Artificial intelligence (AI) requires new ways of evaluating national technology use and strategy for African nations. We conduct a survey of existing “readiness” assessments both for general digital adoption and AI policy in particular. We conclude that existing global readiness assessments do not fully capture African states’ progress in AI readiness and lay the groundwork for how assessments can be better used for the African context. We consider the extent to which these indicators map to the African context and what these indicators miss in capturing African states’ on-the-ground work in meeting AI capability. Through case studies of four African nations of diverse geographic and economic dimensions, we identify nuances missed by global assessments and offer high-level policy considerations for how states can best improve their AI readiness standards and prepare their societies to capture the benefits of AI.

Relatively, recent work by Jeganathan (2008, Cowles Foundation Discussion Paper 1649) and Wang (2014, Econometric Theory, 30(3), 509–535) on generalized martingale central limit theorems (MCLTs) implicitly introduces a new class of instrument arrays that yield (mixed) Gaussian limit theory irrespective of the persistence level in the data. Motivated by these developments, we propose a new semiparametric method for estimation and inference in nonlinear predictive regressions with persistent predictors. The proposed method that we term chronologically trimmed least squares (CTLS) is comparable to the IVX method of Phillips and Magdalinos (2009, Econometric inference in the vicinity of unity. Mimeo, Singapore Management University) and yields conventional inference in regressions where the nature and extent of persistence in the data are uncertain. In terms of model generality, our contribution to the existing literature is twofold. First, our covariate model space allows for both nearly integrated (NI) and fractional processes (stationary and nonstationary) as a special case, while the vast majority of articles in this area only consider NI arrays. Second, we allow for nonlinear regression functions. The CTLS estimator is obtained by applying certain chronological trimming to the OLS instruments using appropriate kernel functions of time trend variables. In particular, the instruments under consideration are a generalized (averaged) version of those widely used for time-varying parameter (TVP) models. For the purposes of our analysis, we develop a novel asymptotic theory for sample averages of various processes weighted by such kernel functionals which is of independent interest and highly relevant to the TVP literature. Leveraging our nonlinear framework, we also provide an investigation on the effects of misbalancing on the predictability hypothesis. A new methodology is proposed to mitigate misbalancing effects. These methods are used for exploring the predictability of SP500 returns.

We aimed to identify risk factors related to COVID-19 reinfection in Hong Kong. We performed a population-based retrospective cohort study and reviewed case-based data on COVID-19 infections reported to the Centre for Health Protection from 8 January 2020 to 29 January 2023. We analyzed the epidemiology of COVID-19 infections and performed a Cox regression analysis. In this period, 3.32% (103,065/3,106,579) of COVID-19 infections recorded were classified as reinfection. Compared with primarily infected cases, a higher proportion of re-infected cases had chronic diseases (33.54% vs. 27.27%) and were residents of residential care homes (RCH) (10.99% vs. 1.41%). The time interval between the two episodes ranged from 31 to 1,050 days (median 282 days). Cox regression analysis of Omicron cases with the adjustment of covariates showed that being female (Hazard Ratio [HR] 1.12, 95% CI 1.11–1.13), chronic diseases (HR 1.18, 95% CI 1.16–1.20) and RCH residents (HR 6.78, 95% CI 6.61–6.95) were associated with reinfection, while additional vaccination after primary infection was protective (HR 0.80, 95% CI 0.79–0.81). Further analytical studies on the risk factors and protectors of COVID-19 reinfection are needed to guide targeted interventions.

Dientamoeba fragilis (D. fragilis) is an intestinal protozoan parasite with uncertain pathogenic potential. In the United States, data on D. fragilis in the era of molecular detection are limited. The aim of this retrospective chart review was to evaluate the epidemiology and clinical characteristics of D. fragilis cases identified using polymerase chain reaction assays between 2016 and 2024 at our academic medical centre located in Utah. We identified 28 unique cases with varying gastrointestinal symptomatology including diarrhoea, abdominal pain, nausea, vomiting, and bloating. Approximately half (52%) of patients with follow-up data demonstrated improvement in symptoms following initial treatment for D. fragilis. The overall prevalence of D. fragilis was low among those tested (0.6% positivity). Additional research, including case-control studies, is needed to better describe the etiologic role of D. fragilis.

