In this note, we formulate a ‘one-sided’ version of Wormald’s differential equation method. In the standard ‘two-sided’ method, one is given a family of random variables that evolve over time and which satisfy some conditions, including a tight estimate of the expected change in each variable over one-time step. These estimates for the expected one-step changes suggest that the variables ought to be close to the solution of a certain system of differential equations, and the standard method concludes that this is indeed the case. We give a result for the case where instead of a tight estimate for each variable’s expected one-step change, we have only an upper bound. Our proof is very simple and is flexible enough that if we instead assume tight estimates on the variables, then we recover the conclusion of the standard differential equation method.

In 2015, the WHO African Region was responding to the largest Ebola virus disease outbreak in history while at the same time working to contain a wild poliovirus outbreak [1]. The 2030 Agenda for Sustainable Development had recently been endorsed, reflecting new global development priorities. By 2016, the Ebola outbreak was under control, and a new approach to reform and priority setting was in place in the region; the Transformation Agenda [2]. This agenda, introduced by the new Regional Director for Africa, Dr Matshidiso Moeti, set up a robust system for improving the efficiency and accountability of the WHO Secretariat for the African Region, which has been instrumental in the transformative changes that have been seen across the region in the past 10 years. This commentary discusses significant contributions to public health in the WHO African Region in the past decade, in the context of the Transformation Agenda, and the contributions of major investment in health security in the region. It is important to understand the need to sustain particular initiatives and elements of the transformative change that has taken place in the region.

In the last two decades the study of random instances of constraint satisfaction problems (CSPs) has flourished across several disciplines, including computer science, mathematics and physics. The diversity of the developed methods, on the rigorous and non-rigorous side, has led to major advances regarding both the theoretical as well as the applied viewpoints. Based on a ceteris paribus approach in terms of the density evolution equations known from statistical physics, we focus on a specific prominent class of regular CSPs, the so-called occupation problems, and in particular on $r$-in-$k$ occupation problems. By now, out of these CSPs only the satisfiability threshold – the largest degree for which the problem admits asymptotically a solution – for the $1$-in-$k$ occupation problem has been rigorously established. Here we determine the satisfiability threshold of the $2$-in-$k$ occupation problem for all $k$. In the proof we exploit the connection of an associated optimization problem regarding the overlap of satisfying assignments to a fixed point problem inspired by belief propagation, a message passing algorithm developed for solving such CSPs.

Although SARS-CoV-2 vaccination reduces hospitalization and mortality, its long-term impact on Long-COVID remains to be elucidated. The aim of the study was to evaluate the different development of Long-COVID clinical phenotypes according to the vaccination status of patients. Clinical and demographic characteristics were assessed for each patient, while Long-COVID symptoms were self-reported and later stratified into distinct clinical phenotypes. Vaccination was significantly associated with the avoidance of hospitalization, less invasive respiratory support, and less alterations of cardiopulmonary functions, as well as reduced lasting lung parenchymal damage. However, no association between vaccination status and the development of at least one Long-COVID symptom was found. Nevertheless, clinical phenotypes were differently associated with vaccination status, as neuropsychiatric were more frequent in unvaccinated patients and cardiorespiratory symptoms were reported mostly in vaccinated patients. Different progression of disease could be at play in the different development of specific Long-COVID clinical phenotypes, as shown by the different serological responses between unvaccinated and vaccinated patients. A higher anti-Spike (S) antibody titre was protective for vaccinated patients, while it was detrimental for unvaccinated patients. A better understanding of the mechanism underlying the development of Long-COVID symptoms might be reached by standardized methodologies and symptom classification.

We consider the hard-core model on a finite square grid graph with stochastic Glauber dynamics parametrized by the inverse temperature $\beta$. We investigate how the transition between its two maximum-occupancy configurations takes place in the low-temperature regime $\beta \to \infty$ in the case of periodic boundary conditions. The hard-core constraints and the grid symmetry make the structure of the critical configurations for this transition, also known as essential saddles, very rich and complex. We provide a comprehensive geometrical characterization of these configurations that together constitute a bottleneck for the Glauber dynamics in the low-temperature limit. In particular, we develop a novel isoperimetric inequality for hard-core configurations with a fixed number of particles and show how the essential saddles are characterized not only by the number of particles but also their geometry.

