Quantitative proteomics plays a central role in uncovering regulatory mechanisms, identifying disease biomarkers, and guiding the development of precision therapies. These insights are often obtained through complex Bayesian models, whose inference procedures are computationally intensive, especially when applied at scale to biological datasets. This limits the accessibility of advanced modelling techniques needed to fully exploit proteomics data. Although Sequential Monte Carlo (SMC) methods offer a parallelisable alternative to traditional Markov Chain Monte Carlo, their high-performance implementations often rely on specialised hardware, increasing both financial and energy costs. We address these challenges by introducing an opportunistic computing framework for SMC samplers, tailored to the demands of large-scale proteomics inference. Our approach leverages idle compute resources at the University of Liverpool via HTCondor, enabling scalable Bayesian inference without dedicated high-performance computing infrastructure. Central to this framework is a novel Coordinator-Manager-Follower architecture that reduces synchronisation overhead and supports robust operation in heterogeneous, unreliable environments. We evaluate the framework on a realistic proteomics model and show that opportunistic SMC delivers accurate inference with weak scaling, increasing samples generated under a fixed time budget as more resources join. To support adoption, we release CondorSMC, an open-source package for deploying SMC samplers in opportunistic computing environments.

Markov Chain Monte Carlo (MCMC) is a method for drawing samples from non-standard probability distributions. Hamiltonian Monte Carlo (HMC) is a popular variant of MCMC that uses gradient information to explore the target distribution. The Sequential Monte Carlo (SMC) sampler is an alternative sampling method which, unlike MCMC, can readily utilise parallel computing architectures. It is typical within SMC literature to target a tempered distribution using a proposal with an accept/reject mechanism. In this letter, we show how the proposal used in the No-U-Turn Sampler (NUTS), an advanced variant of HMC, can be incorporated into an SMC sampler without an accept/reject mechanism. Empirical results show that this can remove the need for tempering and gives rise to accurate estimates being generated in fewer iterations which motivates this technique being deployed on parallel hardware.

The emergence of the novel coronavirus (COVID-19) generated a need to quickly and accurately assemble up-to-date information related to its spread. In this research article, we propose two methods in which Twitter is useful when modelling the spread of COVID-19: (1) machine learning algorithms trained in English, Spanish, German, Portuguese and Italian are used to identify symptomatic individuals derived from Twitter. Using the geo-location attached to each tweet, we map users to a geographic location to produce a time-series of potential symptomatic individuals. We calibrate an extended SEIRD epidemiological model with combinations of low-latency data feeds, including the symptomatic tweets, with death data and infer the parameters of the model. We then evaluate the usefulness of the data feeds when making predictions of daily deaths in 50 US States, 16 Latin American countries, 2 European countries and 7 NHS (National Health Service) regions in the UK. We show that using symptomatic tweets can result in a 6% and 17% increase in mean squared error accuracy, on average, when predicting COVID-19 deaths in US States and the rest of the world, respectively, compared to using solely death data. (2) Origin/destination (O/D) matrices, for movements between seven NHS regions, are constructed by determining when a user has tweeted twice in a 24 h period in two different locations. We show that increasing and decreasing a social connectivity parameter within an SIR model affects the rate of spread of a disease.

The emergence of the novel coronavirus (COVID-19) has generated a need to quickly and accurately assemble up-to-date information related to its spread. While it is possible to use deaths to provide a reliable information feed, the latency of data derived from deaths is significant. Confirmed cases derived from positive test results potentially provide a lower latency data feed. However, the sampling of those tested varies with time and the reason for testing is often not recorded. Hospital admissions typically occur around 1-2 weeks after infection and can be considered out of date in relation to the time of initial infection. The extent to which these issues are problematic is likely to vary over time and between countries. We use a machine learning algorithm for natural language processing, trained in multiple languages, to identify symptomatic individuals derived from social media and, in particular Twitter, in real-time. We then use an extended SEIRD epidemiological model to fuse combinations of low-latency feeds, including the symptomatic counts from Twitter, with death data to estimate parameters of the model and nowcast the number of people in each compartment. The model is implemented in the probabilistic programming language Stan and uses a bespoke numerical integrator. We present results showing that using specific low-latency data feeds along with death data provides more consistent and accurate forecasts of COVID-19 related deaths than using death data alone.

This paper analyses the Taylor rule and its application to monetary policy in the United Kingdom and Euro area. The analysis uses a linear regression on quarterly economic data from 1993Q1 to 2017Q4 for the United Kingdom and 2000Q1 to 2016Q4 for the Euro area. The results show that the Taylor rule does not fully describe the monetary policy actions made by the Bank of England and European Central Bank over the period analysed; and, that both central banks engage in a significant level of interest rate smoothing. The results also suggest that the Taylor rule does not provide the rationale for quantitative easing within the two regions and that interest rates should be higher than they currently are.