In this paper, we identify some conditions to compare the largest order statistics from resilience-scale models with reduced scale parameters in the sense of mean residual life order. As an example of the established result, the exponentiated generalized gamma distribution is examined. Also, for the special case of the scale model, power-generalized Weibull and half-normal distributions are investigated.

This paper is devoted to the study of the asset allocation problem for a DC pension plan with minimum guarantee constraint in a hidden Markov regime-switching economy. Suppose that four types of assets are available in the financial market: a risk-free asset, a zero-coupon bond, an inflation-indexed bond and a stock. The expected return rate of the stock depends on unobservable economic states, and the change of states is described by a hidden Markov chain. In addition, the CIR process is used to describe the evolution of the nominal interest rate. The contribution rate is also assumed to be stochastic. The goal of investment management is to minimize the convex risk measure of the terminal wealth in excess of the minimum guarantee constraint. First, we transform the partially observable optimization problem into the one with complete information using the Wonham filtering technique and deal with the minimum guarantee constraint by constructing auxiliary processes. Furthermore, we derive the optimal investment strategy by the BSDE approach. Finally, some numerical results are presented to illustrate the impacts of some important parameters on investment behaviors.

We study a multivariate system over a finite lifespan represented by a Hermitian-valued random matrix process whose eigenvalues (i) interact in a mean-field way and (ii) converge to their weighted ensemble average at their terminal time. We prove that such a system is guaranteed to converge in time to the identity matrix that is scaled by a Gaussian random variable whose variance is inversely proportional to the dimension of the matrix. As the size of the system grows asymptotically, the eigenvalues tend to mutually independent diffusions that converge to zero at their terminal time, a Brownian bridge being the archetypal example. Unlike commonly studied random matrices that have non-colliding eigenvalues, the proposed eigenvalues of the given system here may collide. We provide the dynamics of the eigenvalue gap matrix, which is a random skew-symmetric matrix that converges in time to the $\textbf{0}$ matrix. Our framework can be applied in producing mean-field interacting counterparts of stochastic quantum reduction models for which the convergence points are determined with respect to the average state of the entire composite system.

Following the report of the first COVID-19 case in Nepal on 23 January 2020, three major waves were documented between 2020 and 2021. By the end of July 2022, 986 596 cases of confirmed COVID-19 and 11 967 deaths had been reported and 70.5% of the population had received at least two doses of a COVID-19 vaccine. Prior to the pandemic, a large dengue virus (DENV) epidemic affected 68 out of 77 districts, with 17 932 cases and six deaths recorded in 2019. In contrast, the country's Epidemiology and Disease Control Division reported 530 and 540 dengue cases in the pandemic period (2020 and 2021), respectively. Furthermore, Kathmandu reported just 63 dengue cases during 2020 and 2021, significantly lower than the 1463 cases reported in 2019. Serological assay showed 3.2% positivity rates for anti-dengue immunoglobulin M antibodies during the pandemic period, contrasting with 26.9–40% prior to it. Real-time polymerase chain reaction for DENV showed a 0.5% positive rate during the COVID-19 pandemic which is far lower than the 57.0% recorded in 2019. Continuing analyses of dengue incidence and further strengthening of surveillance and collaboration at the regional and international levels are required to fully understand whether the reduction in dengue incidence/transmission were caused by movement restrictions during the COVID-19 pandemic.

In this study, a non-linear deterministic model for the transmission dynamics of skin sores (impetigo) disease is developed and analysed by the help of stability of differential equations. Some basic properties of the model including existence and positivity as well as boundedness of the solutions of the model are investigated. The disease-free and endemic equilibrium were investigated, as well as the basic reproduction number, R0, also calculated using the next-generation matrix approach. When R0 < 1, the model's stability analysis reveals that the system is asymptotically stable at disease-free critical point globally as well as locally. If R0 > 1, the system is asymptotically stable at disease-endemic equilibrium both locally and globally. The long-term behaviour of the skin sores model's steady-state solution in a population is investigated using numerical simulations of the model.

