We describe the population-based susceptible-exposed-infected-removed (SEIR) model developed by the Irish Epidemiological Modelling Advisory Group (IEMAG), which advises the Irish government on COVID-19 responses. The model assumes a time-varying effective contact rate (equivalently, a time-varying reproduction number) to model the effect of non-pharmaceutical interventions. A crucial technical challenge in applying such models is their accurate calibration to observed data, e.g. to the daily number of confirmed new cases, as the history of the disease strongly affects predictions of future scenarios. We demonstrate an approach based on inversion of the SEIR equations in conjunction with statistical modelling and spline-fitting of the data to produce a robust methodology for calibration of a wide class of models of this type. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

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A model for the spreading of online information or 'memes' on multiplex networks is introduced and analyzed using branching-process methods. The model generalizes that of (Gleeson et al 2016 Phys. Rev. X) in two ways. First, even for a monoplex (single-layer) network, the model is defined for any specific network defined by its adjacency matrix, instead of being restricted to an ensemble of random networks. Second, a multiplex version of the model is introduced to capture the behavior of users who post information from one social media platform to another. In both cases the branching process analysis demonstrates that the dynamical system is, in the limit of low innovation, poised near a critical point, which is known to lead to heavy-tailed distributions of meme popularity similar to those observed in empirical data. IntroductionThe advent of social media and the resulting ability to instantaneously communicate ideas and messages to connections worldwide is one of the great consequences arising from the telecommunications revolution over the last century. Individuals do not, however, communicate only upon a single platform; instead there exists a plethora of options available to users, many of whom are active on a number of such media. While each platform offers some unique selling point to attract users, e.g. keeping up to date with friends through messaging and statuses (Facebook), photo sharing (Instagram), seeing information from friends, celebrities and numerous other outlets (Twitter) or keeping track of the career paths of friends and past colleagues (Linkedin), the platforms are all based upon the fundamental mechanisms of connecting with other users and transmitting information to them as a result of this link.The dynamics of information flow in online social media is an active research area (see, for example, [1-8]) but analytically solvable models are relatively rare. An analytically tractable model for the spreading of 'memes' (the term is used here in the general sense of a piece of digital information) on a single platform for a directed network was introduced in [9]. This model suggested that as a result of the large amount of information received by users on such platforms competing for their limited attention, the system is poised near criticality. The implications of the near-criticality of such systems include fat-tailed distributions of meme popularity, similar to those observed in empirical studies [3]. The model also incorporated memory effects by allowing users to look backwards a random amount of time in their stream or feed to 'retweet' a meme, which gives a non-Markovian model for this aspect of human behavior [10,11]. Interestingly, while this was a null model in the sense that it was entirely derived from first principles with no assumptions being made regarding the parameters determining the spreading process, it could accurately describe observed cascades of 'tweet' popularities on the directed network Twitter.The model of [9] was, however, overly simplistic in several respects. Fir...

In all competitions where results are based upon an individual’s performance the question of whether the outcome is a consequence of skill or luck arises. We explore this question through an analysis of a large dataset of approximately one million contestants playing Fantasy Premier League, an online fantasy sport where managers choose players from the English football (soccer) league. We show that managers’ ranks over multiple seasons are correlated and we analyse the actions taken by managers to increase their likelihood of success. The prime factors in determining a manager’s success are found to be long-term planning and consistently good decision-making in the face of the noisy contests upon which this game is based. Similarities between managers’ decisions over time that result in the emergence of ‘template’ teams, suggesting a form of herding dynamics taking place within the game, are also observed. Taken together, these findings indicate common strategic considerations and consensus among successful managers on crucial decision points over an extended temporal period.