While malaria transmission in coastal East Africa is strongly shaped by climatic variability, few studies examine long-term interactions in rapidly urbanizing settings. This study evaluated the impact of climate and seasonal trends on malaria incidence in Dar es Salaam, Tanzania (2014–2024). Monthly cases and meteorological data were analyzed using seasonal-trend decomposition (STL) and generalized additive models (GAMs) to quantify nonlinear and lagged climatic associations. Over the decade, malaria incidence declined sharply from >130 cases per 10,000 in 2014 to <30 by 2023. However, strong seasonal peaks persisted, with STL revealing consistent annual surges during April–June following the rainy season. GAM analysis identified rainfall as the dominant climatic driver, demonstrating significant 1- and 2-month lagged effects (p < 0.001). Daytime (1-month lag) and night-time (2-month lag) temperatures showed non-linear associations, peaking in incidence at optimal mosquito-development temperatures (~30–31°C). Despite substantial incidence declines, transmission remains highly climate-sensitive. Driven primarily by lagged rainfall and temperature effects rather than current-month conditions, these dynamics underscore the urgent need for climate-informed early warning systems and targeted seasonal interventions in coastal urban environments.

Late HIV diagnosis increases morbidity and mortality. In this retrospective cohort study on the national HIV register, we analysed risk factors for late HIV diagnosis among newly registered people living with HIV (PLWH) between 2008 and 2023, using the updated definition. Of 2683 PLWH registered, 1813 (67.6%) were newly diagnosed with CD4+ T-cell count available ≤90 days for 1572 (86.7%). Eighty-seven of the 609 (14.3%) individuals with CD4+ T-cell count <350/μL had recent infections and were reclassified as non-late. Of the newly diagnosed, 50.3% were diagnosed late. Multivariable analysis identified higher age as an independent risk factor for late diagnosis (adjusted OR 1.42 per ten years, 95% CI 1.30–1.56). Of the Finnish-born, females had lower odds than males (aOR 0.59, 95% CI 0.39–0.88). Asian-born (aOR 6.83, 95% CI 3.49–13.35) and African-born females (aOR 3.26, 95% CI 1.58–6.73) had significantly higher odds than Finnish-born females. In urban municipalities, men who have sex with men had lower odds than individuals with heterosexual transmission (aOR 0.55, 95% CI 0.40–0.76). Higher age was the most important factor for increasing the proportion of late diagnoses. We recommend enhanced testing and risk awareness for older adults and migrants from high-prevalence countries.

The growing availability of information sources has offered central banks new opportunities to enhance their statistical, analytical, and policy functions. By linking—or integrating—various data sets, they have been able to produce more granular, timely, and diverse statistics in a cost-efficient way. These advancements have also enabled a better use of information available in society, such as administrative records, to improve statistical agility in responding to user needs. Yet integrating alternative data—often generated as a by-product of other processes—also raises challenges, including concerns over accuracy, representativeness, and reliability. This paper aims to review systematically the opportunities and limitations of data integration in central banks, taking stock of their experience thus far. Results underscore the need for strengthening the global statistical infrastructure through adequate data governance, management, and public resources.

Recent advances in learning dynamical systems from data have shown significant promise. However, many existing methods assume access to the full state of the system—an assumption that is rarely satisfied in practice, where systems are typically monitored through a limited number of sensors, leading to partial observability. To address this challenge, we draw inspiration from the Mori–Zwanzig formalism, which provides a theoretical connection between hidden variables and memory terms. Motivated by this perspective, we introduce a constant-lag neural delay differential equations (NDDEs) framework, providing a continuous-time approach for learning non-Markovian dynamics directly from data. These memory effects are captured using a finite set of time delays, which are identified via the adjoint method. We validate the proposed approach on a range of datasets, including synthetic systems, chaotic dynamics, and experimental measurements, such as the Kuramoto–Sivashinsky equation and cavity-flow experiments. Results demonstrate that NDDEs compare favorably with existing approaches for partially observed systems, including long short-term memory (LSTM) networks and augmented neural ordinary differential equations (ANODEs). Overall, NDDEs offer a principled and data-efficient framework for modeling non-Markovian dynamics under partial observability. An open-source implementation accompanies this article.

This paper presents a novel model for bivariate stochastic fluid processes that incorporate a ruin-dependent behavioral switch. Unlike typical models that assume a shared underlying process, the presented model allows each process to operate independently until a ruin event in one triggers a change in the other. Here, each process evolves on the entire real line (unbounded), and ruin occurs when an individual process hits level zero from above for the first time. A mathematical framework for the model is developed, to explore its properties and provide closed-form expressions for approximations of key performance metrics, particularly the joint law of the ruin times. This approach introduces a class of compatible pathwise approximations to analyze ruin probabilities, which are subsequently studied through a matrix-analytic framework. A numerical section illustrates the application of the methodology, including an analysis of the approximation’s convergence and the behavior of joint ruin probabilities.

The rapid proliferation of AI systems has outpaced regulatory and insurance frameworks, leaving risks from unpredictable rogue AI behaviors unaddressed. While academic debates prioritize existential threats, this article shifts focus to governing present-day AI through AI Risk Bonds: market-driven instruments inspired by catastrophe bonds. These bonds securitize AI-related liabilities, using investor scrutiny to price risks based on a system’s expected impact and behavioral predictability. By dynamically adjusting bond yields, higher risks escalate capital costs for developers, incentivizing proactive risk mitigation. The mechanism addresses regulatory blind spots via market oversight, disperses liability through capital markets, and reduces moral hazard by linking financing to risk profiles. Complementing initiatives like the EU AI Act, this framework balances innovation with precaution, tethering profitability to risk minimization for responsible AI development.

