21 minute read
Seen Elsewhere
FIGHTING MULTI-DRUG RESISTANT TB TDR, the Special Programme for Research and Training in Tropical Diseases, is a global programme of scientific collaboration that helps facilitate, support and influence efforts to combat diseases of poverty. In collaboration with the WHO Global Tuberculosis Programme and technical partners, TDR has developed an operational research package (dubbed ShORRT for Short all-Oral Regimens for Rifampicin-resistant Tuberculosis) to support the implementation of such drug regimens.
The ShORRT initiative now involves and supports 25 countries worldwide. In the latest World Health Organization (WHO) guidelines on drug-resistant tuberculosis (DRTB) treatment, modifications to the recommended all-oral treatments for multidrug- and rifampicin-resistant (MDR/RR) TB and novel regimens for patients with extensively drug-resistant TB are encouraged under operational research conditions.
Dr. Babawale Adekunle Victor, Programmatic Management of Drug Resistant Tuberculosis Unit, National Tuberculosis and Leprosy Control Programme, Nigeria has said “Operational Research is proving to be a tremendous tool for guiding our efforts to manage TB in Nigeria, especially in the context of COVID-19”.
DATA MATURITY OF THE NOT-FOR-PROFIT SECTOR Following 15 months and thousands of people using Data Orchard’s Data Maturity Assessment Tool, they crunched the numbers to analyse what the tool’s data tells us about the current state of data maturity in the not-forprofit sector. The results have been published in State of the Sector - Data Maturity in the Not-for-Profit Sector 2020. See http://bit.ly/3hQXnPG.
What the data tells us about not-forprofit data maturity, inter alia, is that the cost of data is huge, hidden, and often wasted, that most leaders don’t see the value of data, that there’s lots of counting but not enough meaningful analysis, and that most not-for-profits lack skills, responsibility and support around data.
DOMO STREAMLINES EMBEDDED ANALYTICS PLATFORM In June 2021 Domo, a cloud-based analytics vendor founded in 2010 and based in Utah, unveiled the latest version of its embedded analytics platform, adding new capabilities while updating others and consolidating all of them under a single umbrella.
When first released in 2017, the embedded analytics platform was made up of three distinct offerings. Domo White Label enabled customers to develop their own analytics applications and market them to their own customers under their own brand. Domo Embed enabled customers to build and embed basic applications such as reports and dashboards. And Domo Publish enabled users to share their analytic assets both with others in their organizations and with outside customers.
The updated version of Domo Everywhere combines the three offerings into a single experience so users can more easily and quickly build and distribute embedded applications. In addition, the new iteration of Domo Everywhere enables users to build, market and share more advanced analytics applications— including machine learning models and other assets built using augmented intelligence capabilities—than they could using previous iterations of Domo Everywhere.
PEST CONTROL SIMULATION Researchers at North Carolina State University have developed a simulation model to predict when and where pests and diseases will attack crops or forests. Their computer modelling system works by combining information on climate conditions suitable for spread of a certain disease or pest with data
on where cases have been recorded, the reproductive rate of the pathogen or pest and how it moves in the environment. Over time, the model improves as natural resource managers add data they gather from the field. This repeated feedback with new data helps the forecasting system get better at predicting future spread. “We have a tool that can be put into the hands of a non-technical user to learn about disease dynamics and management, and how management decisions will affect spread in the future,” lead author Chris Jones said. A report is available in the journal Frontiers in Ecology and the Environment at https://doi. org/10.1002/fee.2357.
IT’S THE CONFERENCE SEASON There has been a flurry of conferences recently. Not the political party conferences, but O.R. conferences: first the regional (EURO) in July, then the international (IFORS) in August and finally the national (OR Society) in September. The first two were hybrid, but all could be attended virtually. There were many examples presented of O.R./analytics making an impact, including several UK-based contributions.
The finalists in the EURO Excellence in Practice Award included Nav Mustafee and John Powell’s work, NHSQuicker, which was featured in the Spring 2021 issue of Impact and the winning entry of Gilberto Montibeller and Alberto Franco. Their work on facilitated decision analysis for emerging health threats enhanced the quality of health experts’ recommendations to the UK Department for Environment, Food and Rural Affairs leadership in the prioritisation of animal and human emerging health threats and informed new international standards for the Food Standards joint programmes of the Food and Agriculture Organization of the United Nations and the World Health Organization.
