Incentivizing at‐risk production capacity building for COVID‐19 vaccines

Our study analyzes capacity management for promising vaccine candidates before regulatory approval (i.e., at‐risk capacity building) in the presence of production outsourcing and different operational challenges: misaligned interests, possible ex post negotiations, asymmetric information between developers and manufacturers, and government involvement. We develop analytical models to compare two vaccine production modes: (1) the integrated mode (a single company determines the at‐risk capacity and produces in‐house) and (2) the outsourcing mode (a manufacturer determines the at‐risk capacity and a developer determines a funding level to share the capacity‐building cost). Our study reveals that outsourcing can achieve a higher at‐risk capacity only if it can achieve sufficient cost savings compared to the integrated mode. Our research also proves that both vaccine production modes tend to underinvest in the at‐risk capacity. Following this, we suggest measures to improve the at‐risk capacity building in both vaccine production modes. Our signaling game model reveals that a developer with high competence cannot always send credible signals of its true competence level to the manufacturer. Our incomplete contract model verifies that the relative performance of the two vaccine production modes is robust when ex post negotiation occurs under the outsourcing mode;however, the two parties may show incompatible preferences for the ex post negotiation. Our study also analyzes the optimal allocation of government financial support to development funding and capacity funding to incentivize at‐risk capacity building. We present comprehensive guidelines for the different stakeholders to collectively contribute to ramping up the at‐risk capacity of promising vaccines.

An Analysis Air Traffic Prediction during a Pandemic

Air transportation during the covid-19 pandemic experienced a very drastic decline. The decrease in the number of passengers was caused by national and international restrictions. The troublesome administration makes passengers discouraged from traveling using Air transportation. Based on the National Statistics Agency, air transportation experienced a decline from early 2020 to 2021. This study focuses on air traffic predictions, namely the number of aircraft passengers during the COVID-19 pandemic at Indonesia's main airports, namely Kuala Namu, Sukarno Hatta, and Juanda airports., Ngurah Rai and Hasanuddin. The method used to predict the number of airplane passengers during a pandemic is the backpropagation algorithm using the Fletcher Reeves method. © 2022 IEEE.

A flatter curve affords hospitals greater time to prepare for a pandemic surge

This paper quantifies the benefits of flattening the curve (with a constant total patient load over the study period) on the risk of a hospital bed shortage in a pandemic. Using discrete-event simulation of patient care paths in hospitals, synthetic data that eliminates issues of confounding affects from the simultaneous occurrence of regional response actions and/or changes in resources, treatments or other situational circumstances, is produced for estimating hospital capacity for pandemic response. Results from systematically designed numerical experiments produced several findings. These include that the higher the acceleration in pandemic patient demand growth, the greater the impact of the intervention. Cutting this acceleration by 75% from the greatest studied rate created over four additional weeks to prepare for an 80% risk of running out of intensive care beds. Additionally, the greater the acceleration in growth, the fewer the days with a high risk of running out of beds, but the greater the total number of critical patients that could not be served with existing resources. Finally, the lower this acceleration, the fewer resources or modifications needed to cope with the surge, but the longer they are needed. The findings further show how hospitals can benefit from analytical tools that exploit digital health information to predict and plan for need levels and time to onset of these levels. These tools can be embedded within a real-time framework in which automated and early warnings can inform the selection of strategies for managing or coping with expected increases in demand for emergency hospital services. © 2022 The Author(s)

On Airport Time Slot Auctions: A Market Design Complying with the IATA Scheduling Guidelines

