Quantifying optimal resource allocation strategies for controlling epidemics.

Frequent emergence of communicable diseases is a major concern worldwide. Lack of sufficient resources to mitigate the disease burden makes the situation even more challenging for lower-income countries. Hence, strategy development for disease eradication and optimal management of the social and economic burden has garnered a lot of attention in recent years. In this context, we quantify the optimal fraction of resources that can be allocated to two major intervention measures, namely reduction of disease transmission and improvement of healthcare infrastructure. Our results demonstrate that the effectiveness of each of the interventions has a significant impact on the optimal resource allocation in both long-term disease dynamics and outbreak scenarios. The optimal allocation strategy for long-term dynamics exhibits non-monotonic behaviour with respect to the effectiveness of interventions, which differs from the more intuitive strategy recommended in the case of outbreaks. Further, our results indicate that the relationship between investment in interventions and the corresponding increase in patient recovery rate or decrease in disease transmission rate plays a decisive role in determining optimal strategies. Intervention programmes with decreasing returns promote the necessity for resource sharing. Our study provides fundamental insights into determining the best response strategy when controlling epidemics in resource-constrained situations.

COVID-19: Agent-Based Simulation-Optimization to Vaccine Center Location Vaccine Allocation Problem

This paper presents an agent-based simulation-optimization modeling and algorithmic framework to determine the optimal vaccine center location and vaccine allocation strategies under budget constraints during an epidemic outbreak. Both simulation and optimization models incorporate population health dynamics, such as susceptible (S), vaccinated (V), infected (I) and recovered (R), while their integrated utilization focuses on the COVID-19 vaccine allocation challenges. We first formulate a dynamic location-allocation mixed-integer programming (MIP) model, which determines the optimal vaccination center locations and vaccines allocated to vaccination centers, pharmacies, and health centers in a multi-period setting in each region over a geographical location. We then extend the agent-based epidemiological simulation model of COVID-19 (Covasim) by adding new vaccination compartments representing people who take the first vaccine shot and the first two shots. The Covasim involves complex disease transmission contact networks, including households, schools, and workplaces, and demographics, such as age-based disease transmission parameters. We combine the extended Covasim with the vaccination center location-allocation MIP model into one single simulation-optimization framework, which works iteratively forward and backward in time to determine the optimal vaccine allocation under varying disease dynamics. The agent-based simulation captures the inherent uncertainty in disease progression and forecasts the refined number of susceptible individuals and infections for the current time period to be used as an input into the optimization. We calibrate, validate, and test our simulation-optimization vaccine allocation model using the COVID-19 data and vaccine distribution case study in New Jersey. The resulting insights support ongoing mass vaccination efforts to mitigate the impact of the pandemic on public health, while the simulation-optimization algorithmic framework could be generalized for other epidemics. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Analyzing the research trends of COVID-19 using topic modeling approach

PurposeThe COVID-19 pandemic has impacted 222 countries across the globe, with millions of people losing their lives. The threat from the virus may be assessed from the fact that most countries across the world have been forced to order partial or complete shutdown of their economies for a period of time to contain the spread of the virus. The fallout of this action manifested in loss of livelihood, migration of the labor force and severe impact on mental health due to the long duration of confinement to homes or residences.Design/methodology/approachThe current study identifies the focus areas of the research conducted on the COVID-19 pandemic. s of papers on the subject were collated from the SCOPUS database for the period December 2019 to June 2020. The collected sample data (after preprocessing) was analyzed using Topic Modeling with Latent Dirichlet Allocation.FindingsBased on the research papers published within the mentioned timeframe, the study identifies the 10 most prominent topics that formed the area of interest for the COVID-19 pandemic research.Originality/valueWhile similar studies exist, no other work has used topic modeling to comprehensively analyze the COVID-19 literature by considering diverse fields and domains.

Tackling the COVID elective surgical backlog: Prioritising need, benefit or equality?

The National Health Service (NHS) in the UK is currently facing a significant waiting list backlog following the disruption of the COVID-19 pandemic, with millions of patients waiting for elective surgical procedures. Effective treatment prioritisation has been identified as a key element of addressing this backlog, with NHS England's delivery plan highlighting the importance of ensuring that those with ‘the clinically most urgent conditions are diagnosed and treated most rapidly'. Indeed, we describe how the current clinical guidance on prioritisation issued by The Federation of Surgical Specialty Associations serves this aim. However, whilst there are strong reasons to prioritise elective surgery in accordance with clinical need, we argue that it would be a mistake to assume that prioritisation in accordance with clinical need requires only a clinical or scientific judgement. The understanding of clinical need that we choose to employ in a prioritisation system will be grounded by some key ethical judgements. Moreover, we may also have to make trade-offs between addressing clinical need, safeguarding equality, and achieving other benefits. As the UK faces up to the backlog, it is important that surgical prioritisation guidelines enshrine a broad range of values that we believe ought to determine access to care in non-emergency circumstances. Our analysis suggests that the current approach to prioritisation is not a sufficiently nuanced way of balancing the different moral values that are operative in this context. © The Author(s) 2023.

