This paper examines an insurer’s optimal asset allocation and reinsurance policies. The financial market framework includes one risk-free and one risky asset. The insurer has two business lines, where the ordinary claim process is modeled by a compound Poisson process and catastrophic claims follow a compound dynamic contagion process. The dynamic contagion process, which is a generalization of the externally exciting Cox process with shot-noise intensity and the self-exciting Hawkes process, is enhanced by accommodating the dependency structure between the magnitude of contribution to intensity after initial events for catastrophic insurance products and its claim/loss size. We also consider the dependency structure between the positive effect on the intensity and the negative crashes on the risky financial asset when initial events occur. Our objective is to maximize the insurer’s expected utility of terminal surplus. We construct the extended Hamilton–Jacobi–Bellman (HJB) equation using dynamic programming principles to derive an explicit optimal reinsurance policy for ordinary claims. We further develop an iterative scheme for solving the value function and the optimal asset allocation policy and the reinsurance policy for catastrophic claims numerically, providing a rigorous convergence proof. Finally, we present numerical examples to demonstrate the impact of key parameters.

We investigated the potential yield of conducting active case finding for tuberculosis (TB) within a defined geographic radius (50 or 100 m) around the households of individuals diagnosed with TB at health facilities. In a well-defined geographic area within Kampala, Uganda, residential locations were determined for 85 people diagnosed with TB at local health facilities over an 18-month period and for 60 individuals diagnosed with TB during a subsequent community-wide door-to-door screening campaign. Ten of the individuals diagnosed through community screening lived within 50 m of an individual previously diagnosed with TB in a local health facility (TB prevalence: 0.98%), and 15 lived at a distance of 50–100 m (prevalence: 0.87%). The prevalence ratio was 1.4 (95% confidence interval (CI): 0.7–2.9) for those <50 m and 1.2 (95% CI 0.6–2.2) for those 50–100 m, compared to >100 m. Using TB notifications to identify areas for geographically targeted case finding is at most moderately more efficient than screening the general population in the context of urban Uganda.

Attaining the target of <0.1% HBsAg positives in children aged <5 years in vaccinated populations by 2030 is a WHO indicator of hepatitis B elimination. We aimed to calculate the prevalence of HBsAg- and anti-HBc-positive children and adolescents in the low-prevalence country of Germany. In total, 3567 children and adolescents aged 3–17 years participated in a national population based cross-sectional study. Data were collected between 2014 and 2017 using questionnaires and health examinations, including blood samples. Applying a weighted analysis to account for survey design and participant characteristics, we calculated the HBsAg and anti-HBc prevalence and described them by anti-HBs positivity. In total, 3007 participants had all three sero-markers measured. None were found HBsAg and anti-HBc positive. Seven (0.3%, 95% CI: 0.1–0.8) were anti-HBc positive and HBsAg negative; six were also anti-HBs positive. All anti-HBc-positive participants were aged ≥7 years and three had no migration background. Four anti-HBc-positive participants had known vaccination status; three had been vaccinated according to national recommendations. This very low hepatitis B virus sero-prevalence among children and adolescents indicates that Germany is reaching some hepatitis B virus elimination targets. We recommend maintaining preventive measures, in particular a high vaccination coverage, in order to reach hepatitis B elimination.

A company with n geographically widely dispersed sites seeks an insurance policy that pays off if m out of the n sites experience rarely occurring catastrophes (e.g., earthquakes) during a year. This study compares three strategies for an insurance company wishing to offer such an m-out-of-n policy, assuming the existence of markets for insurance on the individual sites with coverage periods of various lengths of a year or less. Strategy A is static: at the beginning of the year it buys a reinsurance policy on each individual site covering the entire year and makes no later adjustments. By contrast, Strategies S and C are dynamic and adaptive, exploiting the availability of individual-site policies for shorter periods than a year to make changes in the coverage on individual sites as quakes occur during the year. Strategy S uses the payoff from reinsurance when a quake occurs at a particular site to increase coverage for the remainder of the year on the sites that have not yet had quakes. Strategy C buys individual-site policies covering successive time periods of fixed length, observing the system at the beginning of each period and using cash on hand plus cash obtained from a reinsurance payoff (if any) during the previous period to decide how much cash to retain and how much reinsurance to purchase for the current period. The study relies on expected utility to determine indifference premiums and compare the premiums and loss probabilities for the three strategies.

The process to better understand the intricate evolution of our urban territories requires combining urban data from different or concurrent instances of time to provide stakeholders with more complete views of possible evolutions of a city. Geospatial rules have been proposed in the past to validate 3D semantic city models, however, there is a lack of research in the validation of multiple, concurrent and successive, scenarios of urban evolution. Using Semantic Web Ontologies and logical rules, we present a novel standards-based methodology for validating integrated city models. Using this methodology, we propose interoperable rules for validating integrated open 3D city snapshots used for representing multiple scenarios of evolution. We also implement a reproducible proof of concept test suite for applying the proposed rules. To illustrate how these contributions can be used in a real-world data validation use-case, we also provide example queries on the validated data. These queries are specifically used to construct a 3D web application for visualizing and analysing urban changes across multiple scenarios of evolution of a selected zone of interest.

In this paper, we investigate asymmetric Nash bargaining in the context of proportional insurance contracts between a risk-averse insured and a risk-averse insurer, both seeking to enhance their expected utilities. We obtain a necessary and sufficient condition for the Pareto optimality of the status quo and derive the optimal Nash bargaining solution when the status quo is Pareto dominated. If the insured’s and the insurer’s risk preference exhibit decreasing absolute risk aversion and the insurer’s initial wealth decreases in the insurable risk in the sense of reversed hazard rate order, we show that both the optimal insurance coverage and the optimal insurance premium increase with the insured’s degree of risk aversion and the insurer’s bargaining power. If the insured’s risk preference further follows constant absolute risk aversion, we find that greater insurance coverage is induced as the insurer’s constant initial wealth increases.