The growth in air traffic (before the Covid-19 pandemic) made airport time slots an increasingly scarce resource (and it is believed that this growth will continue after recovery). It is widely acknowledged that the grandfathering schemes used nowadays lead to inefficient allocations and that auctions would be a means to allocate valuable airport time slots efficiently. It has, however, also been pointed out that the design of such slot auctions is challenging due to the various constraints that need to be considered. The present paper proposes a market design for the sales of airport time slots at EU airports that complies with the Worldwide Scheduling Guidelines of the International Air Transport Association (IATA), most notably the reference value systems at level 3 airports. These guidelines need to be considered but lead to significant additional complexity in the market design. Capacity constraints are defined for overlapping time windows, which render the maximum welfare flight scheduling problem NP-hard. Auction formats with good incentive properties such as the Vickrey-Clarke-Groves mechanism or core-selecting auctions require an exact solution to the allocation problem. Given its hardness, it is far from obvious that the allocation problem can be solved to optimality sufficiently fast for practically relevant sizes of real-world problems. We introduce a mathematical model formulation for the maximum welfare flight scheduling problem that complies with all specified IATA constraints and evaluate it on near real-world data sets of flight requests for a full season of a major international airport. We show that the allocation can be computed within minutes and that all the payment computations for the winners can be done in less than two hours on average for realistic problem sizes. The consideration of values of airlines within the proposed auction mechanism leads to significant welfare gains of more than 35% as compared with benchmarks resulting from different standard objectives. These include the maximization of the number of movements, the minimization of the number of movements for which deviations from requested times occur, and the minimization of the total deviation of scheduled from requested times. Whereas the results indicate that auctions can be solved quickly for realistic problem sizes and promise significant welfare gains under the standard independent private values assumptions, the implementation of auctions in the field leads to additional serious challenges. For example, the regulator might have to impose allocation constraints to mitigate the market power of incumbent airlines. In addition, the valuation of slots and the interdependencies of the slot assignment with those at other coordinated airports need careful attention. Copyright: © 2022 INFORMS.

Coping with COVID-19: the role of hospital care structures and capacity expansion in five countries.

This contribution examines the responses of five health systems in the first wave of the COVID-19 pandemic: Denmark, Germany, Israel, Spain and Sweden. The aim is to understand to what extent this crisis response of these countries was resilient. The study focuses on hospital care structures, considering both existing capacity before the pandemic and the management and expansion of capacity during the crisis. Evaluation criteria include flexibility in the use of existing resources and response planning, as well as the ability to create surge capacity. Data were collected from country experts using a structured questionnaire. Main findings are that not only the total number but also the availability of hospital beds is critical to resilience, as is the ability to mobilise (highly) qualified personnel. Indispensable for rapid capacity adjustment is the availability of data. Countries with more centralised hospital care structures, more sophisticated concepts for providing specialised services and stronger integration of the inpatient and outpatient sectors have clear structural advantages. A solid digital infrastructure is also conducive. Finally, a centralised governance structure is crucial for flexibility and adaptability. In decentralised systems, robust mechanisms to coordinate across levels are important to strengthen health care system resilience in pandemic situations and beyond.

Managing Common and Catastrophic Risks in the Airline Industry

This chapter discusses risk management with a focus on the airline industry. The world has become acutely aware of major supply chain disruptions due to the COVID pandemic. Consumers, airline passengers, and companies are scrambling to understand and respond to these events. In that light, we begin the chapter with a brief overview of risk management, highlighting both common and catastrophic risks faced by companies and their supply chains. We then discuss approaches that companies employ to mitigate them. Our primary goal is to explore the risks that airlines face and the approaches they take to manage them, including fuel hedging, capacity management, and ticket pricing. Based on company interviews and our firsthand experience, we note that the airlines typically make these decisions in silos. Therefore, we introduce an analytical model that explicitly integrates them. We derive analytical results and propose directions for future research. We conclude with summary comments about managing risks once the world moves past COVID. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

On Airport Time Slot Auctions: A Market Design Complying with the IATA Scheduling Guidelines