The finalists in the IFORS Prize for O.R. in Development competition included Paola Scaparra, who presented the work of a team of researchers concerned to minimize flood impact on the road infrastructure in Vietnam. Their work, funded by the Global Challenge Research Fund through the British Academy, identified costefficient measures to mitigate the impacts of urban flooding and created an optimisation tool embedded into a Decision Support System which was applied to generate a 20-year plan of investments. More than half of the total reduction in congestion and damage was found to be achievable with less than 25% of available budgets. The project has catalysed interest in Vietnam and in neighbouring countries to develop capacity in O.R. to address development challenges.
At the OR Society conference, Lucy Morgan spoke about modelling of waiting lists for diagnosis and treatment of chronic heart failure following the Covid-19 pandemic, which has disrupted access to health services for patients with non-Covid-19 conditions due, not just to increased demand, but also as a result of the associated lockdowns and social distancing measures. A discrete event simulation model was built to describe the impact of the pandemic and associated societal lockdowns on access to diagnosis procedures. Clinicians at Leeds Royal Infirmary were involved and it is hoped that the interventions found to reduce waiting lists will be put into practice by them. She also reported on work carried out with the Liverpool School of Tropical Medicine to investigate the impact of decentralised treatment pathways for multidrug-resistant TB (MDR-TB) patients in Ethiopia. Reducing costs incurred by MDR-TB patients is a priority of Ethiopia’s National Tuberculosis Programme. Lucy presented a simulation model representing MDRTB patient treatment pathways, which was used to investigate the impact of patient-centred and hybrid treatment strategies on patient costs during treatment and compare this to costs incurred under the current centralised treatment strategy in Ethiopia.
EDGE ANALYTICS As Impact makes clear, analytical insights can be key for organizations to make decisions. Real-time data requires realtime analytics for better decision-making processes. And with growing data volumes from various applications and locations, they need to be effectively managed and analysed. Edge analytics deals with this. Edge analytics ensures automatic, real-time analytical computation of data without sending the data to the centralized data server for analytics or storage. With increasing numbers of connected IoT devices, businesses are making use of edge analytics and leveraging the solutions offered by cloud providers. Using next-generation data
analytical tools, cloud providers like Google, IBM, and Amazon Web Services (AWS) are allowing businesses to unleash greater value from their data.
Despite the challenges of the pandemic, the global market for edge analytics is estimated to reach US$25.4 billion by 2026 according to an industry report by Global Industry Analytics Inc. Edge analysis solutions are expected to grow the fastest with more businesses now realizing the advantages they can have by leveraging data closer to the edge. The world’s leading economies, the US and China, are expected to adapt edge analytics solutions the most.
In addition to several large manufacturers and telecommunication companies, more small and mid-sized companies in the US are contributing to the growth in edge analytics. Smart cities that are extensively fitted with sensors tend to rely heavily on edge analytics for the best experience and service. More at: http://bit.ly/EdgeAnalytics1
DIRECT FLIGHTS SAVE LIVES New research in the INFORMS journal Management Science tackles the transportation part of the kidney transplantation problem (see https:// doi.org/10.1287/mnsc.2021.4103).
“Airline transportation limits the flexibility of organ transplantation, but new, more direct airline routes can increase the number of kidneys shared between regions connected by these routes by more than 7%,” said Tinglong Dai of Johns Hopkins University. “Operations Research and analytics is trying to save lives and allow kidneys to be more readily available to those who need them when they need them. Too often, viable kidneys are wasted because they can’t reach a patient in time.”
The research identifies how new airline routes can provide the necessary efficient airline transportation needed for the time-sensitive nature of kidney transplantation and reduce the number of viable kidneys being wasted because they didn’t reach the patient in time.
The authors analysed U.S. airline transportation and kidney transplantation datasets and used the data to track the evolution of airline routes connecting all U.S. airports. Then they looked at kidney transplants between donors and recipients connected by these airports. “Transportation plays a major role in providing patients with available donations, if new airline routes can increase the volume of shared kidneys by 7.3%, think of how many lives could be saved,” continued Dai. “We also find the post-transplant survival rate remains largely unchanged. It’s a step forward in organ donations thanks to O.R. and analytics”.
E2OPEN TO MODERNIZE TESCO’S GLOBAL LOGISTICS MANAGEMENT E2open, a leading network-based provider of 100% cloud-based, mission-critical, end-to-end supply chain management software, have announced that they are working with Tesco PLC. Tesco will replace its legacy transport management system and utilize the E2open platform for increased visibility, transport execution, invoice processes and supply collaboration.
