Viruses present an amazing genetic variability. An ensemble of infecting viruses, also called a viral quasispecies, is a cloud of mutants centered around a specific genotype. The simplest model of evolution, whose equilibrium state is described by the quasispecies equation, is the Moran–Kingman model. For the sharp-peak landscape, we perform several exact computations and derive several exact formulas. We also obtain an exact formula for the quasispecies distribution, involving a series and the mean fitness. A very simple formula for the mean Hamming distance is derived, which is exact and does not require a specific asymptotic expansion (such as sending the length of the macromolecules to $\infty$ or the mutation probability to 0). With the help of these formulas, we present an original proof for the well-known phenomenon of the error threshold. We recover the limiting quasispecies distribution in the long-chain regime. We try also to extend these formulas to a general fitness landscape. We obtain an equation involving the covariance of the fitness and the Hamming class number in the quasispecies distribution. Going beyond the sharp-peak landscape, we consider fitness landscapes having finitely many peaks and a plateau-type landscape. Finally, within this framework, we prove rigorously the possible occurrence of the survival of the flattest, a phenomenon which was previously discovered by Wilke et al. (Nature 412, 2001) and which has been investigated in several works (see e.g. Codoñer et al. (PLOS Pathogens 2, 2006), Franklin et al. (Artificial Life 25, 2019), Sardanyés et al. (J. Theoret. Biol. 250, 2008), and Tejero et al. (BMC Evolutionary Biol. 11, 2011)).

In this article, I will consider the moral issues that might arise from the possibility of creating more complex and sophisticated autonomous intelligent machines or simply artificial intelligence (AI) that would have the human capacity for moral reasoning, judgment, and decision-making, and (the possibility) of humans enhancing their moral capacities beyond what is considered normal for humanity. These two possibilities raise an urgency for ethical principles that could be used to analyze the moral consequences of the intersection of AI and transhumanism. In this article, I deploy personhood-based relational ethics grounded on Afro-communitarianism as an African ethical framework to evaluate some of the moral problems at the intersection of AI and transhumanism. In doing so, I will propose some Afro-ethical principles for research and policy development in AI and transhumanism.

Anthrax is a bacterial zoonotic disease caused by Bacillus anthracis. We qualitatively examined facilitators and barriers to responding to a potential anthrax outbreak using the capability, opportunity, motivation behaviour model (COM-B model) in the high-risk rural district of Namisindwa, in Eastern Uganda. We chose the COM-B model because it provides a systematic approach for selecting evidence-based techniques and approaches for promoting the behavioural prompt response to anthrax outbreaks. Unpacking these facilitators and barriers enables the leaders and community members to understand existing resources and gaps so that they can leverage them for future anthrax outbreaks.

This was a qualitative cross-sectional study that was part of a bigger anthrax outbreak simulation study conducted in September 2023. We conducted 10 Key Informant interviews among key stakeholders. The interviews were audio recorded on Android-enabled phones and later transcribed verbatim. The transcripts were analyzed using a deductive thematic content approach through Nvivo 12.

The facilitators were; knowledge of respondents about anthrax disease and anthrax outbreak response, experience and presence of surveillance guidelines, availability of resources, and presence of communication channels. The identified barriers were; porous boarders that facilitate unregulated animal trade across, lack of essential personal protective equipment, and lack of funds for surveillance and response activities.

Generally, the district was partially ready for the next anthrax outbreak. The district was resourced in terms of human resources but lacked adequate funds for animal, environmental and human surveillance activities for anthrax and related response. The district technical staff had the knowledge required to respond to the anthrax outbreak but lacked adequate funds for animal, environmental and human surveillance for anthrax and related response. We think that our study findings are generalizable in similar settings and therefore call for the implementation of such periodic evaluations to help leverage the strong areas and improve other aspects. Anthrax is a growing threat in the region, and there should be proactive efforts in prevention, specifically, we recommend vaccination of livestock and further research for human vaccines.

When implementing Markov Chain Monte Carlo (MCMC) algorithms, perturbation caused by numerical errors is sometimes inevitable. This paper studies how the perturbation of MCMC affects the convergence speed and approximation accuracy. Our results show that when the original Markov chain converges to stationarity fast enough and the perturbed transition kernel is a good approximation to the original transition kernel, the corresponding perturbed sampler has fast convergence speed and high approximation accuracy as well. Our convergence analysis is conducted under either the Wasserstein metric or the $\chi^2$ metric, both are widely used in the literature. The results can be extended to obtain non-asymptotic error bounds for MCMC estimators. We demonstrate how to apply our convergence and approximation results to the analysis of specific sampling algorithms, including Random walk Metropolis, Metropolis adjusted Langevin algorithm with perturbed target densities, and parallel tempering Monte Carlo with perturbed densities. Finally, we present some simple numerical examples to verify our theoretical claims.

The analysis of insurance and annuity products issued on multiple lives requires the use of statistical models which account for lifetime dependence. This paper presents a Dirichlet process mixture-based approach that allows to model dependent lifetimes within a group, such as married couples, accounting for individual as well as group-specific covariates. The model is analyzed in a fully Bayesian setting and illustrated to jointly model the lifetime of male–female couples in a portfolio of joint and last survivor annuities of a Canadian life insurer. The inferential approach allows to account for right censoring and left truncation, which are common features of data in survival analysis. The model shows improved in-sample and out-of-sample performance compared to traditional approaches assuming independent lifetimes and offers additional insights into the determinants of the dependence between lifetimes and their impact on joint and last survivor annuity prices.