The growth in air traffic (before the Covid-19 pandemic) made airport time slots an increasingly scarce resource (and it is believed that this growth will continue after recovery). It is widely acknowledged that the grandfathering schemes used nowadays lead to inefficient allocations and that auctions would be a means to allocate valuable airport time slots efficiently. It has, however, also been pointed out that the design of such slot auctions is challenging due to the various constraints that need to be considered. The present paper proposes a market design for the sales of airport time slots at EU airports that complies with the Worldwide Scheduling Guidelines of the International Air Transport Association (IATA), most notably the reference value systems at level 3 airports. These guidelines need to be considered but lead to significant additional complexity in the market design. Capacity constraints are defined for overlapping time windows, which render the maximum welfare flight scheduling problem NP-hard. Auction formats with good incentive properties such as the Vickrey-Clarke-Groves mechanism or core-selecting auctions require an exact solution to the allocation problem. Given its hardness, it is far from obvious that the allocation problem can be solved to optimality sufficiently fast for practically relevant sizes of real-world problems. We introduce a mathematical model formulation for the maximum welfare flight scheduling problem that complies with all specified IATA constraints and evaluate it on near real-world data sets of flight requests for a full season of a major international airport. We show that the allocation can be computed within minutes and that all the payment computations for the winners can be done in less than two hours on average for realistic problem sizes. The consideration of values of airlines within the proposed auction mechanism leads to significant welfare gains of more than 35% as compared with benchmarks resulting from different standard objectives. These include the maximization of the number of movements, the minimization of the number of movements for which deviations from requested times occur, and the minimization of the total deviation of scheduled from requested times. Whereas the results indicate that auctions can be solved quickly for realistic problem sizes and promise significant welfare gains under the standard independent private values assumptions, the implementation of auctions in the field leads to additional serious challenges. For example, the regulator might have to impose allocation constraints to mitigate the market power of incumbent airlines. In addition, the valuation of slots and the interdependencies of the slot assignment with those at other coordinated airports need careful attention.

The other side of the crisis: organizational flexibility in balancing Covid-19 and non-Covid-19 health-care services.

BACKGROUND: Many healthcare systems have been unable to deal with Covid-19 without influencing non-Covid-19 patients with pre-existing conditions, risking a paralysis in the medium term. This study explores the effects of organizational flexibility on hospital efficiency in terms of the capacity to deliver healthcare services for both Covid-19 and non-Covid-19 patients. METHOD: Focusing on Italian health system, a two-step strategy is adopted. First, Data Envelope Analysis is used to assess the capacity of hospitals to address the needs of Covid-19 and non-Covid-19 patients relying on internal resource flexibility. Second, two panel regressions are performed to assess external organizational flexibility, with the involvement in demand management of external operators in the health-care service, examining the impact on efficiency in hospital capacity management. RESULTS: The overall response of the hospitals in the study was not fully effective in balancing the needs of the two categories of patients (the efficiency score is 0.87 and 0.58, respectively, for Covid-19 and non-Covid-19 patients), though responses improved over time. Furthermore, among the measures providing complementary services in the community, home hospitalization and territorial medicine were found to be positively associated with hospital efficiency (0.1290, p < 0.05 and 0.2985, p < 0.01, respectively, for non-Covid-19 and Covid-19 patients; 0.0026, p < 0.05 and 0.0069, p < 0.01, respectively, for non-Covid-19 and Covid-19). In contrast, hospital networks are negatively related to efficiency in Covid-19 patients (-0.1037, p < 0.05), while the relationship is not significant in non-Covid-19 patients. CONCLUSIONS: Managing the needs of Covid-19 patients while also caring for other patients requires a response from the entire healthcare system. Our findings could have two important implications for effectively managing health-care demand during and after the Covid-19 pandemic. First, as a result of a naturally progressive learning process, the resource balance between Covid-19 and non-Covid-19 patients improves over time. Second, it appears that demand management to control the flow of patients necessitates targeted interventions that combine agile structures with decentralization. Finally, untested integration models risk slowing down the response, giving rise to significant costs without producing effective results.

An Integer Linear Programming for Hospital Bed Scheduling and Capacity Management

COVID-19 has struck the Philippines in December 2019 and has brought great panic to the country's healthcare system. In a short period of time, the number of infected increased exponentially. Hospitals are suddenly filled with patients infected by the virus to the extent that patients wait for hours to days to be admitted. Others die on the road even before finding hospitals that can accommodate them. The hospitals and the country's healthcare system must consider this increasing demand to serve patients fully. Patient planning is commonly used in other countries to maximize bed allocation. A recent study using Bernoulli Distributed Random Variable represents the binary integer program. The approach combines the queuing model and simulation to reduce the patient dismissal rate and increase hospital output. On the other hand, this paper deals with strategic hospital bed capacity optimization using linear integer programming by considering the diverse resources, such as doctors, nurses, beds, and hospital rooms. © 2022 ACM.