E2open’s network and applications manage all tiers of production, inventory, logistics, global trade and channel activity from a single platform in the cloud. Tesco will leverage this networked-platform to manage their large global community of suppliers and logistics partners as they serve nearly 4,000 stores around the world. “With the complexity of today’s supply chain there is simply no room for error,” said Michael Farlekas, president and chief executive officer at E2open. “From procurement to logistics to payment, our expansive, integrated platform will provide Tesco with the highest level of visibility into their global supply chain, reducing costs and improving efficiency while providing greater oversight to help avoid consumer shortages.”
E2open will help Tesco improve efficiency through standardizing disparate processes, resulting in better utilization, reduction in detention and demurrage fees, and improve overall stock management. E2open’s platform will also facilitate ordering and ensure that Tesco reaches a better fill rate of its shipping containers and other assets.
© Creations/Shutterstock
SUPPORTING THE UK’S OVERSEAS TERRITORIES DURING THE COVID-19 PANDEMIC
PETE BAILEY AND STRUAN MILLAR
© Crown copyright 2020 DURING THE EARLY PHASES OF THE COVID-19 PANDEMIC, the Government needed to consider not just the needs of the UK, but also of its Overseas Territories and our military personnel deployed abroad. Ministry of Defence (MOD) support to these overseas locations was known as Operation BROADSHARE. The Defence Science and Technology Laboratory (Dstl) was asked to provide Operational Research (referred to as Operational Analysis within MOD) to assess the likely numbers and implications of COVID-19 patients presenting at medical facilities (such as hospitals) in these locations. This article will describe the modelling and analyses conducted by Dstl, covering the process from the collection of data, the tailoring of extant methods to better represent the problem of COVID-19, the types of metrics assessed and the impact of the work conducted. This included the deployment of RFA Argus (pictured here) to provide a medical response capability prior to the hurricane season as well as in support of the pandemic response.
OPERATIONS RESCRIPT AND BROADSHARE For MOD, the pandemic was expected to affect operations overseas and at home, military training, overseas bases and normal departmental business as Defence observed government guidance on working from home where possible, social distancing and essential movement. The Department’s response, therefore, was not only the contribution to the Government’s effort to mitigate the effects of COVID-19 but encompassed its internal resilience activity. Two operations were activated:
• Op RESCRIPT to deliver COV-
ID-19 activity in the UK and
Crown Dependencies, including
Departmental business continuity and resilience, Military Aid to the
Civil Authorities, and other support to government departments and local responders. • Op BROADSHARE delivering operations and activity outside the
UK, including the overseas bases, the
Defence overseas network, overseas training teams, support to the
Overseas Territories, and government overseas operational and business continuity activity.
MEDICAL MODELLING WITHIN MOD The MOD has a duty of care to its service personnel, plus a remit to provide humanitarian aid to those in need. Understanding potential numbers of casualties, alongside the effectiveness of current defence medical capabilities in different operating environments, is vital to ensuring our deployed forces have access to the right medical facilities at the right time. This improves treatment times and, ultimately, survival rates. The Ministry of Defence has a duty of care to its service personnel, plus a remit to provide humanitarian aid to those in need
Dstl developed the ZEUS medical model in 2018 with support from Defence Medical Services (DMS). It is a decision support tool, used to assess medical plans (typically referred to as laydowns) at the operational level, in order to identify potential bottlenecks and limitations within the deployed medical system. This medical system is technically referred to as the Operational Patient Care Pathway and is detailed in Allied Joint Doctrine for Medical Support (AJP-4.10 Edition C Version 1, dated Sept 2019). ZEUS tracks patients (on a minute-by-minute basis) from the Point of Injury (or Illness – for the purposes of this article, it will be assumed that the term injury will include illness), through movement to and treatment at a medical treatment facility (the larger, more established of these can be considered to be small hospitals) to release (such as by returning to their original unit or being evacuated back to facilities within the UK). It was designed to assess questions such as:
• Do we have the correct mix of medical capabilities to support the operation in question? • Are those capabilities appropriately located and do they have sufficient capacity to meet the clinical timelines? The clinical timelines provide planning guidance for the receipt of different levels of treatment, for example, the UK aims to provide advanced resuscitation and pre-hospital emergency care within one hour of injury or onset of acute symptoms. • What are the resource (e.g. personnel and equipment) and logistic (e.g. medications, blood, oxygen, Personal Protective Equipment [PPE]) requirements?