A two-stages mathematical model for a Covid-19 vaccination fuzzy scheme and resource allocation in hub locations considering transmission risk and environment factor

Tackling with Covid-19 dilemma of vaccine distribution needed a stack of analysis and examination. This paper develops a generalizable framework for designing a hub vaccination dispensing network to achieve expand the Covid-19 vaccination coverage for public. Designing a hub location routing network for vaccine distribution is the main concern for this research. The proposed model hinges on maximum coverage and patients' safety by considering high-priority population alongside the cost reduction in an uncertain environment. The hub location model enhances the accessibility of the vaccines to various communities and helps to overcome the challenges. The results of this model were examined through both numerical and case studies in the north of Tehran to demonstrate its application. Furthermore, in order to reduce the costs of vaccine imports, vaccine entry routing can be developed from border and air points to the country in order to be able to perform vaccination in the fastest time and lowest cost in the future. The results concede that increasing the number of outreach dispensing locations per hub dispensing location will not necessarily result in increased coverage.

An Analytical Approach for Dispatch Operations of Emergency Medical Services: A Case Study of COVID-19

: Emergency medical services (EMS) aims to deliver timely ambulatory care to incidents in communities. However, the operations of EMS may contend with suddenly increasing demands resulting from unexpected disasters such as disease outbreaks (e.g., COVID-19) or hurricanes. To this end, it usually requires better strategical decisions to dispatch, allocate, and reallocate EMS resources to meet the demand changes over time in terms of demographic and geographic distribution of incidents. In this study, we focus on the operation of the EMS resources (i.e., ambulance dispatch) in response to a demand disruption amid the COVID-19 pandemic. Specifically, we present a analytical framework to (1) analyze the underlying demographic and geographic patterns of emergency incidents and EMS resources;(2) develop a mathematical programming model to identify potential demand gaps of EMS coverage across different districts;and (3) provide a remedial reallocation solution to the EMS system with the existing ambulance capacity. The proposed method is validated with emergency response incident data in New York City for the first COVID-19 surge from March to April 2020. We found that it takes a long incident response time to scenes which reflects unexpected incident demands during COVID-19 surge. To cover such disruptive demands, ambulances need to be reallocated between service districts while meeting the response time standard. The proposed framework can be potentially applied to similar disruptive scenarios in the future and other operational systems disrupted by other disasters. Highlights: We propose an analytical framework using optimization modeling and simulation techniques for EMS resource allocation in response to a demand disruption amid the COVID-19 pandemic.We propose mathematical programming models to identify potential demand gaps of EMS coverage across different districts.We provide a remedial reallocation solution to the EMS system with the existing ambulance capacity. © 2023, The Author(s).

A fuzzy sustainable model for COVID-19 medical waste supply chain network

The COVID-19 has placed pandemic modeling at the forefront of the whole world's public policymaking. Nonetheless, forecasting and modeling the COVID-19 medical waste with a detoxification center of the COVID-19 medical wastes remains a challenge. This work presents a Fuzzy Inference System to forecast the COVID-19 medical wastes. Then, people are divided into five categories are divided according to the symptoms of the disease into healthy people, suspicious, suspected of mild COVID-19, and suspicious of intense COVID-19. In this regard, a new fuzzy sustainable model for COVID-19 medical waste supply chain network for location and allocation decisions considering waste management is developed for the first time. The main purpose of this paper is to minimize supply chain costs, the environmental impact of medical waste, and to establish detoxification centers and control the social responsibility centers in the COVID-19 outbreak. To show the performance of the suggested model, sensitivity analysis is performed on important parameters. A real case study in Iran/Tehran is suggested to validate the proposed model. Classifying people into different groups, considering sustainability in COVID 19 medical waste supply chain network and examining new artificial intelligence methods based on TS and GOA algorithms are among the contributions of this paper. Results show that the decision-makers should use an FIS to forecast COVID-19 medical waste and employ a detoxification center of the COVID-19 medical wastes to reduce outbreaks of this pandemic. © 2023, Crown.