A flatter curve affords hospitals greater time to prepare for a pandemic surge

This paper quantifies the benefits of flattening the curve (with a constant total patient load over the study period) on the risk of a hospital bed shortage in a pandemic. Using discrete-event simulation of patient care paths in hospitals, synthetic data that eliminates issues of confounding affects from the simultaneous occurrence of regional response actions and/or changes in resources, treatments or other situational circumstances, is produced for estimating hospital capacity for pandemic response. Results from systematically designed numerical experiments produced several findings. These include that the higher the acceleration in pandemic patient demand growth, the greater the impact of the intervention. Cutting this acceleration by 75% from the greatest studied rate created over four additional weeks to prepare for an 80% risk of running out of intensive care beds. Additionally, the greater the acceleration in growth, the fewer the days with a high risk of running out of beds, but the greater the total number of critical patients that could not be served with existing resources. Finally, the lower this acceleration, the fewer resources or modifications needed to cope with the surge, but the longer they are needed. The findings further show how hospitals can benefit from analytical tools that exploit digital health information to predict and plan for need levels and time to onset of these levels. These tools can be embedded within a real-time framework in which automated and early warnings can inform the selection of strategies for managing or coping with expected increases in demand for emergency hospital services.

Design and Simulation of a Digital Twin Mobility Concept: An Electric Aviation System Dynamics Case Study with Capacity Constraints

Featured ApplicationManagement model for air mobility and Service-oriented On-Demand Air Mobility. Modeling method for aircraft units between different vertiports within a given region considering mobility needs, capacity constraints, maintenance and charging needs. Exemplary application in a simulation model for a regional area of fifteen vertiports and their interconnection by means of electric aircraft units.Vertical mobility, as a commercial service, has been considered for scheduled volume and long-distance mobility services. To overcome its limits and increase its potential coverage, flexibility, and adaptability, centralized mobility hubs, similar to airports, will need to be constructed. Within this context, a customized and on-demand air mobility concept providing high flexibility in location combinations and time schedules could provide a solution for regional mobility needs. The aim of this research was to provide a generic framework for various mobility schemes as well as to design a holistic air mobility management concept for electric vertical mobility. A system dynamics simulation case study applied the conceptual model for an on-demand air mobility network of electric aircraft in a regional area with capacity constraints including vertiports, aircraft, charging, and parking stations. Therefore, bottlenecks and delays were quantified using a digital twin tool for customized scenarios. Simulation results showed that optimized maintenance management and the redistribution of aircraft units improved service indicators such as the number of customers served, and customer wait times as well as a reduction in the amount of time an aircraft spent on the ground. As a result, a digital twin air mobility network model with simulation capabilities may be a key factor for future implementation.

Models for Assessing Strategies for Improving Hospital Capacity for Handling Patients during a Pandemic.

OBJECTIVE: The aim of this study was to investigate the performance of key hospital units associated with emergency care of both routine emergency and pandemic (COVID-19) patients under capacity enhancing strategies. METHODS: This investigation was conducted using whole-hospital, resource-constrained, patient-based, stochastic, discrete-event, simulation models of a generic 200-bed urban U.S. tertiary hospital serving routine emergency and COVID-19 patients. Systematically designed numerical experiments were conducted to provide generalizable insights into how hospital functionality may be affected by the care of COVID-19 pandemic patients along specially designated care paths, under changing pandemic situations, from getting ready to turning all of its resources to pandemic care. RESULTS: Several insights are presented. For example, each day of reduction in average ICU length of stay increases intensive care unit patient throughput by up to 24% for high COVID-19 daily patient arrival levels. The potential of 5 specific interventions and 2 critical shifts in care strategies to significantly increase hospital capacity is also described. CONCLUSIONS: These estimates enable hospitals to repurpose space, modify operations, implement crisis standards of care, collaborate with other health care facilities, or request external support, thereby increasing the likelihood that arriving patients will find an open staffed bed when 1 is needed.

