Statistical Approaches to Infectious Diseases Modelling in Developing Countries: A Case of COVID-19

Essential skills required for both statistical consulting and collaboration are mostly informal and are rarely taught in the training institutions in developing countries. These critical skills constitute a significant missing gap and a major hindrance to the growth and development of capacity in statistics and data science practice in developing countries. The advent of LISA 2020 initiative is bridging this gap with a fast-growing network of “stat labs” spread across higher education institutions in Africa, India, Brazil and other parts of the world. This chapter will highlight how LISA 2020 Stat Labs (and other potential labs outside LISA 2020) engage in building capacity to improve informal statistical skills through training and collaborations. In addition, the chapter will review the activities and programs of the stat labs and the contributions being made to bring data science to bear on real-world problems. The chapter plans to draw out lessons that are unique and common to the different stat labs in the network

Evaluating the Provision of Botnet Defences using Translational Research Concepts.

Botnet research frequently draws on concepts from other fields. An example is the use of epidemiological models when studying botnet propagation, which facilitate an understanding of bot spread dynamics and the exploration of behavioural theory. Whilst the literature is rich with these models, it is lacking in work aimed at connecting the insights of theoretical research with day-to-day practice. To address this, we look at botnets through the lens of implementation science, a discipline from the field of translational research in health care, which is designed to evaluate the implementation process. In this paper, we explore key concepts of implementation science, and propose a framework-based approach to improve the provision of security measures to network entities. We demonstrate the approach using existing propagation models, and discuss the role of implementation science in malware defence

Simulation of networks of spiking neurons: A review of tools and strategies

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of Computational Neuroscience, in press (2007

A novel dynamics model of fault propagation and equilibrium analysis in complex dynamical communication network

International audienceTo describe failure propagation dynamics in complex dynamical communication networks, we propose an efficient and compartmental standard-exception-failure propagation dynamics model based on the method of modeling disease propagation in social networks. Mathematical formulas are derived and differential equations are solved to analyze the equilibrium of the propagation dynamics. Stability is evaluated in terms of the balance factor G and it is shown that equilibrium where the number of nodes in different states does not change, is globally asymptotically stable if G≥1. The theoretical results derived are verified by numerical simulations. We also investigate the effect of some network parameters, e.g. node density and node movement speed, on the failure propagation dynamics in complex dynamical communication networks to gain insights for effective measures of control of the scale and duration of the failure propagation in complex dynamical communication networks

Early detection and control of potential pandemics.

Early information is crucial for policy makers and public health officials responsible for protecting the public from the virulent spread of contagious diseases. Current indicators of the spread of contagious outbreaks lag behind the actual spread of the epidemic, leaving no time for a planned response. The studies of Christakis et al. in 2010 have shown that social networks can provide more timely information for prediction. Our focus, however, is on the effective control of the spread of contagious outbreaks in their early stages. We do this by defining a more effective way to chart the spread of contagious outbreaks, in a spatio-temporal sense, so that effective control actions can be taken. In this paper, we use information from sensors , such as, First Watch and EARS (Early Aberration Response Systems) and central individuals in social networks for early spatio-temporal prediction of virulent contagious outbreaks as a means to allocate resources to nip a potential pandemic in the bud. Specifically we combine the research of Christakis et. al on social networks and that of Hongbo Yu on spatio-temporal prediction of human activities to chart the spread of a virulent disease

Architectures and GPU-Based Parallelization for Online Bayesian Computational Statistics and Dynamic Modeling

Recent work demonstrates that coupling Bayesian computational statistics methods with dynamic models can facilitate the analysis of complex systems associated with diverse time series, including those involving social and behavioural dynamics. Particle Markov Chain Monte Carlo (PMCMC) methods constitute a particularly powerful class of Bayesian methods combining aspects of batch Markov Chain Monte Carlo (MCMC) and the sequential Monte Carlo method of Particle Filtering (PF). PMCMC can flexibly combine theory-capturing dynamic models with diverse empirical data. Online machine learning is a subcategory of machine learning algorithms characterized by sequential, incremental execution as new data arrives, which can give updated results and predictions with growing sequences of available incoming data. While many machine learning and statistical methods are adapted to online algorithms, PMCMC is one example of the many methods whose compatibility with and adaption to online learning remains unclear. In this thesis, I proposed a data-streaming solution supporting PF and PMCMC methods with dynamic epidemiological models and demonstrated several successful applications. By constructing an automated, easy-to-use streaming system, analytic applications and simulation models gain access to arriving real-time data to shorten the time gap between data and resulting model-supported insight. The well-defined architecture design emerging from the thesis would substantially expand traditional simulation models' potential by allowing such models to be offered as continually updated services. Contingent on sufficiently fast execution time, simulation models within this framework can consume the incoming empirical data in real-time and generate informative predictions on an ongoing basis as new data points arrive. In a second line of work, I investigated the platform's flexibility and capability by extending this system to support the use of a powerful class of PMCMC algorithms with dynamic models while ameliorating such algorithms' traditionally stiff performance limitations. Specifically, this work designed and implemented a GPU-enabled parallel version of a PMCMC method with dynamic simulation models. The resulting codebase readily has enabled researchers to adapt their models to the state-of-art statistical inference methods, and ensure that the computation-heavy PMCMC method can perform significant sampling between the successive arrival of each new data point. Investigating this method's impact with several realistic PMCMC application examples showed that GPU-based acceleration allows for up to 160x speedup compared to a corresponding CPU-based version not exploiting parallelism. The GPU accelerated PMCMC and the streaming processing system can complement each other, jointly providing researchers with a powerful toolset to greatly accelerate learning and securing additional insight from the high-velocity data increasingly prevalent within social and behavioural spheres. The design philosophy applied supported a platform with broad generalizability and potential for ready future extensions. The thesis discusses common barriers and difficulties in designing and implementing such systems and offers solutions to solve or mitigate them

Thermodynamical Material Networks for Modeling, Planning and Control of Circular Material Flows

Waste production, carbon dioxide atmospheric accumulation and dependence on finite natural resources are expressions of the unsustainability of the current industrial networks that supply fuels, energy and manufacturing products. In particular, circular manufacturing supply chains and carbon control networks are urgently needed. To model and design these and, in general, any material networks, we propose to generalize the approach used for traditional networks such as water and thermal power systems using compartmental dynamical systems thermodynamics, graph theory and the force-voltage analogy. The generalized modeling methodology is explained, then challenges and future research directions are discussed. We hope this paper inspires to use dynamical systems and control, which are typically techniques used for industrial automation, for closing material flows, which is an issue of primary concern in industrial ecology and circular economy.Comment: Perspective paper in preparatio