As such it is aligned to the NATO Principles and Policies of Medical Support (MC 0326/4, August 2018):
‘The aim of operational medical support is to ensure that every casualty gets the right treatment in a timely manner and at an appropriate facility.’
ZEUS was developed primarily in R and uses Microsoft Excel to manage the user input and the R Shiny package to provide visualisations of the outputs (Figure 1). Early development of the model requirement identified a number of factors, which drove the design, such as:
• The need to represent, and react to, surges in demand on the medical laydown led to the model being designed to simulate random, distinct injury events. For example, the medical system may be able to treat 60 patients distributed equally over a 24-hour period, but how might it cope if many of those patients arrived simultaneously? • The need to represent future concepts of operation for the medical force (which may not yet exist) meant that the model needed the ability to represent these via data rather than as fixed structures. The deployed medical laydown will typically include facilities that expand and/or contract over time and those facilities may move at various points in the operation. • The requirement to recreate all of the decisions made within the model to enable the verification of its operation.
In order to mesh the requirement for the inclusion of random elements with the requirement for transparency,
© Crown copyright 2021, Dstl
FIGURE 1 ZEUS MODELLING ENVIRONMENT
the core simulation is deterministic but includes a pre-processor that generates multiple patient streams, each of which details the number of patients, the timing of their injury and their individual medical requirements. A replication (i.e. an individual model run) is hence the processing of one of these patient streams by the core simulation.
The model itself has two modes of operations:
• Unconstrained, where the model assumes that sufficient resources are available to meet the patient demand, and • Constrained, in which the model will block the onward movement or treatment of patients, for example due to the lack of beds or ambulances.
TAILORING ZEUS FOR COVID-19 In March 2020, Dstl was requested to support Defence Medical Services’ contribution to Op BROADSHARE with analysis to aid the understanding and assessment of future responses and potential medical provision for a number of overseas bases, with a focus on:
• The likely inflow of COVID-19 cases (categorised by severity of infection) presenting in each of those locations. • The numbers of Low and High Dependency beds potentially required in each location, where: ∘ Low Dependency represents a General Ward bed, and ∘ High Dependency represents a Critical Care bed, which may be equipped to provide mechanical ventilation.
ZEUS had previously been identified as a potential model to address these issues but it required some modification of its input data to adapt it to better represent COVID-19, in particular how the disease affected different age groups and the escalating treatment requirements for increasing levels of severity. Data collection focussed on extant sources, including open source, supplemented with Subject Matter Expert judgement, and included: • The ‘Population At Risk’ in each location (stratified into various age bands), which was provided by the analysis requestor. • Severity of infection was based upon papers from Imperial College London and the World Health Organisation. • Rates of infection (varying by week) were based upon the Scientific Advisory Group for Emergencies (SAGE)
Realistic Worst-Case scenario or
Defence Medical Services modelling, supported by Defence Statistics (Health) data on reported cases. • Medical treatment pathways, which detail the types of treatment required and the durations of those treatments, for different categories of patients, were developed in conjunction with Defence Clinical Advisors (who also have roles with the NHS).
The analysis was conducted at pace and the outputs were presented less than 72 hours after the initial request was received. This resulted in a number of actions, including the deployment of medical staff and other resources to the Falkland Islands to supplement the facilities in that location. Figure 2 shows an oxygen tank being loaded prior to delivery to the King Edward Memorial Hospital in the Falkland Islands.
This initial analysis was well received, and follow-on work was requested, both to investigate the initial analysis in more depth and to assess other locations.
OVERSEAS TERRITORIES (OT) The UK’s Overseas Territories are fourteen territories that do not form part of the United Kingdom itself, but which retain a constitutional and historical link with the United Kingdom. Most of the
FIGURE 2 OXYGEN TANK BEING LOADED PRIOR TO DELIVERY TO THE KING EDWARD MEMORIAL HOSPITAL IN THE FALKLAND ISLANDS
permanently inhabited territories are internally self-governing, with the UK retaining responsibility for defence and foreign relations.