High-frequency mortality data have attracted growing attention, but their use has largely been confined to specific applications rather than general modeling and forecasting. Such data pose new challenges to traditional mortality models due to pronounced seasonal patterns and short-term fluctuations. To address these challenges and produce more accurate forecasts with the high-frequency mortality data, this paper introduces a novel integration of gradient boosting techniques into traditional stochastic mortality models under a multi-population setting. Our key innovation lies in using the Li and Lee model as the weak learner within the gradient boosting framework, replacing conventional decision trees. Empirical studies are conducted using weekly mortality data from 30 countries (Human Mortality Database, 2015–2019). Empirical evidence highlights that the proposed methodology not only enhances model fit by accurately capturing underlying mortality trends and seasonal patterns but also achieves superior forecast accuracy, compared to the benchmark models. We also investigate a key challenge in multi-population mortality modeling: how to select appropriate subpopulations with sufficiently similar mortality experiences. A comprehensive clustering exercise is conducted based on mortality improvement rates and seasonal strength. The empirical results demonstrate that our proposed model maintains strong forecast accuracy across different clustering configurations, thereby reducing the need for extensive data preprocessing.

Observational vaccine effectiveness (VE) studies provide essential real-world evidence but are prone to bias. Valid synthesis relies on rigorous risk-of-bias (RoB) assessment in systematic reviews of VE studies. Following JBI guidance, we mapped and described RoB assessment methodologies in systematic reviews of VE studies. We searched MEDLINE, Embase, and Web of Science from 1 January 2013 to 17 May 2023 and the grey literature from 1 January 2018 to 15 August 2023. Of 367 identified reviews, 38 lacked any RoB assessment, yielding 203 systematic reviews. Of these, 190 used existing tools (NOS (85/190, 44.7%), ROBINS-I (46/190, 24.2%), and JBI (11/190, 5.8%)) and 13 used an author-developed tool (13/203, 6.4%). Tools were adapted in 16.7% (34/203) of reviews and 7.2% (14/203) used multiple tools. Reviews included 20 (±25.7) observational studies, commonly cohorts (175/203, 86.2%), with COVID-19 (66/203, 32.5%) and seasonal influenza (62/203, 30.5%) frequently studied. VE was reported descriptively in 25.1% (51/203) of reviews, while 74.9% (152/203) provided meta-analyzed estimates primarily based on laboratory-confirmed infection (137/203, 67.5%) and symptomatic disease (130/203, 64.0%). Our findings indicate heterogeneous RoB assessment, reflected by use of different/multiple tools, frequent adaptations, author-developed methods, and absence of RoB assessment, highlighting the need for clearer guidance or tailored tools.

Digital Twin Construction (DTC) is a data-centric mode of construction that leverages digital twin technologies to maintain a continuously updated representation of a project and to enable short cycle Plan Do Check Act (PDCA) planning and control with continuous feedback and improvement. Industrial implementations reported to date have been too narrow in scope and too few in number to provide comprehensive proof of feasibility and empirical evidence of impacts. We address these gaps using a laboratory setup that implements a complete DTC PDCA workflow for a precast residential project. The experimental DTC system stores project intent, monitors and captures current status, and supports human in the loop replanning or automated optimization across factory production, logistics, and erection on site for a 1:25 scale model building. Validation through numerous full construction project runs demonstrates consistent end-to-end operation and practical usability. The testbed itself provides a reference architecture for DTC systems and a platform for controlled and replicable experiments that provide comparable quantitative evidence on DTC impacts under varied levels of automation.

Let $\Omega _1, \ldots , \Omega _m$ be probability spaces, let ${\mathbf \Omega }=\Omega _1 \times \cdots \times \Omega _m$ be their product and let $A_1, \ldots , A_n \subset {\mathbf \Omega }$ be events. Suppose that each event $A_i$ depends on $r_i$ coordinates of a point $x \in {\mathbf \Omega }$, $x=\left (\xi _1, \ldots , \xi _m\right )$, and that for each event $A_i$ there are $\Delta _i$ other events $A_j$ that depend on some of the coordinates that $A_i$ depends on. Let $\Delta =\max \{5,\ \Delta _i\,:\, i=1, \ldots , n\}$ and let $\mu _i=\min \{r_i,\ \Delta _i+1\}$ for $i=1, \ldots , n$. We prove that if ${\mathbb P}(A_i) \lt (3\Delta )^{-3\mu _i}$ for all $i$, then for any $0 \lt \epsilon \lt 1$, the probability ${\mathbb P}\left ( \bigcap _{i=1}^n \overline {A}_i\right )$ of the intersection of the complements of all $A_i$ can be computed within relative error $\epsilon$ in polynomial time from the probabilities ${\mathbb P}\left (A_{i_1} \cap \ldots \cap A_{i_k}\right )$ of $k$-wise intersections of the events $A_i$ for $k = e^{O(\Delta )} \ln (n/\epsilon )$.

In this paper, we consider catastrophe stop-loss reinsurance valuation for a reinsurance company with dynamic contagion claims. To deal with conventional and emerging catastrophic events, we propose the use of a compound dynamic contagion process for the catastrophic component of the liability. Under the premise that there is an absence of arbitrage opportunity in the market, we obtain arbitrage-free premiums for these contracts. To this end, the Esscher transform is adopted to specify an equivalent martingale probability measure. We show that reinsurers have various ways of levying the security loading on the net premiums to quantify the catastrophic liability in light of the growing challenges posed by emerging risks arising from climate change, cyberattacks, and pandemics. We numerically compare arbitrage-free catastrophe stop-loss reinsurance premiums via the Monte Carlo simulation method. We also compare them with those from generalized compound Hawkes/compound Cox cases. Sensitivity analyses are performed by changing the retention level, the Esscher parameters, and the intensity parameters.