Mass gatherings (MG) present a number of challenges to public health authorities and governments across the world with sporting events, tournaments, music festivals, religious gatherings and all other MG having historically posed a risk to the spread and amplification of a range of infectious diseases. Transmission of gastrointestinal, respiratory, waterborne and sexually transmitted infectious diseases pose a particular risk: all have been linked to MG events [1–4]. Infection risk often depends on the nature of the mass gathering, and on the profile and behaviour of its participants. The interaction between environmental, psychological, biological and social factors plays a vital part. The risk of outbreaks particularly as a result of respiratory transmission remains high at MG, with the majority of outbreaks over the last two decades resulting from a variety of respiratory and vaccine preventable pathogens [5–7]. Concerns about the spread of infectious diseases at MG are often focussed on crowding, lack of sanitation and the mixing of population groups from different places. Sporting events, which have in recent decades become more complex and international in nature, pose a challenge to the control of communicable disease transmission [8]. Despite this, large scale outbreaks at sporting events have been rare in recent decades, particularly since the rise of more robust public health planning, prevention, risk assessment and improved health infrastructures in host countries [9].

Bacterial survival on, and interactions with, human skin may explain the epidemiological success of MRSA strains. We evaluated the bacterial counts for 27 epidemic and 31 sporadic MRSA strains on 3D epidermal models based on N/TERT cells (NEMs) after 1, 2 and 8 days. In addition, the expression of antimicrobial peptides (hBD-2, RNase 7), inflammatory cytokines (IL-1β, IL-6) and chemokine IL-8 by NEMs was assessed using immunoassays and the expression of 43 S. aureus virulence factors was determined by a multiplex competitive Luminex assay. To explore donor variation, bacterial counts for five epidemic and seven sporadic MRSA strains were determined on 3D primary keratinocyte models (LEMs) from three human donors. Bacterial survival was comparable on NEMs between the two groups, but on LEMs, sporadic strains showed significantly lower survival numbers compared to epidemic strains. Both groups triggered the expression of immune factors. Upon interaction with NEMs, only the epidemic MRSA strains expressed pore-forming toxins, including alpha-hemolysin (Hla), gamma-hemolysin (HlgB), Panton-Valentine leucocidin (LukS) and LukED. Together, these data indicate that the outcome of the interaction between MRSA and human skin mimics, depends on the unique combination of bacterial strain and host factors.

From 2016–2019, dry bulb onions were the suspected cause of three multistate outbreaks in the United States. We investigated a large multistate outbreak of Salmonella Newport infections that caused illnesses in both the United States and Canada in 2020. Epidemiologic, laboratory and traceback investigations were conducted to determine the source of the infections, and data were shared among U.S. and Canadian public health officials. We identified 1127 U.S. illnesses from 48 states with illness onset dates ranging from 19 June to 11 September 2020. Sixty-six per cent of ill people reported consuming red onions in the week before illness onset. Thirty-five illness sub-clusters were identified during the investigation and seventy-four per cent of sub-clusters served red onions to customers during the exposure period. Traceback for the source of onions in illness sub-clusters identified a common onion grower in Bakersfield, CA as the source of red onions, and onions were recalled at this time. Although other strains of Salmonella Newport were identified in environmental samples collected at the Bakersfield, CA grower, extensive environmental and product testing did not yield the outbreak strain. This was the third largest U.S. foodborne Salmonella outbreak in the last 30 years. It is the first U.S. multistate outbreak with a confirmed link to dry bulb onions, and it was nearly 10-fold larger than prior outbreaks with a suspected link to onions. This outbreak is notable for its size and scope, as well as the international data sharing that led to implication of red onions as the primary cause of the outbreak. Although an environmental assessment at the grower identified several factors that likely contributed to the outbreak, no main reason was identified. The expedient identification of the outbreak vehicle and response of multiple public health agencies allowed for recall and removal of product from the marketplace, and rapid messaging to both the public and industry on actions to protect consumers; these features contributed to a decrease in cases and expeditious conclusion of the outbreak.