Standing Joint Force Headquarters (SJFHQ), leading MOD’s response, worked closely with the Overseas Territories to ensure they received the help they needed to manage outbreaks of COVID-19. Having been informed of Dstl’s analysis in support of the overseas bases, in early April they requested analytical support to assess the healthcare capabilities of the Caribbean territories:
• Anguilla, which has a population of approximately 15,000 • Bermuda, approximate population 64,000 • British Virgin Islands, approximate population 30,000 • Cayman Islands, approximate population 66,000 • Montserrat, approximate population 5,000 • Turks & Caicos Islands, approximate population 37,000
INVESTIGATING SCALE OF CAPABILITY REQUIRED As for the analysis of the bases, the initial request was for an assessment of the likely scale of medical capacity required within the territories, assuming different potential rates of infection (based upon updated data and modelling from SAGE, DMS and Defence Statistics). At the time, a number of the territories were considering whether to lift their local lockdown restrictions and SJFHQ wished to understand the potential impacts of this upon the numbers of patients presenting. Whilst the majority of the cases may have been expected to develop mild (or no) symptoms, a sufficiently large number may have been expected to require hospitalisation. The knowledge generated (via the ZEUS modelling) of the potential demand upon the medical facilities was sufficiently compelling that SJFHQ advised that the extant lockdown measures be maintained, which they were.
INVESTIGATING RISK DUE TO LACK OF CAPABILITY For the second phase of the analysis, we received up-to-date information on the current medical capabilities and capacities within each of the territories. As ZEUS models individual patients, we were able to use its Constrained mode to calculate how many of them had their treatment delayed waiting for a specific resource to become available (for example, a Ventilated Critical Care bed) and what was the duration of each of those delays. Whilst we could not say with any certainty what the specific medical implications of those delays in treatment could have been (in this case, what was the increased likelihood of mortality?), we could infer that we should be aiming to minimise both metrics should any delays occur. For a number of the territories, whilst the majority of the patients requiring Ventilated Critical Care beds may have been expected to suffer no delays in receiving necessary treatment, a number may have faced significant (and potentially unsurvivable) delays. Using this information allowed SJFHQ to make judgments on the balance of medical risk across all of the territories and identify which had urgent requirements for supplementary resources and supplies.
IMPACT The various analysis tasks undertaken during this period highlighted the value of building flexibility into the development of our models. The original requirement for the ZEUS model was based upon it being used to support the development of
medical plans for deployed military forces where many of the casualties would come as the result of combat operations or their supporting activities. However, by keeping its functionality as generic as possible, we were rapidly able to adapt the input data to represent the spread of the virus through civilian populations and to model their treatment whilst the knowledge of that was also being developed.
The range of analysis conducted during this period had significant and, in some cases, near immediate impact. For the initial analysis of the overseas bases, the analysis was requested on a Monday afternoon, the outputs fed into the senior decision makers meeting on the following Thursday morning, the medical team was tasked on the Saturday and they arrived with the additional equipment required to increase the Critical Care capacity within the Falkland Islands by the next Tuesday.
The impact of the analysis for the Caribbean Overseas Territories was not quite so quick but still rapid – the delivery of the equipment and supplies required to significantly increase the medical capacity within the Turks & Caicos Islands took place within 2 weeks of the analysis reporting at the end of April 2020. In addition, RFA Argus (Figure 3) sailed to the Caribbean in preparation for the hurricane season and to support the response to the COVID-19 pandemic if required. She has a fully equipped 100-bed medical complex on board, which includes an emergency department and resuscitation facilities.
Major General R T H Jones, Standing Joint Force Commander noted that ‘. . . the modelling drove many of the key planning assumptions, providing clear targets and timelines for individual OT healthcare capacity and capability requirements (for example, ventilator numbers, staffing levels and PPE requirements). . . ’.
FIGURE 3 RFA ARGUS (A PRIMARY CASUALTY RECEIVING SHIP) IN THE CARIBBEAN SEA, SUMMER 2020
Pete Bailey is a Principal Analyst within Dstl’s Exploration Division. He was the technical lead for the analysis in support of Op BROADSHARE, having previously led the development of the ZEUS model.
Struan Millar is a Senior Engineer within Dstl’s Platform Systems Division. He was seconded to SJFHQ during Op BROADSHARE, as a deployable operational analyst, to provide real-time decision support and enable effective communications back to the wider science and technology community.
© Crown copyright (2021), Dstl. This material is licensed under the terms of the Open Government Licence except where otherwise stated. To view this licence, visit http://www.nationalarchives. gov.uk/doc/open-governmentlicence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: psi@nationalarchives.gov.uk
