A Practical Approach to Subset Selection for Multi-objective Optimization via Simulation

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordWe describe a practical two-stage algorithm, BootComp, for multi-objective optimization via simulation. Our algorithm finds a subset of good designs that a decision-maker can compare to identify the one that works best when considering all aspects of the system, including those that cannot be modeled. BootComp is designed to be straightforward to implement by a practitioner with basic statistical knowledge in a simulation package that does not support sequential ranking and selection. These requirements restrict us to a two-stage procedure that works with any distributions of the outputs and allows for the use of common random numbers. Comparisons with sequential ranking and selection methods suggest that it performs well, and we also demonstrate its use analyzing a real simulation aiming to determine the optimal ward configuration for a UK hospital.National Institute for Health Research (NIHR

Global Peak in Atmospheric Radiocarbon Provides a Potential Definition for the Onset of the Anthropocene Epoch in 1965

Anthropogenic activity is now recognised as having profoundly and permanently altered the Earth system, suggesting we have entered a human-dominated geological epoch, the ‘Anthropocene’. To formally define the onset of the Anthropocene, a synchronous global signature within geological-forming materials is required. Here we report a series of precisely-dated tree-ring records from Campbell Island (Southern Ocean) that capture peak atmospheric radiocarbon (14C) resulting from Northern Hemisphere-dominated thermonuclear bomb tests during the 1950s and 1960s. The only alien tree on the island, a Sitka spruce (Picea sitchensis), allows us to seasonally-resolve Southern Hemisphere atmospheric 14C, demonstrating the ‘bomb peak’ in this remote and pristine location occurred in the last-quarter of 1965 (October-December), coincident with the broader changes associated with the post-World War II ‘Great Acceleration’ in industrial capacity and consumption. Our findings provide a precisely-resolved potential Global Stratotype Section and Point (GSSP) or ‘golden spike’, marking the onset of the Anthropocene Epoch

Long Delays and Missed Opportunities in Diagnosing Smear-Positive Pulmonary Tuberculosis in Kampala, Uganda: A Cross-Sectional Study

BACKGROUND: Early detection and treatment of tuberculosis cases are the hallmark of successful tuberculosis control. We conducted a cross-sectional study at public primary health facilities in Kampala city, Uganda to quantify diagnostic delay among pulmonary tuberculosis (PTB) patients, assess associated factors, and describe trajectories of patients' health care seeking. METHODOLOGY/PRINCIPAL FINDINGS: Semi-structured interviews with new smear-positive PTB patients (≥ 15 years) registered for treatment. Between April 2007 and April 2008, 253 patients were studied. The median total delay was 8 weeks (IQR 4-12), median patient delay was 4 weeks (inter-quartile range [IQR] 1-8) and median health service delay was 4 weeks (IQR 2-8). Long total delay (>14 weeks) was observed for 61/253 (24.1%) of patients, long health service delay (>6 weeks) for 71/242 (29.3%) and long patient delay (>8 weeks) for 47/242 (19.4%). Patients who knew that TB was curable were less likely to have long total delay (adjusted Odds Ratio [aOR] 0.28; 95%CI 0.11-0.73) and long patient delay (aOR 0.36; 95%CI 0.13-0.97). Being female (aOR 1.98; 95%CI 1.06-3.71), staying for more than 5 years at current residence (aOR 2.24 95%CI 1.18-4.27) and having been tested for HIV before (aOR 3.72; 95%CI 1.42-9.75) was associated with long health service delay. Health service delay contributed 50% of the total delay. Ninety-one percent (231) of patients had visited one or more health care providers before they were diagnosed, for an average (median) of 4 visits (range 1-30). All but four patients had systemic symptoms by the time the diagnosis of TB was made. CONCLUSIONS/SIGNIFICANCE: Diagnostic delay among tuberculosis patients in Kampala is common and long. This reflects patients waiting too long before seeking care and health services waiting until systemic symptoms are present before examining sputum smears; this results in missed opportunities for diagnosis

Periodic Active Case Finding for TB: When to Look?