Applications of deep learning to physical simulations such as Computational Fluid Dynamics have recently experienced a surge in interest, and their viability has been demonstrated in different domains. However, due to the highly complex, turbulent, and three-dimensional flows, they have not yet been proven usable for turbomachinery applications. Multistage axial compressors for gas turbine applications represent a remarkably challenging case, due to the high-dimensionality of the regression of the flow field from geometrical and operational variables. This paper demonstrates the development and application of a deep learning framework for predictions of the flow field and aerodynamic performance of multistage axial compressors. A physics-based dimensionality reduction approach unlocks the potential for flow-field predictions, as it re-formulates the regression problem from an unstructured to a structured one, as well as reducing the number of degrees of freedom. Compared to traditional “black-box” surrogate models, it provides explainability to the predictions of the overall performance by identifying the corresponding aerodynamic drivers. The model is applied to manufacturing and build variations, as the associated performance scatter is known to have a significant impact on $ \mathrm{C}{\mathrm{O}}_2 $ emissions, which poses a challenge of great industrial and environmental relevance. The proposed architecture is proven to achieve an accuracy comparable to that of the CFD benchmark, in real-time, for an industrially relevant application. The deployed model is readily integrated within the manufacturing and build process of gas turbines, thus providing the opportunity to analytically assess the impact on performance with actionable and explainable data.

This study examines Nigeria’s National Information Technology Development Agency Code of Practice for Interactive Computer Service Platforms as one of Africa’s first push towards digital and social media co-regulation. Already established as a regulatory practice in Europe, co-regulation emphasises the need to impose duties of care on platforms and hold them, instead of users, accountable for safe online experiences. It is markedly different from the prior (and existing) regulatory paradigm in Nigeria, which is based on direct user regulation. By analysing the Code of Practice, therefore, this study considers what Nigeria’s radical turn towards co-regulation means for digital policy and social media regulation in relation to standards, information-gathering, and enforcement. It further sheds light on what co-regulation entails for digital regulatory practice in the wider African context, particularly in terms of the balance of power realities between Global North platforms and Global South countries.

We consider interacting urns on a finite directed network, where both sampling and reinforcement processes depend on the nodes of the network. This extends previous research by incorporating node-dependent sampling and reinforcement. We classify the sampling and reinforcement schemes, as well as the networks on which the proportion of balls of either colour in each urn converges almost surely to a deterministic limit. We also investigate conditions for achieving synchronisation of the colour proportions across the urns and analyse fluctuations under specific conditions on the reinforcement scheme and network structure.

In August 2023, the Finnish Institute for Health and Welfare received reports of a potential cluster of pneumococcal pneumonia cases among shipyard employees in Turku, Finland. Considering a similar outbreak in the same shipyard in 2019, we initiated a case–control study to investigate individual and environmental risk factors specific to this occupational setting in order to inform targeted prevention measures. In total, 14 hospitalized cases were identified from 19 August to 15 October 2023. Streptococcus pneumoniae serotypes 4 and 9 V were isolated from blood cultures of seven cases. Eleven cases and 67 controls working at the shipyard were included in the case–control study. Compared with controls, cases were more likely to be living in an apartment/studio or a hotel/hostel, and less likely in a house or with family. Furthermore, cases were more likely to have a shorter duration of employment (< 1 year) at the shipyard compared to controls. Control measures, including an information and a vaccination campaign, were implemented. We emphasize shipyard-wide hygiene improvements and recommend nationwide consideration of expanding pneumococcal vaccination eligibility to all shipyard construction employees as an occupational high-risk group.

Malaria remains a major health challenge in developing countries, with climate change intensifying its impact. Pakistan is among the most vulnerable nations. This study examines the relationship between temperature and malaria cases in two highly affected districts, Bannu and Lakki Marwat, to inform climate-adaptive interventions.

We analyzed monthly malaria cases (2014–2022) from the Integrated Vector Control/Malaria Control Program in Khyber Pakhtunkhwa, combined with gridded meteorological data from Copernicus ERA5-Land. Time-series analysis using distributed lag nonlinear models and quasi-Poisson regression was applied to assess the associations.

The findings suggest that as temperatures exceed 22.4°C, malaria transmission increases by 9 to 10% for every 1°C rise in both districts. In Bannu, up to 39.8% of reported malaria cases could be attributed to heat, while in Lakki Marwat, 54.1% of cases were attributable to heat. Under high emission scenarios, heat-related malaria cases could increase by 0.8 to 3.5% by the 2060s. Relationship between temperature and malaria transmission is complex and is influenced by environmental factors such as precipitation and humidity.

Given Pakistan’s limited healthcare infrastructure, addressing climate-driven malaria risks is urgent. Recent severe floods and malaria surges highlight the need for climate adaptation measures and strengthened healthcare systems to enhance community resilience.

