Operational research: A multidisciplinary approach for the management of infectious disease in a global context

Conceptualisation and implementation of integrated disease surveillance globally: a scoping review

OBJECTIVES

The objective of this study was to examine the conceptualisation and operationalisation of Integrated Disease Surveillance (IDS) systems globally and the evidence for their effectiveness. Furthermore, to determine whether the recommendations made by Morgan et al. are supported by the evidence and what the evidence is to inform country development of IDS.

STUDY DESIGN

The study incorporated a scoping review.

METHODS

This review summarised evidence meeting the following inclusion criteria: Participants: any health sector; Concept: IDS; and Context: global. We searched Medline, Embase, and Epistemonikos for English publications between 1998 and 2022. Standard review methods were applied. A bespoke conceptual framework guided the narrative analysis. This scoping review is part of a research programme with three key elements, with the other studies being a survey of the International Association of National Public Health Institutes members on the current status of their disease surveillance systems and a deeper analysis and case studies of the surveillance systems in seven countries, to highlight the opportunities and challenges of integration.

RESULTS

Eight reviews and five primary studies, which were assessed as being of low quality, were included, mostly examining IDS in Africa, the human sector, and communicable diseases. None reported on the effects on disease control or on the evolution of IDS during the COVID-19 pandemic. Descriptions of IDS and of integration varied. Prerequisites of effective IDS systems mostly related to the adequacy of core functions and resourcing requirements. Laws or regulations supporting system integration and data sharing were not addressed. The provision of core functions and resourcing requirements were described as inadequate, financing as non-sustainable, and governance as poor. Enablers included active data sharing, close cooperation between agencies, clear reporting channels, integration of vertical programs, increased staff training, and adopting mobile reporting. Whilst the conceptual framework for IDS and Morgan et al.'s proposed principles were to some extent reflected in the highlighted priorities for IDS in the literature, the evidence base remains weak.

CONCLUSIONS

Available evidence is fragmented, incomplete, and of poor quality. The review found a lack of robust evaluation studies on the impact of IDS on disease control. Whilst a lack of evidence does not imply a lack of benefit or effect, it should signal the need to evaluate the process and impact of integration in the future development of surveillance systems. A common IDS definition and articulation of the parts that constitute an IDS system are needed. Further robust impact evaluations, as well as country reviews and evaluations of their IDS systems, are required to improve the evidence base.

Performance analysis of English hospitals during the first and second waves of the coronavirus pandemic

The coronavirus infection COVID-19 killed millions of people around the world in 2019-2022. Hospitals were in the forefront in the battle against the pandemic. This paper proposes a novel approach to assess the effectiveness of hospitals in saving lives. We empirically estimate the production function of COVID-19 deaths among hospital inpatients, applying Heckman's two-stage approach to correct for the bias caused by a large number of zero-valued observations. We subsequently assess performance of hospitals based on regression residuals, incorporating contextual variables to convex quantile regression. Data of 187 hospitals in England over a 35-week period from April to December 2020 is divided in two sub-periods to compare the structural differences between the first and second waves of the pandemic. The results indicate significant performance improvement during the first wave, however, learning by doing was offset by the new mutated virus straits during the second wave. While the elderly patients were at significantly higher risk during the first wave, their expected mortality rate did not significantly differ from that of the general population during the second wave. Our most important empirical finding concerns large and systematic performance differences between individual hospitals: larger units proved more effective in saving lives, and hospitals in London had a lower mortality rate than the national average.

On the impact of resource relocation in facing health emergencies

The outbreak of SARS-CoV-2 and the corresponding surge in patients with severe symptoms of COVID-19 put a strain on health systems, requiring specialized material and human resources, often exceeding the locally available ones. Motivated by a real emergency response system employed in Northern Italy, we propose a mathematical programming approach for rebalancing the health resources among a network of hospitals in a large geographical area. It is meant for tactical planning in facing foreseen peaks of patients requiring specialized treatment. Our model has a clean combinatorial structure. At the same time, it considers the handling of patients by a dedicated home healthcare service, and the efficient exploitation of resource sharing. We introduce mathematical programming heuristic based on decomposition methods and column generation to drive very large-scale neighborhood search. We evaluate its embedding in a multi-objective optimization framework. We experiment on real world data of the COVID-19 in Northern Italy during 2020, whose aggregation and post processing is made openly available to the community. Our approach proves to be effective in tackling realistic instances, thus making it a reliable basis for actual decision support tools.

Closing the gap in our understanding of infectious diseases

Systematic reviews (SR) and meta-analyses (MA) have become important in addressing specific questions of clinical importance and presenting evidence from an in-depth analysis of literature and aiding clinical decision-making. The "Systematic Reviews on infectious diseases" collection will address several important questions by summarizing large bodies of evidence in a reproducible and concise approach to advance our knowledge and understanding of infectious diseases.