We analyze the process M(t) representing the maximum of the one-dimensional telegraph process X(t) with exponentially distributed upward random times and generally distributed downward random times. The evolution of M(t) is governed by an alternating renewal of two phases: a rising phase R and a constant phase C. During a rising phase, X(t) moves upward, whereas, during a constant phase, it moves upward and downward, continuing to move until it attains the maximal level previously reached. Under some choices of the distribution of the downward times, we are able to determine the distribution of C, which allows us to obtain some bounds for the survival function of M(t). In the particular case of exponential downward random times, we derive an explicit expression for the survival function of M(t). Finally, the moments of the first passage time $\Theta_w$ of the process X(t) through a fixed boundary $w>0$ are analyzed.

Mali is a country where little information is known about the circulation of avian influenza viruses (AIVs) in poultry. Implementing risk-based surveillance strategies would allow early detection and rapid control of AIVs outbreaks in the country. In this study, we implemented a multi-criteria decision analysis (MCDA) method coupled with geographic information systems (GIS) to identify risk areas for AIVs occurrence in domestic poultry in Mali. Five risk factors associated with AIVs occurrence were identified from the literature, and their relative weights were determined using the analytic hierarchy process (AHP). Spatial data were collected for each risk factor and processed to produce risk maps for AIVs outbreaks using a weighted linear combination (WLC). We identified the southeast regions (Bamako and Sikasso) and the central region (Mopti) as areas with the highest risk of AIVs occurrence. Conversely, northern regions were considered low-risk areas. The risk areas agree with the location of HPAI outbreaks in Mali. This study provides the first risk map using the GIS-MCDA approach to identify risk areas for AIVs occurrence in Mali. It should provide a basis for designing risk-based and more cost-effective surveillance strategies for the early detection of avian influenza outbreaks in Mali.

A detailed exploration is presented of the integration of human–machine collaboration in governance and policy decision-making, against the backdrop of increasing reliance on artificial intelligence (AI) and automation. This exploration focuses on the transformative potential of combining human cognitive strengths with machine computational capabilities, particularly emphasizing the varying levels of automation within this collaboration and their interaction with human cognitive biases. Central to the discussion is the concept of dual-process models, namely Type I and II thinking, and how these cognitive processes are influenced by the integration of AI systems in decision-making. An examination of the implications of these biases at different levels of automation is conducted, ranging from systems offering decision support to those operating fully autonomously. Challenges and opportunities presented by human–machine collaboration in governance are reviewed, with a focus on developing strategies to mitigate cognitive biases. Ultimately, a balanced approach to human–machine collaboration in governance is advocated, leveraging the strengths of both humans and machines while consciously addressing their respective limitations. This approach is vital for the development of governance systems that are both technologically advanced and cognitively attuned, leading to more informed and responsible decision-making.

We investigate the tail behavior of the first-passage time for Sinai’s random walk in a random environment. Our method relies on the connection between Sinai’s walk and branching processes with immigration in a random environment, and the analysis on some important quantities of these branching processes such as extinction time, maximum population, and total population.

In developing countries, a significant amount of natural gas is used for household water heating, accounting for roughly 50% of total usage. Legacy systems, typified by large water heaters, operate inefficiently by continuously maintaining a large volume of water at a constant temperature, irrespective of demand. With dwindling domestic gas reserves and rising demand, this increases dependence on expensive energy imports.

We introduce a novel Internet of Things (IoT)-inspired solution to understand and predict water usage patterns and only activate the water heater when there’s a predicted demand. This retrofit system is maintenance-free and uses a rechargeable battery powered by a thermoelectric generator (TEG), which capitalizes on the temperature difference between the heater and its environment for electricity. Our study shows a notable 70% reduction in natural gas consumption compared to traditional systems. Our solution offers a sustainable and efficient method for water heating, addressing the challenges of depleting gas reserves and rising energy costs.

Data irregularities, namely small disjuncts, class skew, imbalance, and outliers significantly affect the performance of classifiers. Another challenge posed to classifiers is when new unlabelled data have different characteristics than the training data; this change is termed as a data shift. In this paper, we focus on identifying small disjuncts and dataset shift using the supervised classifier, sequential ellipsoidal partitioning classifier (SEP-C). This method iteratively partitions the dataset into minimum-volume ellipsoids that contain points of the same label, based on the idea of Reduced Convex Hulls. By allowing an ellipsoid that contains points of one label to contain a few points of the other, such small disjuncts may be identified. Similarly, if new points are accommodated only by expanding one or more of the ellipsoids, then shifts in data can be identified. Small disjuncts are distribution-based irregularities that may be considered as being rare but more error-prone than large disjuncts. Eliminating small disjuncts by removal or pruning is seen to affect the learning of the classifier adversely. Dataset shifts have been identified using Bayesian methods, use of confidence scores, and thresholds—these require prior knowledge of the distributions or heuristics. SEP-C is agnostic of the underlying data distributions, uses a single hyperparameter, and as ellipsoidal partitions are generated, well-known statistical tests can be performed to detect shifts in data; it is also applicable as a supervised classifier when the datasets are highly skewed and imbalanced. We demonstrate the performance of SEP-C with UCI, MNIST handwritten digit image, and synthetically generated datasets.