In 2020 and 2021, non-pharmaceutical interventions (NPIs) were implemented globally to mitigate the transmission of COVID-19. This study is aimed to assess the impact of NPIs on the public mental health in Ireland by drawing on two datasets: (1) data of 43,433 chats initiated at Text About It, a free text-based mental health crisis service in Ireland, and (2) emotional well-being indicators reported by respondents of the Amárach public opinion survey, carried out on behalf of the Department of Health, Ireland. Our analysis reveals that COVID-19-related chats drove overall volumes of chats to Text About It between June 2020 to July 2021. Surges in text volumes immediately prior to new restrictions in Ireland indicate an association between the announcements of the new restrictions and a sudden rise in mental health concerns. Through segmented regression, seven distinct breakpoints were identified across weekly chat volumes at Text About It, the majority of which co-occurred with dates when considerable changes in NPIs were made. Significant high cross-correlation is found between emotional well-being variables in the Amárach dataset and the number of weekly COVID-19 related texts to Text About It. This analysis confirms the value of Text About It as community surveillance indicator for population mental health.

With the rise of new technologies, such as Edge computing, Internet of Things, Smart Cities, and Smart Grids, there is a growing need for multi-agent systems (MAS) approaches. Designing multi-agent systems is challenging, and doing this in an automated way is even more so. To address this, we propose a new framework, Evolved Gossip Contracts (EGC). It builds on Gossip Contracts (GC), a decentralised cooperation protocol that is used as the communication mechanism to facilitate self-organisation in a cooperative MAS. GC has several methods that are implemented uniquely, depending on the goal the MAS aims to achieve. The EGC framework uses evolutionary computing to search for the best implementation of these methods. To evaluate EGC, it was used to solve a classical NP-hard optimisation problem, the Bin Packing Problem (BPP). The experimental results show that EGC successfully discovered a decentralised strategy to solve the BPP, which is better than two classical heuristics on test cases similar to those on which it was trained; the improvement is statistically significant. EGC is the first framework that leverages evolutionary computing to semi-automate the discovery of a communication protocol for a MAS that has been shown to be effective at solving an NP-hard problem.

The effective reproduction number (ℜt) is a theoretical indicator of the course of an infectious disease that allows policymakers to evaluate whether current or previous control efforts have been successful or whether additional interventions are necessary. This metric, however, cannot be directly observed and must be inferred from available data. One approach to obtaining such estimates is fitting compartmental models to incidence data. We can envision these dynamic models as the ensemble of structures that describe the disease's natural history and individuals' behavioural patterns. In the context of the response to the COVID-19 pandemic, the assumption of a constant transmission rate is rendered unrealistic, and it is critical to identify a mathematical formulation that accounts for changes in contact patterns. In this work, we leverage existing approaches to propose three complementary formulations that yield similar estimates for ℜt based on data from Ireland's first COVID-19 wave. We describe these Data Generating Processes (DGP) in terms of State-Space models. Two (DGP1 and DGP2) correspond to stochastic process models whose transmission rate is modelled as Brownian motion processes (Geometric and Cox-Ingersoll-Ross). These DGPs share a measurement model that accounts for incidence and transmission rates, where mobility data is assumed as a proxy of the transmission rate. We perform inference on these structures using Iterated Filtering and the Particle Filter. The final DGP (DGP3) is built from a pool of deterministic models that describe the transmission rate as information delays. We calibrate this pool of models to incidence reports using Hamiltonian Monte Carlo. By following this complementary approach, we assess the tradeoffs associated with each formulation and reflect on the benefits/risks of incorporating proxy data into the inference process. We anticipate this work will help evaluate the implications of choosing a particular formulation for the dynamics and observation of the time-varying transmission rate.

Seasonal influenza surveillance is usually carried out by sentinel general practitioners (GPs) who compile weekly reports based on the number of influenza-like illness (ILI) clinical cases observed among visited patients. This traditional practice for surveillance generally presents several issues, such as a delay of one week or more in releasing reports, population biases in the health-seeking behaviour, and the lack of a common definition of ILI case. On the other hand, the availability of novel data streams has recently led to the emergence of non-traditional approaches for disease surveillance that can alleviate these issues. In Europe, a participatory web-based surveillance system called Influenzanet represents a powerful tool for monitoring seasonal influenza epidemics thanks to aid of self-selected volunteers from the general population who monitor and report their health status through Internet-based surveys, thus allowing a real-time estimate of the level of influenza circulating in the population. In this work, we propose an unsupervised probabilistic framework that combines time series analysis of self-reported symptoms collected by the Influenzanet platforms and performs an algorithmic detection of groups of symptoms, called syndromes. The aim of this study is to show that participatory web-based surveillance systems are capable of detecting the temporal trends of influenza-like illness even without relying on a specific case definition. The methodology was applied to data collected by Influenzanet platforms over the course of six influenza seasons, from 2011-2012 to 2016-2017, with an average of 34,000 participants per season. Results show that our framework is capable of selecting temporal trends of syndromes that closely follow the ILI incidence rates reported by the traditional surveillance systems in the various countries (Pearson correlations ranging from 0.69 for Italy to 0.88 for the Netherlands, with the sole exception of Ireland with a correlation of 0.38). The proposed framework was able to forecast quite accurately the ILI trend of the forthcoming influenza season (2016-2017) based only on the available information of the previous years (2011-2016). Furthermore, to broaden the scope of our approach, we applied it both in a forecasting fashion to predict the ILI trend of the 2016-2017 influenza season (Pearson correlations ranging from 0.60 for Ireland and UK, and 0.85 for the Netherlands) and also to detect gastrointestinal syndrome in France (Pearson correlation of 0.66). The final result is a near-real-time flexible surveillance framework not constrained by any specific case definition and capable of capturing the heterogeneity in symptoms circulation during influenza epidemics in the various European countries.