Optimal timing of non-pharmaceutical interventions during an epidemic

In response to the recent outbreak of the SARS-CoV-2 virus governments have aimed to reduce the virus's spread through, inter alia, non-pharmaceutical intervention. We address the question when such measures should be implemented and, once implemented, when to remove them. These issues are viewed through a real-options lens and we develop an SIRD-like continuous-time Markov chain model to analyze a sequence of options: the option to intervene and introduce measures and, after intervention has started, the option to remove these. Measures can be imposed multiple times. We implement our model using estimates from empirical studies and, under fairly general assumptions, our main conclusions are that: (1) measures should be put in place not long after the first infections occur; (2) if the epidemic is discovered when there are many infected individuals already, then it is optimal never to introduce measures; (3) once the decision to introduce measures has been taken, these should stay in place until the number of susceptible or infected members of the population is close to zero; (4) it is never optimal to introduce a tier system to phase-in measures but it is optimal to use a tier system to phase-out measures; (5) a more infectious variant may reduce the duration of measures being in place; (6) the risk of infections being brought in by travelers should be curbed even when no other measures are in place. These results are robust to several variations of our base-case model.

Design of control strategies to help prevent the spread of COVID-19 pandemic

This paper proposes control strategies to allocate COVID-19 patients to screening facilities, health facilities, and quarantine facilities for minimizing the spread of the virus by these patients. To calculate the transmission rate, we propose a function that accounts for contact rate, duration of the contact, age structure of the population, susceptibility to infection, and the number of transmission events per contact. Moreover, the COVID-19 cases are divided into different groups according to the severity of their disease and are allocated to appropriate health facilities that provide care tailored to their needs. The multi-stage fuzzy stochastic programming approach is applied to cope with uncertainty, in which the probability associated with nodes of the scenario tree is treated as fuzzy variables. To handle the probabilistic model, we use a more flexible measure, M e measure, which allows decision-makers to adopt varying attitudes by assigning the optimistic-pessimistic parameter. This measure does not force decision-makers to hold extreme views and obtain the interval solution that provides further information in the fuzzy environment. We apply the proposed model to the case of Tehran, Iran. The results of this study indicate that assigning patients to appropriate medical centers improves the performance of the healthcare system. The result analysis highlights the impact of the demographic differences on virus transmission, and the older population has a greater influence on virus transmission than other age groups. Besides, the results indicate that behavioral changes in the population and their vaccination play a key role in curbing COVID-19 transmission.

Teaching Mathematical Modelling and Programming with GAMS in Dual Management Master Curricula Using Flipped Classrooms and Open Book Exams

In this article, we present a flipped classroom based teaching concept and related open book exam for Master’s courses in the field of Operational Research while paying particular attention to dual curricula management students. The characteristics of dual curricula include that students study part-time complemented by practical stages in cooperating firms. As a consequence, there is a limited number of lectures available compared to full-time programs. Hence, adequate course structures are needed to facilitate learning and encourage students to explore the research field further. We present an illustrative teaching concept focusing on the well-known Resource Constrained Project Scheduling Problem (RCPSP) and GAMS programming. Moreover, we demonstrate how self-study phases and classroom trainings are systematically combined to support the management students’ autonomous programming activities culminating in 24-h open book exams.

Central European journal of operations research (CJOR) "operations research applied to health services (ORAHS) in Europe: general trends and ORAHS 2020 conference in Vienna, Austria"

This articles provides a short summary of the research topics and latest research results of the European Working Group "Operations Research Applied to Health Services" (ORAHS) organized as an e-conference in Juli 2020 at the University of Vienna, Austria (https://orahs2020.univie.ac.at/). Furthermore, challenges for OR in health care including application areas, decision support systems, general trends, and modelling techniques are briefly illustrated from an European and international perspective by providing selected essential literature reviews.

Measuring vaccination willingness in response to COVID-19 using a multi-criteria-decision making method

The pandemic COVID-19 is continued to the massive burden of morbidity and mortality while disrupting economies and societies all over the world. At an earlier stage, wearing a face mask, social distancing, and hand hygiene were suggested to limit the transmission of this infection. The WHO, CDC, and other governing bodies were doing an effort to develop the coronavirus vaccine. Now COVID-19 vaccines are available to protect people against the coronavirus. People are hesitant about whether to receive a vaccination or do not to vaccinate. This study was aimed to analyze the COVID-19 vaccination willingness level of the general public of Pakistan to manage the COVID-19 disease. A multi-criteria decision-making method known as an analytical hierarchical method was applied to determine the COVID-19 vaccination willingness level of the public. The significant determinants of COVID-19 vaccination willingness were cues to action, perceived benefits, positive attitude, government recommendation, as well as perceived stress scoring high weights to the vaccination. Determinants of willingness to uptake the COVID-19 vaccine were individual decision, vaccine origin, adapting to change, and perceived barriers high obstacles to vaccinating. The determinants relating to the COVID-19 vaccine may help to increase the uptake of the vaccination program. The government may need communication campaigns to reinforce the benefits of the vaccine.

Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review

The COVID-19 pandemic has strongly affected the dynamics of Emergency Departments (EDs) worldwide and has accentuated the need for tackling different operational inefficiencies that decrease the quality of care provided to infected patients. The EDs continue to struggle against this outbreak by implementing strategies maximizing their performance within an uncertain healthcare environment. The efforts, however, have remained insufficient in view of the growing number of admissions and increased severity of the coronavirus disease. Therefore, the primary aim of this paper is to review the literature on process improvement interventions focused on increasing the ED response to the current COVID-19 outbreak to delineate future research lines based on the gaps detected in the practical scenario. Therefore, we applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to perform a review containing the research papers published between December 2019 and April 2021 using ISI Web of Science, Scopus, PubMed, IEEE, Google Scholar, and Science Direct databases. The articles were further classified taking into account the research domain, primary aim, journal, and publication year. A total of 65 papers disseminated in 51 journals were concluded to satisfy the inclusion criteria. Our review found that most applications have been directed towards predicting the health outcomes in COVID-19 patients through machine learning and data analytics techniques. In the overarching pandemic, healthcare decision makers are strongly recommended to integrate artificial intelligence techniques with approaches from the operations research (OR) and quality management domains to upgrade the ED performance under social-economic restrictions.

Fighting against COVID-19: what operations research can help and the sense-and-respond framework

COVID-19 is affecting all walks of life. To deal with it, we need to make use of scientifically sound tools and models. Operations research (OR), as a well-established field which focuses on deploying analytical tools to solving decision making problems, comes to the rescue. In this paper, by examining the OR literature and practices related to pandemics (including COVID-19), we discuss what OR can help to tackle challenges under COVID-19. We classify the literature into three stages, namely "before pandemic", "during pandemic" and "after pandemic". We examine the related literature and reveal the respective research areas and OR methods employed. Then, we propose a future research agenda. Finally, we establish the sense-and-respond OR framework regarding what specific actions should be taken to cope with COVID-19 from the perspectives of governments, healthcare and non-profit-making organizations, and businesses. We believe that the findings of this paper lay the solid foundation to stimulate further OR studies to combat COVID-19.

Supply chain design to tackle coronavirus pandemic crisis by tourism management

The rapid growth of the COVID-19 pandemic in the world and the importance of controlling it in all regions have made managing this crisis a great challenge for all countries. In addition to imposing various monetary costs on countries, this pandemic has left many serious damages and casualties. Proper control of this crisis will provide better medical services. Controlling travel and tourists in this crisis is also an effective factor. Hence, the proposed model wants to control the crisis by controlling the volume of incoming tourists to each city and region by closing the entry points of that region, which reduces the inpatients. The proposed multi-objective model is designed to aim at minimizing total costs, minimizing the tourist patients, and maximizing the number of city patients. The Improved Multi-choice Goal programming (IMCGP) method has been used to solve the multi-objective problem. The model examines the results by considering a case study. Sensitivity analyses and managerial insight are also provided. According to the results obtained from the model and case study, two medical centers with the capacity of 300 and 700 should be opened if the entry points are not closed.

Maintaining efficient logistics and supply chain management operations during and after coronavirus (COVID-19) pandemic: learning from the past experiences

The outbreak of the novel coronavirus (COVID-19) forced the governing bodies across the world to ban all kinds of travel involving the movement of people. However, the policymakers have been working hard to mobilize the movement of essential goods and services considering its importance in containing the pandemic. It signifies how important the establishment and maintenance of logistics and supply chain management (LSCM) operations are, both during the containment and the successive periods. Motivated with the paramount importance of LSCM operations during the rapid spread of the novel coronavirus (COVID-19) across the globe, this paper critically reviews the existing literature closely related to it. The main aim is to identify and enhance the understanding of the logistical characteristics that play a vital role during pandemics. The selection of the literature was done using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology. The classification of the selected literature was done using a tripartite framework. Results show that researchers have focused mostly on "Post-event" (48.24%) management of logistical operations followed by the "Pre-event" (31.76%) and least in the "Integrated" (20%.) approaches. Furthermore, the analysis of the results provided useful insights that are discussed in detail. Also, twelve key areas have been identified that need due attention to improve the overall efficiency of the LSCM operations. We believe that the findings from this paper would be useful to the decision-makers and other stakeholders, as far as, maintaining efficient LSCM operations during as well after the pandemics are concerned.