A Hybrid Modelling Framework for Real-time Decision-support for Urgent and Emergency Healthcare

In healthcare, opportunities to use real-time data to support quick and effective decision-making are expanding rapidly, as data increases in volume, velocity and variety. In parallel, the need for short-term decision-support to improve system resilience is increasingly relevant, with the recent COVID-19 crisis underlining the pressure that our healthcare services are under to deliver safe, effective, quality care in the face of rapidly-shifting parameters. A real-time hybrid model (HM) which combines real-time data, predictions, and simulation, has the potential to support short-term decision-making in healthcare. Considering decision-making as a consequence of situation awareness focuses the HM on what information is needed where, when, how, and by whom with a view toward sustained implementation. However the articulation between real-time decision-support tools and a sociotechnical approach to their development and implementation is currently lacking in the literature. Having identified the need for a conceptual framework to support the development of real-time HMs for short-term decision-support, this research proposed and tested the Integrated Hybrid Analytics Framework (IHAF) through an examination of the stages of a Design Science methodology and insights from the literature examining decision-making in dynamic, sociotechnical systems, data analytics, and simulation. Informed by IHAF, a HM was developed using real-time Emergency Department data, time-series forecasting, and discrete-event simulation. The application started with patient questionnaires to support problem definition and to act as a formative evaluation, and was subsequently evaluated using staff interviews. Evaluation of the application found multiple examples where the objectives of people or sub-systems are not aligned, resulting in inefficiencies and other quality problems, which are characteristic of complex adaptive sociotechnical systems. Synthesis of the literature, the formative evaluation, and the final evaluation found significant themes which can act as antecedents or evaluation criteria for future real-time HM studies in sociotechnical systems, in particular in healthcare. The generic utility of IHAF is emphasised for supporting future applications in similar domains

Adequacy of SEIR models when epidemics have spatial structure: Ebola in Sierra Leone.

Dynamic SEIR (Susceptible, Exposed, Infectious, Removed) compartmental models provide a tool for predicting the size and duration of both unfettered and managed outbreaks-the latter in the context of interventions such as case detection, patient isolation, vaccination and treatment. The reliability of this tool depends on the validity of key assumptions that include homogeneity of individuals and spatio-temporal homogeneity. Although the SEIR compartmental framework can easily be extended to include demographic (e.g. age) and additional disease (e.g. healthcare workers) classes, dependence of transmission rates on time, and metapopulation structure, fitting such extended models is hampered by both a proliferation of free parameters and insufficient or inappropriate data. This raises the question of how effective a tool the basic SEIR framework may actually be. We go some way here to answering this question in the context of the 2014-2015 outbreak of Ebola in West Africa by comparing fits of an SEIR time-dependent transmission model to both country- and district-level weekly incidence data. Our novel approach in estimating the effective-size-of-the-populations-at-risk ( Neff) and initial number of exposed individuals ( E0) at both district and country levels, as well as the transmission function parameters, including a time-to-halving-the-force-of-infection ( tf/2) parameter, provides new insights into this Ebola outbreak. It reveals that the estimate R0 ≈ 1.7 from country-level data appears to seriously underestimate R0 ≈ 3.3 - 4.3 obtained from more spatially homogeneous district-level data. Country-level data also overestimate tf/2 ≈ 22 weeks, compared with 8-10 weeks from district-level data. Additionally, estimates for the duration of individual infectiousness is around two weeks from spatially inhomogeneous country-level data compared with 2.4-4.5 weeks from spatially more homogeneous district-level data, which estimates are rather high compared with most values reported in the literature. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'

A multi-agent architecture for internet distributed computing system

This thesis presents the developed taxonomy of the agent-based distributed computing systems. Based on this taxonomy, a design, implementation, analysis and distribution protocol of a multi-agent architecture for internet-based distributed computing system was developed. A prototype of the designed architecture was implemented on Spider III using the IBM Aglets software development kit (ASDK 2.0) and the language Java

Best-subset Selection for Complex Systems using Agent-based Simulation

It is difficult to analyze and determine strategies to control complex systems due to their inherent complexity. The complex interactions among elements make it difficult to develop and test decision makers' intuition of how the system will behave under different policies. Computer models are often used to simulate the system and to observe both direct and indirect effects of alternative interventions. However, many decision makers are unwilling to concede complete control to a computer model because of the abstractions in the model, and the other factors that cannot be modeled, such as physical, human, social and organizational relationship constraints. This dissertation develops an agent-based simulation (ABS) model to analyze a complex system and its policy alternatives, and contributes a best-subset selection (BSS) procedure that provides a group of good performing alternatives to which decision makers can then apply their subject and context knowledge in making a final decision for implementation. As a specific example of a complex system, a mass casualty incident (MCI) response system was simulated using an ABS model consisting of three interrelated sub-systems. The model was then validated by a series of sensitivity analysis experiments. The model provides a good test bed to evaluate various evacuation policies. In order to find the best policy that minimizes the overall mortality, two ranking-and-selection (R&S) procedures from the literature (Rinott (1978) and Kim and Nelson (2001)) were implemented and compared. Then a new best-subset selection (BSS) procedure was developed to efficiently select a statistically guaranteed best-subset containing all alternatives that are close enough to the best one for a pre-specified probability. Extensive numerical experiments were organized to prove the effectiveness and demonstrate the performance of the BSS procedure. The BSS procedure was then implemented in conjunction with the MCI ABS model to select the best evacuation policies. The experimental results demonstrate the feasibility and effectiveness of our agent-based optimization methodology for complex system policy evaluation and selection