Summary One of the major challenges facing cloud computing is to accurately predict future resource usage to provision data centers for future demands. Cloud resources are constantly in a state of flux, making it difficult for forecasting algorithms to produce accurate predictions for short times scales (ie, 5 minutes to 1 hour). This motivates the research presented in this paper, which compares nonlinear and linear forecasting methods with a sequence prediction algorithm known as a recurrent neural network to predict CPU utilization and network bandwidth usage for live migration. Experimental results demonstrate that a multitime‐ahead prediction algorithm reduces bandwidth consumption during critical times and improves overall efficiency of a data center.

Solving a finite Markov decision process using techniques from dynamic programming such as value or policy iteration require a complete model of the environmental dynamics. The distribution of rewards, transition probabilities, states and actions all need to be fully observable, discrete and complete. For many problem domains, a complete model containing a full representation of the environmental dynamics may not be readily available. Bayesian reinforcement learning (RL)\ is a technique devised to make better use of the information observed through learning than simply computing Q-functions. However, this approach can often require extensive experience in order to build up an accurate representation of the true values. To address this issue, this paper proposes a method for parallelising a Bayesian RL technique aimed at reducing the time it takes to approximate the missing model. We demonstrate the technique on learning next state transition probabilities without prior knowledge. The approach is general enough for approximating any probabilistically driven component of the model. The solution involves multiple learning agents learning in parallel on the same task. Agents share probability density estimates amongst each other in an effort to speed up convergence to the true values.

SUMMARY Public Infrastructure as a Service (IaaS) clouds such as Amazon, GoGrid and Rackspace deliver computational resources by means of virtualisation technologies. These technologies allow multiple independent virtual machines to reside in apparent isolation on the same physical host. Dynamically scaling applications running on IaaS clouds can lead to varied and unpredictable results because of the performance interference effects associated with co‐located virtual machines. Determining appropriate scaling policies in a dynamic non‐stationary environment is non‐trivial. One principle advantage exhibited by IaaS clouds over their traditional hosting counterparts is the ability to scale resources on‐demand. However, a problem arises concerning resource allocation as to which resources should be added and removed when the underlying performance of the resource is in a constant state of flux. Decision theoretic frameworks such as Markov Decision Processes are particularly suited to decision making under uncertainty. By applying a temporal difference, reinforcement learning algorithm known as Q‐learning, optimal scaling policies can be determined. Additionally, reinforcement learning techniques typically suffer from curse of dimensionality problems, where the state space grows exponentially with each additional state variable. To address this challenge, we also present a novel parallel Q‐learning approach aimed at reducing the time taken to determine optimal policies whilst learning online. Copyright © 2012 John Wiley & Sons, Ltd.

Particle swarm optimisation (PSO) is an intelligent random search algorithm, and the key to success is to effectively balance between the exploration of the solution space in the early stages and the exploitation of the solution space in the late stages. This paper presents a new dynamic topology called "gradually increasing directed neighbourhoods (GIDN)" that provides an effective way to balance between exploration and exploitation in the entire iteration process. In our model, each particle begins with a small number of connections and there are many small isolated swarms that improve the exploration ability. At each iteration, we gradually add a number of new connections between particles which improves the ability of exploitation gradually. Furthermore, these connections among particles are created randomly and have directions. We formalise this topology using random graph representations. Experiments are conducted on 31 benchmark test functions to validate our proposed topology. The results show that the PSO with GIDN performs much better than a number of the state of the art algorithms on almost all of the 31 functions.

This paper examines a simulation of the Beer Distribution Game and a number of optimisation approaches to this game. This well known game was developed at MIT in the 1960s and has been widely used to educate graduate students and business managers on the dynamics of supply chains. This game offers a complex simulation environment involving multidimensional constrained parameters. In this research we have examined a traditional genetic algorithm approach to optimising this game, while also for the first time examining a particle swarm optimisation approach. Optimisation is used to determine the best ordering policies across an entire supply chain. This paper will present experimental results for four complex customer demand patterns. We will examine the efficacy of our optimisation approaches and analyse the implications of the results on the Beer Distribution Game. Our experimental results clearly demonstrate the advantages of both genetic algorithm and particle swarm approaches to this complex problem. We will outline a direct comparison of these results, and present a series of conclusions relating to the Beer Distribution Game.

Semantic Web technologies are bring increasingly employed to solve knowledge management issues in traditional Web technologies. This paper follows that trend and proposes using Semantic rule languages to construct rules for defining access control rules for Web Services. Using these rules, a system will be able to manage access to Web Services and also the information accessed via these services.

This paper describes a number of enhancements for the standard CORBA Event Service. The basic service definition has been extended to support stream events, multimedia data flows, event fragmentation, quality of service definition, as well as multicast event delivery. The paper evaluates the service performance and describes experiences using the enhanced service in the development of a test application.