We introduce and study a game-theoretic model to understand the spread of an epidemic in a homogeneous population. A discrete-time stochastic process is considered where, in each epoch, first, a randomly chosen agent updates their action trying to maximize a proposed utility function, and then agents who have viral exposures beyond their immunity get infected. Our main results discuss asymptotic limiting distributions of both the cardinality of the subset of infected agents and the action profile, considered under various values of two parameters (initial action and immunity profile). We also show that the theoretical distributions are almost always achieved in the first few epochs.

The study of many population growth models is complicated by only partial observation of the underlying stochastic process driving the model. For example, in an epidemic outbreak we might know when individuals show symptoms to a disease and are removed, but not when individuals are infected. Motivated by the above example and the long-established approximation of epidemic processes by branching processes, we explore the number of individuals alive in a time-inhomogeneous branching process with a general phase-type lifetime distribution given only (partial) information on the times of deaths of individuals. Deaths are detected independently with a detection probability that can vary with time and type. We show that the number of individuals alive immediately after the kth detected death can be expressed as the mixture of random variables each of which consists of the sum of k independent zero-modified geometric distributions. Furthermore, in the case of an Erlang lifetime distribution, we derive an easy-to-compute mixture of negative binomial distributions as an approximation of the number of individuals alive immediately after the kth detected death.

Current standard microbiological techniques are generally very time consuming, usually requiring 24–72 h to establish a diagnosis. Consequentially, contemporary clinical practices implement broad-spectrum antibiotic administration prior to pathogen detection, prompting the emergence of extremely dangerous antibiotic-resistant bacteria. Additionally, lengthy test-to-result turnover times can greatly exacerbate the rate of disease spread. Rapid point-of-care (POC) diagnostics has quickly gained importance since the SARS-CoV-2 pandemic; accordingly, we have developed a rapid four-channel POC plasmonic quantitative polymerase chain reaction (qPCR) machine (Kimera P-IV) to respond to the deficiencies in infection control. Utilizing gold nanorods (GNRs) as nano-heaters and integrating vertical cavity surface emitting lasers (VCSEL) to replace traditional Peltier blocks, the Kimera P-IV has also incorporated quantitative real-time fluorescent monitoring. Using Chlamydia trachomatis genetic material to evaluate the rapid thermocycling performance of the platform, we have generated positive amplicons in less than 13 min; however, to achieve these results, several biological reagent considerations needed to be taken into account, specifically primer design. The device can achieve a limit of detection (LoD) of <101 DNA copies, a PCR efficiency of 88.3%, and can differentiate positive from negative results with 100% accuracy. Moreover, it can also analyze C. trachomatis DNA spiked urine samples via a simple dilution, suggesting that a separate nucleic acid step may not be needed for diagnosing infections. In conclusion, the operation of the Kimera P-IV prototype places it in a unique position of POC devices to revolutionize infectious disease diagnosis.

In this paper, we provide a systematic review of existing artificial intelligence (AI) regulations in Europe, the United States, and Canada. We build on the qualitative analysis of 129 AI regulations (enacted and not enacted) to identify patterns in regulatory strategies and in AI transparency requirements. Based on the analysis of this sample, we suggest that there are three main regulatory strategies for AI: AI-focused overhauls of existing regulation, the introduction of novel AI regulation, and the omnibus approach. We argue that although these types emerge as distinct strategies, their boundaries are porous as the AI regulation landscape is rapidly evolving. We find that across our sample, AI transparency is effectively treated as a central mechanism for meaningful mitigation of potential AI harms. We therefore focus on AI transparency mandates in our analysis and identify six AI transparency patterns: human in the loop, assessments, audits, disclosures, inventories, and red teaming. We contend that this qualitative analysis of AI regulations and AI transparency patterns provides a much needed bridge between the policy discourse on AI, which is all too often bound up in very detailed legal discussions and applied sociotechnical research on AI fairness, accountability, and transparency.