Incentivizing at-risk production capacity building for COVID-19 vaccines

Our study analyzes capacity management for promising vaccine candidates before regulatory approval (i.e., at-risk capacity building) in the presence of production outsourcing and different operational challenges: misaligned interests, possible ex-post negotiations, asymmetric information between developers and manufacturers, and government involvement. We develop analytical models to compare two vaccine production modes: (1) the integrated mode (a single company determines the at-risk capacity and produces in-house);and (2) the outsourcing mode (a manufacturer determines the at-risk capacity and a developer determines a funding level to share the capacity building cost). Our study reveals that outsourcing can achieve a higher at-risk capacity only if it can achieve sufficient cost savings compared with the integrated mode. Our research also proves that both vaccine production modes tend to underinvest in the at-risk capacity. Following this, we suggest measures to improve the at-risk capacity building in both vaccine production modes. Our signaling game model reveals that a developer with high competence cannot always send credible signals of its true competence level to the manufacturer. Our incomplete contract model verifies that the relative performance of the two vaccine production modes is robust when ex-post negotiation occurs under the outsourcing mode;however, the two parties may show incompatible preferences for the ex-post negotiation. Our study also analyzes the optimal allocation of government financial support to development funding and capacity funding to incentivize at-risk capacity building. We present comprehensive guidelines for the different stakeholders to collectively contribute to ramping up the at-risk capacity of promising vaccines. This article is protected by copyright. All rights reserved

One year with the COVID-19 pandemic - Lessons learnt? Intersectoral collaboration measures established during the crisis could benefit capacity and patient flow management in daily clinical practice.

PURPOSE: The COVID-19 pandemic has changed the way hospitals work. Strategies that were detached from the boundaries of departments and responsibilities in the COVID-19 pandemic have proven themselves under extreme conditions and show a beneficial influence on patient flow and resource management as well as on the communication culture. The continuation of closer interdisciplinary and cross-sectoral co-operation in a "new clinical routine" could have a positive impact on personnel concepts, communication strategies, and the management of acute care capacities and patient pathways. DESIGN/METHODOLOGY/APPROACH: The aim of the paper is to critically discuss the knowledge gained in the context of the COVID-19 pandemic from the various approaches in patient flow and capacity management as well as interdisciplinary co-operation. More recent research has evaluated patient pathway management, personnel planning and communication measures with regard to their effect and practicability for continuation in everyday clinical practice. FINDINGS: Patient flows and acute care capacities can be more efficiently managed by continuing a culture change towards closer interdisciplinary and intersectoral co-operation and technologies that support this with telemedicine functionalities and regional healthcare data interoperability. Together with a bi-directional, more frequent and open communication and feedback culture, it could form a "new clinical routine". ORIGINALITY/VALUE: This paper discusses a holistic approach on the way away from silo thinking towards cross-departmental collaboration.

Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation.

COVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke's hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients.

COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care.

Managing healthcare demand and capacity is especially difficult in the context of the COVID-19 pandemic, where limited intensive care resources can be overwhelmed by a large number of cases requiring admission in a short space of time. If patients are unable to access this specialist resource, then death is a likely outcome. In appreciating these 'capacity-dependent' deaths, this paper reports on the clinically-led development of a stochastic discrete event simulation model designed to capture the key dynamics of the intensive care admissions process for COVID-19 patients. With application to a large public hospital in England during an early stage of the pandemic, the purpose of this study was to estimate the extent to which such capacity-dependent deaths can be mitigated through demand-side initiatives involving non-pharmaceutical interventions and supply-side measures to increase surge capacity. Based on information available at the time, results suggest that total capacity-dependent deaths can be reduced by 75% through a combination of increasing capacity from 45 to 100 beds, reducing length of stay by 25%, and flattening the peak demand to 26 admissions per day. Accounting for the additional 'capacity-independent' deaths, which occur even when appropriate care is available within the intensive care setting, yields an aggregate reduction in total deaths of 30%. The modelling tool, which is freely available and open source, has since been used to support COVID-19 response planning at a number of healthcare systems within the UK National Health Service.