bacteria in repeated serial passage experiments to explore how features such as fitness landscape ruggedness, the size of the mutational target under selection, and the mutation supply rate, interact to affect the evolution of microbial populations of different sizes.</p> <p>Results</p> <p>We find that if the fitness landscape has many local peaks, there can be a trade-off between the rate of adaptation and the potential to reach high fitness peaks. This result derives from the fact that whereas large populations evolve mostly deterministically and often become trapped on local fitness peaks, smaller populations can follow more stochastic evolutionary paths and thus locate higher fitness peaks. We also find that the target size of adaptation and the mutation rate interact with population size to influence the trade-off between rate of adaptation and final fitness.</p> <p>Conclusion</p> <p>Our study suggests that the optimal population size for adaptation depends on the details of the environment and on the importance of either the ability to evolve rapidly or to reach high fitness levels.</p

Correction: The Role of Compensatory Mutations in the Emergence of Drug Resistance

Pathogens that evolve resistance to drugs usually have reduced fitness. However, mutations that largely compensate for this reduction in fitness often arise. We investigate how these compensatory mutations affect population-wide resistance emergence as a function of drug treatment. Using a model of gonorrhea transmission dynamics, we obtain generally applicable, qualitative results that show how compensatory mutations lead to more likely and faster resistance emergence. We further show that resistance emergence depends on the level of drug use in a strongly nonlinear fashion. We also discuss what data need to be obtained to allow future quantitative predictions of resistance emergence

Nonnormality and the localized control of extended systems

The idea of controlling the dynamics of spatially extended systems using a small number of localized perturbations is very appealing - such a setup is easy to implement in practice. However, when the distance between controllers generating the perturbations becomes large, control fails due to increasing sensitivity of the system to noise and nonlinearities. We show that this failure is due to the fact that the evolution operator for the controlled system becomes increasingly nonnormal as the distance between controllers grows. This nonnormality is the result of control and can arise even for systems whose evolution operator is normal in the absence of control.Comment: 4 pages, 4 figure

SARS-CoV-2 Viral and Serological Testing When College Campuses Reopen: Some Practical Considerations

The coronavirus disease 2019 (COVID-19) pandemic prompted universities across the United States to close campuses in Spring 2020. Universities are deliberating whether, when, and how they should resume in-person instruction in Fall 2020. In this essay, we discuss some practical considerations for the use of 2 potentially useful control strategies based on testing: (1) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reverse transcriptase-polymerase chain reaction (RT-PCR) testing followed by case-patient isolation and quarantine of close contacts, and (2) serological testing followed by an “immune shield” approach, that is, low social distancing requirements for seropositive persons. The isolation of case-patients and quarantine of close contactsmay be especially challenging, and perhaps prohibitively difficult, on many university campuses. The “immune shield” strategy might be hobbled by a low positive predictive value of the tests used in populations with low seroprevalence. Both strategies carry logistical, ethical, and financial implications. The main nonpharmaceutical interventions will remain methods based on social distancing (eg, capping class size) and personal protective behaviors (eg, universal facemask wearing in public space) until vaccines become available, or unless the issues discussed herein can be resolved in such a way that using mass testing as main control strategies becomes viable

Neuraminidase Inhibitor Resistance in Influenza: Assessing the Danger of Its Generation and Spread

Neuraminidase Inhibitors (NI) are currently the most effective drugs against influenza. Recent cases of NI resistance are a cause for concern. To assess the danger of NI resistance, a number of studies have reported the fraction of treated patients from which resistant strains could be isolated. Unfortunately, those results strongly depend on the details of the experimental protocol. Additionally, knowing the fraction of patients harboring resistance is not too useful by itself. Instead, we want to know how likely it is that an infected patient can generate a resistant infection in a secondary host, and how likely it is that the resistant strain subsequently spreads. While estimates for these parameters can often be obtained from epidemiological data, such data is lacking for NI resistance in influenza. Here, we use an approach that does not rely on epidemiological data. Instead, we combine data from influenza infections of human volunteers with a mathematical framework that allows estimation of the parameters that govern the initial generation and subsequent spread of resistance. We show how these parameters are influenced by changes in drug efficacy, timing of treatment, fitness of the resistant strain, and details of virus and immune system dynamics. Our study provides estimates for parameters that can be directly used in mathematical and computational models to study how NI usage might lead to the emergence and spread of resistance in the population. We find that the initial generation of resistant cases is most likely lower than the fraction of resistant cases reported. However, we also show that the results depend strongly on the details of the within-host dynamics of influenza infections, and most importantly, the role the immune system plays. Better knowledge of the quantitative dynamics of the immune response during influenza infections will be crucial to further improve the results

An attempt to reproduce a previous meta-analysis and a new analysis regarding the impact of directly observed therapy on tuberculosis treatment outcomes

Directly observed therapy (DOT) is almost universally used for the treatment of TB. Several meta-analyses using different methods have assessed the effectiveness of DOT compared to self-administered therapy (SAT). The results of these meta-analyses often conflict with some concluding DOT is superior and others that there is little or no difference. Meta-analyses can guide policymaking, but such analyses must be reliable. To assess the validity of a previous meta-analysis, we tried to reproduce it. We encountered problems with the previous analysis that did not allow for a meaningful reproduction. We describe the issues we encountered here. We then performed a new meta-analysis comparing the treatment outcomes of adults given treatment with SAT versus DOT. Outcomes in the new analysis are loss to follow-up, treatment failure, cure, treatment completed, and all-cause mortality. All data, documentation, and code used to generate our results is provided. Our new analysis included four randomized and three observational studies with 1603 and 1626 individuals respectively. The pooled relative risks (RR) are as follows: Lost to follow-up (RR = 1.2, 95% CI 0.9, 1.7), Treatment Failure (RR = 1.1, 95% CI 0.6, 2), Cure (RR = 0.9, 95% CI 0.8, 1.1), Treatment Completion (RR = 1, 95% CI 0.9, 1.1), Mortality (RR = 0.9, 95% CI 0.6, 1.3). Based on data from our new meta-analysis, the magnitude of the difference between DOT and SAT for all reported outcomes is small, and none of the differences are statistically significant