Cellulitis, a common subcutaneous infection, is influenced by host, pathogen, and environmental factors. Previous studies have shown seasonal patterns in adult cellulitis, suggesting temperature as a risk factor. This study investigated seasonal patterns in paediatric cellulitis in Jerusalem’s semi-arid climate. A single-center retrospective cohort study reviewed medical records of 2,219 hospitalized children under 18 with cellulitis between 1990 and 2020. Demographic, clinical, temperature, and humidity data were collected. Results revealed a significant sinusoidal pattern for limb cellulitis (LC) but for other body sites, with summer peaks and winter nadirs (P < 0.01). August showed the highest incidence, tripling that of February. Age groups 1-6 and 6-12 demonstrated the largest seasonal differences (P = 0.004, P = 0.008). Over three decades, paediatric hospitalized LC cases increased by 71% (P < 0.001), correlating with rising temperatures. Elevated ambient temperature seven days prior to diagnosis was a risk factor for LC development (OR = 1.02, P = 0.03). This study highlights the cyclic seasonal pattern of paediatric LC, peaking in summer. The significant increase in cases over time, coupled with rising temperatures, suggests climate change as a contributing factor. These findings could inform public health strategies for cellulitis prevention and management in children.

We consider time-inhomogeneous ordinary differential equations (ODEs) whose parameters are governed by an underlying ergodic Markov process. When this underlying process is accelerated by a factor $\varepsilon^{-1}$, an averaging phenomenon occurs and the solution of the ODE converges to a deterministic ODE as $\varepsilon$ vanishes. We are interested in cases where this averaged flow is globally attracted to a point. In that case, the equilibrium distribution of the solution of the ODE converges to a Dirac mass at this point. We prove an asymptotic expansion in terms of $\varepsilon$ for this convergence, with a somewhat explicit formula for the first-order term. The results are applied in three contexts: linear Markov-modulated ODEs, randomized splitting schemes, and Lotka–Volterra models in a random environment. In particular, as a corollary, we prove the existence of two matrices whose convex combinations are all stable but are such that, for a suitable jump rate, the top Lyapunov exponent of a Markov-modulated linear ODE switching between these two matrices is positive.

The strategy of tuberculosis (TB) contact investigation is essential for enhancing disease detection. We conducted a cross-sectional study to evaluate the yield of contact investigation for new TB cases, estimate the prevalence of TB, and identify characteristics of index cases associated with infection among contacts of new cases notified between 2010 and 2020 in São Paulo, Brazil. Out of 186466 index TB cases, 131055 (70.3%) underwent contact investigation. A total of 652286 contacts were screened, of which 451704 (69.2%) were examined. Of these, 12243 were diagnosed with active TB (yield of 1.9%), resulting in a number needed to screen of 53 and a number needed to test of 37 to identify one new TB case. The weighted prevalence for the total contacts screened was 2.8% (95% confidence interval [CI]: 2.7%–2.9%), suggesting underreporting of 6021 (95% CI: 5269–6673) cases. The likelihood of TB diagnosis was higher among contacts of cases identified through active case-finding, abnormal chest X-ray, pulmonary TB, or drug resistance, as well as among children, adults, women, individuals in socially vulnerable situations, and those with underlying clinical conditions. The study highlights significant TB underreporting among contacts, recommending strengthened contact investigation to promptly identify and treat new cases.

Research in decentralized computing, specifically in consensus algorithms, has focused on providing resistance to an adversary with a minority stake. This has resulted in systems that are majoritarian in the extreme, ignoring valuable lessons learned in law and politics over centuries. In this article, we first detail this phenomenon of majoritarianism and point out how minority protections in the nondigital world have been implemented. We motivate adding minority protections to collaborative systems with examples. We also show how current software deployment models exacerbate majoritarianism, highlighting the problem of monoculture in client software in particular. We conclude by giving some suggestions on how to make decentralized computing less hostile to those in the minority.

For a given graph $H$, we say that a graph $G$ has a perfect $H$-subdivision tiling if $G$ contains a collection of vertex-disjoint subdivisions of $H$ covering all vertices of $G.$ Let $\delta _{\mathrm {sub}}(n, H)$ be the smallest integer $k$ such that any $n$-vertex graph $G$ with minimum degree at least $k$ has a perfect $H$-subdivision tiling. For every graph $H$, we asymptotically determined the value of $\delta _{\mathrm {sub}}(n, H)$. More precisely, for every graph $H$ with at least one edge, there is an integer $\mathrm {hcf}_{\xi }(H)$ and a constant $1 \lt \xi ^*(H)\leq 2$ that can be explicitly determined by structural properties of $H$ such that $\delta _{\mathrm {sub}}(n, H) = \left (1 - \frac {1}{\xi ^*(H)} + o(1) \right )n$ holds for all $n$ and $H$ unless $\mathrm {hcf}_{\xi }(H) = 2$ and $n$ is odd. When $\mathrm {hcf}_{\xi }(H) = 2$ and $n$ is odd, then we show that $\delta _{\mathrm {sub}}(n, H) = \left (\frac {1}{2} + o(1) \right )n$.