The existence of moments of first downward passage times of a spectrally negative Lévy process is governed by the general dynamics of the Lévy process, i.e. whether it is drifting to $+\infty$, $-\infty$, or oscillating. Whenever the Lévy process drifts to $+\infty$, we prove that the $\kappa$th moment of the first passage time (conditioned to be finite) exists if and only if the $(\kappa+1)$th moment of the Lévy jump measure exists. This generalizes a result shown earlier by Delbaen for Cramér–Lundberg risk processes. Whenever the Lévy process drifts to $-\infty$, we prove that all moments of the first passage time exist, while for an oscillating Lévy process we derive conditions for non-existence of the moments, and in particular we show that no integer moments exist.

Coronavirus disease 2019 (COVID-19) has been described as having an overdispersed offspring distribution, i.e. high variation in the number of secondary transmissions of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) per single primary COVID-19 case. Accordingly, countermeasures focused on high-risk settings and contact tracing could efficiently reduce secondary transmissions. However, as variants of concern with elevated transmissibility continue to emerge, controlling COVID-19 with such focused approaches has become difficult. It is vital to quantify temporal variations in the offspring distribution dispersibility. Here, we investigated offspring distributions for periods when the ancestral variant was still dominant (summer, 2020; wave 2) and when Alpha variant (B.1.1.7) was prevailing (spring, 2021; wave 4). The dispersion parameter (k) was estimated by analysing contact tracing data and fitting a negative binomial distribution to empirically observed offspring distributions from Nagano, Japan. The offspring distribution was less dispersed in wave 4 (k = 0.32; 95% confidence interval (CI) 0.24–0.43) than in wave 2 (k = 0.21 (95% CI 0.13–0.36)). A high proportion of household transmission was observed in wave 4, although the proportion of secondary transmissions generating more than five secondary cases did not vary over time. With this decreased variation, the effectiveness of risk group-focused interventions may be diminished.

During 6 weeks in February–March 2021, the Dutch municipal health service Utrecht studied the epidemiological effects on test incidence and the detection of acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with mass testing (MT). During MT, inhabitants of Bunschoten could repeatedly test regardless of symptoms and as often as desired at the close-by test facilities in the municipality. Data from the regular COVID-19 registration was used for analysis. In Bunschoten, MT caused a significant increase in test incidence and an immediate increase in the number of detected active infections, in contrast to a stabilisation in the rest of the province of Utrecht. Age distribution of test incidence shifted to the older population in Bunschoten during MT. During MT, there was a 6.8 percentage point increase in detected asymptomatic cases, a 0.4 percentage point increase in pre-symptomatic cases and a decrease of 0.5 days between onset of symptoms and test date. This study has shown that MT increases test incidence and helps to obtain a more complete view of the presence of SARS-CoV-2 in a community, which can be useful in specific situations with a defined target group or goal. However, the question remains open whether the use of MT is proportionate to the overall gain.

We consider a Lévy process Y(t) that is not continuously observed, but rather inspected at Poisson($\omega$) moments only, over an exponentially distributed time $T_\beta$ with parameter $\beta$. The focus lies on the analysis of the distribution of the running maximum at such inspection moments up to $T_\beta$, denoted by $Y_{\beta,\omega}$. Our main result is a decomposition: we derive a remarkable distributional equality that contains $Y_{\beta,\omega}$ as well as the running maximum process $\bar Y(t)$ at the exponentially distributed times $T_\beta$ and $T_{\beta+\omega}$. Concretely, $\overline{Y}(T_\beta)$ can be written as the sum of two independent random variables that are distributed as $Y_{\beta,\omega}$ and $\overline{Y}(T_{\beta+\omega})$. The distribution of $Y_{\beta,\omega}$ can be identified more explicitly in the two special cases of a spectrally positive and a spectrally negative Lévy process. As an illustrative example of the potential of our results, we show how to determine the asymptotic behavior of the bankruptcy probability in the Cramér–Lundberg insurance risk model.