OBJECTIVE: To investigate the factors influencing the performance and cost-efficacy of periodic rounds of active case finding (ACF) for TB. METHODS: A mathematical model of TB dynamics and periodic ACF (PACF) in the HIV era, simplified by assuming constant prevalence of latent TB infection, is analyzed for features that control intervention outcome, measured as cases averted and cases found. Explanatory variables include baseline TB incidence, interval between PACF rounds, and different routine and PACF case-detection rates among HIV-infected and uninfected TB cases. FINDINGS: PACF can be cost-saving over a 10 year time frame if the cost-per-round is lower than a threshold proportional to initial incidence and cost-per-case-treated. More cases are averted at higher baseline incidence rates, when more potent PACF strategies are used, intervals between PACF rounds are shorter, and when the ratio of HIV-negative to positive TB cases detected is higher. More costly approaches, e.g. radiographic screening, can be as cost-effective as less costly alternatives if PACF case-detection is higher and/or implementation less frequent. CONCLUSION: Periodic ACF can both improve control and save medium-term health care costs in high TB burden settings. Greater costs of highly effective PACF at frequent (e.g. yearly) intervals may be offset by higher numbers of cases averted in populations with high baseline TB incidence, higher prevalence of HIV-uninfected cases, higher costs per-case-treated, and more effective routine case-detection. Less intensive approaches may still be cost-neutral or cost-saving in populations lacking one or more of these key determinants

A simulation modelling toolkit for organising outpatient dialysis services during the COVID-19 pandemic

This study presents two simulation modelling tools to support the organisation of networks of dialysis services during the COVID-19 pandemic. These tools were developed to support renal services in the South of England (the Wessex region caring for 650 dialysis patients), but are applicable elsewhere. A discrete-event simulation was used to model a worst case spread of COVID-19, to stress-test plans for dialysis provision throughout the COVID-19 outbreak. We investigated the ability of the system to manage the mix of COVID-19 positive and negative patients, the likely effects on patients, outpatient workloads across all units, and inpatient workload at the centralised COVID-positive inpatient unit. A second Monte-Carlo vehicle routing model estimated the feasibility of patient transport plans. If current outpatient capacity is maintained there is sufficient capacity in the South of England to keep COVID-19 negative/recovered and positive patients in separate sessions, but rapid reallocation of patients may be needed. Outpatient COVID-19 cases will spillover to a secondary site while other sites will experience a reduction in workload. The primary site chosen to manage infected patients will experience a significant increase in outpatients and inpatients. At the peak of infection, it is predicted there will be up to 140 COVID-19 positive patients with 40 to 90 of these as inpatients, likely breaching current inpatient capacity. Patient transport services will also come under considerable pressure. If patient transport operates on a policy of one positive patient at a time, and two-way transport is needed, a likely scenario estimates 80 ambulance drive time hours per day (not including fixed drop-off and ambulance cleaning times). Relaxing policies on individual patient transport to 2-4 patients per trip can save 40-60% of drive time. In mixed urban/rural geographies steps may need to be taken to temporarily accommodate renal COVID-19 positive patients closer to treatment facilities.This article is freely available via Open Access. Click on the Publisher URL to access it via the publisher's site.This article presents independent research funded by the National Institute for Health Research (NIHR) Applied Research Collaboration (ARC) South West Peninsula (MA, SL). The views expressed in this publication are those of the author(s) and not necessarily those of the National Health Service, the NIHR or the Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Operational Research: methods and applications

This is the final version. Available on open access from Taylor & Francis via the DOI in this recordThroughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion and used as a point of reference by a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

Optimizing Pricing and Packing of Variable-Sized Cargo

Organizations have successfully used dynamic pricing to optimize revenues for many years, where research and practice have mainly focused on applications with independent, discrete commodities; for example, an airline ticket. In this research we consider applications where the commodity is continuous and the value of the commodity available to sell depends on the combination of previously accepted demand. We focus on vehicle ferries, where the accepted vehicle bookings are packed in lanes in the ferry to leave a usable space for future bookings. Certain combinations of vehicles may result in areas of unusable space, which will affect future revenue. While this application is the focus of the paper, there are numerous industries that face similar challenges including freight and the sale of advertising on television and radio. In this paper, we simultaneously solve the pricing and resource utilization problem to optimality for a discrete set of product types and stochastic demand. Our approach combines a dynamic pricing model with a mixed-integer linear program to optimize the packing. We present results for real-world examples from the ferry industry and discuss extensions to the method to improve the selection of vehicle configurations