Seir immune strategy for instance weighted naive bayes classification

© Springer International Publishing Switzerland 2015. Naive Bayes (NB) has been popularly applied in many classification tasks. However, in real-world applications, the pronounced advantage of NB is often challenged by insufficient training samples. Specifically, the high variance may occur with respect to the limited number of training samples. The estimated class distribution of a NB classier is inaccurate if the number of training instances is small. To handle this issue, in this paper, we proposed a SEIR (Susceptible, Exposed, Infectious and Recovered) immune-strategy-based instance weighting algorithm for naive Bayes classification, namely SWNB. The immune instance weighting allows the SWNB algorithm adjust itself to the data without explicit specification of functional or distributional forms of the underlying model. Experiments and comparisons on 20 benchmark datasets demonstrated that the proposed SWNB algorithm outperformed existing state-of-the-art instance weighted NB algorithm and other related computational intelligence methods

Predictive analytics framework for electronic health records with machine learning advancements : optimising hospital resources utilisation with predictive and epidemiological models

The primary aim of this thesis was to investigate the feasibility and robustness of predictive machine-learning models in the context of improving hospital resources’ utilisation with data- driven approaches and predicting hospitalisation with hospital quality assessment metrics such as length of stay. The length of stay predictions includes the validity of the proposed methodological predictive framework on each hospital’s electronic health records data source. In this thesis, we relied on electronic health records (EHRs) to drive a data-driven predictive inpatient length of stay (LOS) research framework that suits the most demanding hospital facilities for hospital resources’ utilisation context. The thesis focused on the viability of the methodological predictive length of stay approaches on dynamic and demanding healthcare facilities and hospital settings such as the intensive care units and the emergency departments. While the hospital length of stay predictions are (internal) healthcare inpatients outcomes assessment at the time of admission to discharge, the thesis also considered (external) factors outside hospital control, such as forecasting future hospitalisations from the spread of infectious communicable disease during pandemics. The internal and external splits are the thesis’ main contributions. Therefore, the thesis evaluated the public health measures during events of uncertainty (e.g. pandemics) and measured the effect of non-pharmaceutical intervention during outbreaks on future hospitalised cases. This approach is the first contribution in the literature to examine the epidemiological curves’ effect using simulation models to project the future hospitalisations on their strong potential to impact hospital beds’ availability and stress hospital workflow and workers, to the best of our knowledge. The main research commonalities between chapters are the usefulness of ensembles learning models in the context of LOS for hospital resources utilisation. The ensembles learning models anticipate better predictive performance by combining several base models to produce an optimal predictive model. These predictive models explored the internal LOS for various chronic and acute conditions using data-driven approaches to determine the most accurate and powerful predicted outcomes. This eventually helps to achieve desired outcomes for hospital professionals who are working in hospital settings

Epidemic processes in complex networks

In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.Comment: 62 pages, 15 figures, final versio

Machine learning and applications in microbiology

To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence application called machine learning. Applying machine learning to address biological problems is expected to grow at an unprecedented rate, yet it is perceived by the uninitiated as a mysterious and daunting entity entrusted to the domain of mathematicians and computer scientists. The aim of this review is to identify key points required to start the journey of becoming an effective machine learning practitioner. These key points are further reinforced with an evaluation of how machine learning has been applied so far in a broad scope of real-life microbiology examples. This includes predicting drug targets or vaccine candidates, diagnosing microorganisms causing infectious diseases, classifying drug resistance against antimicrobial medicines, predicting disease outbreaks and exploring microbial interactions. Our hope is to inspire microbiologists and other related researchers to join the emerging machine learning revolution

Incorporating Particle Filtering and System Dynamic Modelling in Infection Transmission of Measles and Pertussis

Childhood viral and bacterial infections remain an important public problem, and research into their dynamics has broader scientific implications for understanding both dynamical systems and associated methodologies at the population level. Measles and pertussis are two important childhood infectious diseases. Measles is a highly transmissible disease and is one of the leading causes of death among young children under 5 globally. Pertussis (whooping cough) is another common childhood infectious disease, which is most harmful for babies and young children and can be deadly. While the use of ongoing surveillance data and - recently - dynamic models offer insight on measles (or pertussis) dynamics, both suffer notable shortcomings when applied to measles (or pertussis) outbreak prediction. In this thesis, I apply the Sequential Monte Carlo approach of particle filtering, incorporating reported measles and pertussis incidence for Saskatchewan during the pre-vaccination era, using an adaptation of a previously contributed measles and pertussis compartmental models. To secure further insight, I also perform particle filtering on age structured adaptations of the models. For some models, I further consider two different methods of configuring the contact matrix. The results indicate that, when used with a suitable dynamic model, particle filtering can offer high predictive capacity for measles and pertussis dynamics and outbreak occurrence in a low vaccination context. Based on the most competitive model as evaluated by predictive accuracy, I have performed prediction and outbreak classification analysis. The prediction results demonstrated that the most competitive models could predict the measles and pertussis outbreak patterns and classify whether there will be an outbreak or not in the next month (Area under the ROC Curve of measles is 0.89, while pertussis is 0.91). I conclude that anticipating the outbreak dynamics of measles and pertussis in low vaccination regions by applying particle filtering with simple measles and pertussis transmission models, and incorporating time series of reported case counts, is a valuable technique to assist public health authorities in estimating risk and magnitude of measles and pertussis outbreaks. Such approach offers particularly strong value proposition for other pathogens with little-known dynamics, important latent drivers, and in the context of the growing number of high-velocity electronic data sources. Strong additional benefits are also likely to be realized from extending the application of this technique to highly vaccinated populations

Scalable Bayesian inference for stochastic epidemic processes

The research reported in this thesis is motivated by the goal of using mathematical models to better understand the within-herd disease dynamics of Bovine tuberculosis (BTB) in UK cattle. This led to the development of new Bayesian methods and tools, including an open-source software package for Bayesian data analysis. In particular, those applicable to Discrete-state-space Partially Observed Markov Processes (DPOMP models). These were applied to the problem of model and parameter inference for a sample of individual herds selected from UK BTB surveillance records. Those findings led to the alternative models and methods utilised in the penultimate chapter, where we present a large scale, system-of-herds model and report novel parameter estimates for BTB. The latter include those that relate to disease detection; regional background risk; and farmer behaviour (specifically, the trading of live cattle). The work goes beyond previous, similar published research in three ways: it incorporates individual herd records, not just aggregated data; it includes formal methods of model assessment; that work is (partially) extended to systems comprising up to thousands of herds

Applications of Artificial Intelligence in Battling Against Covid-19: A Literature Review

© 2020 Elsevier Ltd. All rights reserved.Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.Peer reviewe

Book of Abstracts of SPE 2021

Book of Abstracts of SPE 202