Research that examines the impact of economic, social, and political factors on political corruption uses expert’ and citizen’ perceptions for measuring corruption and testing arguments. Scholars argue that the perception of corruption is a good proxy for actual corruption because data on actual corruption are limited and not entirely trustworthy. However, perception indexes do not allow for testing separate mechanisms driving citizen’ perceptions of corruption from actual levels of corruption in different government branches. To address this issue, I introduce a new index based on Latin American countries to measure the risk of corruption in political parties. Using a de jure analysis of laws and regulations, the Risk of Corruption (ROC) index evaluates the likelihood of political parties engaging in corrupt activities. Instead of measuring corrupt activities or perception directly, the ROC measures the risks of involving in corruption. The index has important implications for academics and practitioners in anti-corruption issues. First, it allows us to test arguments about the role of political parties and legislatures in reducing political corruption. Second, it helps to understand how political parties could improve their internal organization to decrease the risk of corrupt activities. Finally, it is a valuable instrument for cross-national studies in diverse fields that study political parties.

Model-based systems engineering (MBSE) aims at creating a model of a system under development, covering the complete system with a level of detail that allows to define and understand its behavior and enables to define any interface and work package based on the model. Once the model is established, further benefits can be reaped, such as the analysis of complex technical correlations within the system. Various insights can be gained by displaying the model as a formal graph and querying it. To enable such queries, a graph schema is necessary, which allows to transfer the model into a graph database. In the course of this paper, we discuss the design of a graph schema and MBSE modeling approach, enabling deep going system analysis and anomaly resolution in complex embedded systems with a focus on testing and anomaly resolution. The schema and modeling approach are designed to answer questions such as What happens if there is an electrical short in a component? Which other components are now offline and which data cannot be gathered anymore? If a component becomes unresponsive, which alternative routes can be established to obtain data processed by it. We build on the use case of qualification and operations of a small spacecraft. Structural elements of the MBSE model are transferred to a graph database where analyses are conducted on the system. The schema is implemented by means of an adapter for MagicDraw to Neo4J. A selection of complex analyses is shown in the example of the MOVE-II space mission.

While many Latin American countries have a tradition of receiving migrants, including the countries selected as case studies, there are no institutionalized mechanisms for the integration and settlement of migrants. The objective of this article is to explore how to improve migration data collection and management in a region that does not have many migration integration policies in place. I assess the state of migration data collection and management in three case studies: the city of Cucuta in Colombia, the North Huetar Region in Costa Rica, and the city of Monterrey in Mexico. The three countries publish data exclusively at the national level, rather than the local or municipal. Despite all case studies having a variety of administrative data, mainly in the form of entries and exits by nationality, these data are not enough to properly identify the sociodemographic characteristics of migrant populations in a country, and much less in specific cities. I make recommendations divided into three main themes to improve migration data in Latin America.

Does digitalization reduce corruption? What are the integrity benefits of government digitalization? While the correlation between digitalization and corruption is well established, there is less actionable evidence on the integrity dividends of specific digitalization reforms on different types of corruption and the policy channels through which they operate. These linkages are especially relevant in high corruption risk environments. This article unbundles the integrity dividends of digital reforms undertaken by governments around the world, accelerated by the pandemic. It analyzes the rise of data-driven integrity analytics as promising tools in the anticorruption space deployed by tech-savvy integrity actors. It also assesses the broader integrity benefits of the digitalization of government services and the automation of bureaucratic processes, which contribute to reducing bribe solicitation risks by front-office bureaucrats. It analyzes in particular the impact of digitalization on social transfers. It argues that government digitalization can be an implicit yet effective anticorruption strategy, with subtler yet deeper effects, but there needs to be greater synergies between digital reforms and anticorruption strategies.

Corruption has pervasive effects on economic development and the well-being of the population. Despite being crucial and necessary, fighting corruption is not an easy task because it is a difficult phenomenon to measure and detect. However, recent advances in the field of artificial intelligence may help in this quest. In this article, we propose the use of machine-learning models to predict municipality-level corruption in a developing country. Using data from disciplinary prosecutions conducted by an anti-corruption agency in Colombia, we trained four canonical models (Random Forests, Gradient Boosting Machine, Lasso, and Neural Networks), and ensemble their predictions, to predict whether or not a mayor will commit acts of corruption. Our models achieve acceptable levels of performance, based on metrics such as the precision and the area under the receiver-operating characteristic curve, demonstrating that these tools are useful in predicting where misbehavior is most likely to occur. Moreover, our feature-importance analysis shows us which groups of variables are most important in predicting corruption.