This paper initiates the explicit study of face numbers of matroid polytopes and their computation. We prove that, for the large class of split matroid polytopes, their face numbers depend solely on the number of cyclic flats of each rank and size, together with information on the modular pairs of cyclic flats. We provide a formula which allows us to calculate $f$-vectors without the need of taking convex hulls or computing face lattices. We discuss the particular cases of sparse paving matroids and rank two matroids, which are of independent interest due to their appearances in other combinatorial and geometric settings.

We assessed the validity of serum total anti-nucleoprotein Immunoglobulin (N-antibodies) to identify SARS-CoV-2 (re)infections by estimating the persistence of N-antibody seropositivity and boosting following infection. From a prospective Dutch cohort study (VASCO), we included adult participants with ≥2 consecutive self-collected serum samples, 4–8 months apart, between May 2021–May 2023. Sample pairs were stratified by N-seropositivity of the first sample and by self-reported infection within the sampling interval. We calculated the proportions of participants with N-seroconversion and fold-increase (1.5, 2, 3, 4) of N-antibody concentration over time since infection and explored determinants. We included 67,632 sample pairs. Pairs with a seronegative first sample (70%) showed 89% N-seroconversion after reported infection and 11% when no infection was reported. In pairs with a seropositive first sample (30%), 82%–65% showed a 1.5- to 4-fold increase with a reported reinfection, and 19%–10% without a reported reinfection, respectively. After one year, 83% remained N-seropositive post-first infection and 93%–61% showed a 1.5-fold to 4-fold increase post-reinfection. Odds for seroconversion/fold increase were higher for symptomatic infections and Omicron infections. In the current era with limited antigen or PCR testing, N-serology can be validly used to detect SARS-CoV-2 (re)infections at least up to a year after infection, supporting the monitoring of COVID-19 burden and vaccine effectiveness.

Sexual and gender–based violence (SGBV) is a multifaceted, endemic, and nefarious phenomenon that remains poorly measured and understood, despite greater global awareness of the issue. While efforts to improve data collection methods have increased–including the implementation of the Demographic and Health Survey (DHS) in some countries–the lack of reliable SGBV data remains a significant challenge to developing targeted policy interventions and advocacy initiatives. Using a recent mixed–methods research project conducted by the authors in Sierra Leone as a case study, this paper discusses the current status of SGBV data, challenges faced, and potential research a pproaches.

Climate change exacerbates existing risks and vulnerabilities for people globally, and migration is a longstanding adaptation response to climate risk. The mechanisms through which climate change shapes human mobility are complex, however, and gaps in data and knowledge persist. In response to these gaps, the United Nations Development Programme’s (UNDP) Predictive Analytics, Human Mobility, and Urbanization Project employed a hybrid approach that combined predictive analytics with participatory foresight to explore climate change-related mobility in Pakistan and Viet Nam from 2020 to 2050. Focusing on Karachi and Ho Chi Minh City, the project estimated temporal and spatial mobility patterns under different climate change scenarios and evaluated the impact of such in-migration across key social, political, economic, and environmental domains. Findings indicate that net migration into these cities could significantly increase under extreme climate scenarios, highlighting both the complex spatial patterns of population change and the potential for anticipatory policies to mitigate these impacts. While extensive research exists on foresight methods and theory, process reflections are underrepresented. The innovative approach employed within this project offers valuable insights on foresight exercise design choices and their implications for effective stakeholder engagement, as well as the applicability and transferability of insights in support of policymaking. Beyond substantive findings, this paper offers a critical reflection on the methodological alignment of data-driven and participatory foresight with the aim of anticipatory policy ideation, seeking to contribute to the enhanced effectiveness of foresight practices.

The Flexible Farrington Algorithm (FFA) is widely used to detect infectious disease outbreaks at national/regional levels on a weekly basis. The rapid spread of SARS-CoV-2 alongside the speed at which diagnostic and public health interventions were introduced made the FFA of limited use. We describe how the methodology was adapted to provide a daily alert system to support local health protection teams (HPTs) working in the 316 English lower-tier local authorities. To minimize the impact of a rapidly changing epidemiological situation, the FFA was altered to use 8 weeks of data. The adapted algorithm was based on reported positive counts using total tests as an offset. Performance was assessed using the root mean square error (RMSE) over a period. Graphical reports were sent to local teams enabling targeted public health action. From 1 July 2020, results were routinely reported. Adaptions accommodated the impact on reporting because of changes in diagnostic strategy (introduction of lateral flow devices). RMSE values were relatively small compared to observed counts, increased during periods of increased reporting, and were relatively higher in the northern and western areas of the country. The exceedance reports were well received. This presentation should be considered as a successful proof-of-concept.

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.