Working from home challenges: Resources, tools, and quality concerns

This chapter provides a glimpse into the conversation around the resources that university staff need to thrive in their work both on campus or through telework. The COVID-19 pandemic and shifting to working from home exposed disparities in resources for staff at the University of Utah many of which existed in the on-campus work environment as well. Institutions of higher education were no exception;most non-essential employees made the change from working on campus to a teleworking environment. Because most colleges and universities still operate from a brick-and-mortar setting and primarily offer in-person instruction, this change to serving students and carrying out job responsibilities from home was a huge and unexpected shift, and very little infrastructure was in place for addressing needs and providing essential tools and resources for employee thriving in a work-at-home environment. It is found that the move to working from home revealed a broad continuum where on one end staff had access to essential resources for thriving as new telecommuters, and on the other end staff struggled from one day to the next to maintain quality services for students and co-workers due to the lack of basic resources. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Improving Healthcare Deserts in Sub-Saharan Africa: A Health Improvement Pilot Study Leveraging a Global, Cloud-Based Telemedicine Platform

Objectives: The objective of this project was to improve healthcare deserts in Sub-Saharan Africa through sustainable knowledge transfer and capacity-building leveraging an advanced cloud-based telemedicine platform. Method(s): In 2022, WTI and its network of partners delivered 2 telehealth devices as part of the effort to create a sustainable platform to address a known health desert in a previously abandoned clinic in the village of Opoji, in the state of Edo, Nigeria. Providers were trained in two cohorts. Global Experts for this project were organized with Providence Health and their Global and Domestic Engagement (GDE) department and trained in telementoring and teleconsulting. Local Specialists were first trained on the platform and then telementored by Global Experts. To better understand the health value outcomes of these interventions, observational research was employed to measure the improvement of patient-to-provider ratios. These ratios were baselined for average patient loads. Result(s): As a result of the pilot, provider-to-patient ratios were improved. Prior to the WTI program, interventions were only available 5% of the time (9 hrs/wk vs 168 hrs/wk), with very basic expertise. After the Opoji Comprehensive Medical Center was reopened and the supporting physicians were scheduled, patients could be seen with a high level of global medical expertise 100% of the time (24 hours per day). Conclusion(s): Telemedicine technology can improve capacity-building in Sub-Saharan Africa with relatively minimal resource allocation in a replicable and scalable manner. Data collection for the pilot did have limitations. The opportunity to collect patient-reported outcomes, including patient satisfaction with telemedicine visits, exists but COVID and other barriers prevented researchers from fully implementing. By mentoring the local specialty hospital staff to deliver care by cloud-based devices, the program has developed an "Africans helping Africans" approach to achieve sustainable capacity building which can be built upon and further researched.Copyright © 2023

Predicting Intensive Care Unit Admission Using Machine and Deep Learning: COVID-19 Case Study

The COVID-19 pandemic has affected the entire world by causing widespread panic and disrupting normal life. Since the outbreak began in December 2019, the virus has killed thousands of people and infected millions more. Hospitals are struggling to keep up with large patient flows. In some situations, hospitals are lacking enough beds and ventilators to accommodate all of their patients or are running low on supplies such as masks and gloves. Predicting intensive care unit (ICU) admission of patients with COVID-19 could help clinicians better allocate scarce ICU resources. In this study, many machine and deep learning algorithms are tested over predicting ICU admission of patients with COVID-19. Most of the algorithms we studied are extremely accurate toward this goal. With the convolutional neural network (CNN), we reach the highest results on our metrics (90.09% accuracy and 93.08% ROC-AUC), which demonstrates the usability of these learning models to identify patients who are likely to require ICU admission and assist hospitals in optimizing their resource management and allocation during the COVID-19 pandemic or others. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Digitalization of the supply chain: transformation factors

PurposeThere is an increasing interest in the supply chain's digitalization, yet the topic is still in the preliminary stages of academic research. The academic literature has no consensus and is still limited to research assessing the supply chain's digitalization of organizations. This study aims to explore the supply chain digitalization drivers to understand the emerging phenomena. More specifically, the authors devised from the literature the most common factors in assessing the readiness in scaling supply chain digitalization.Design/methodology/approachThis study followed a five-phased systematic literature review (SLR) methodology in this research: designing, analyzing, conducting, writing and assessing the quality of the review. The SLR is beneficial for justifying future research regardless of the complex process that requires dealing with high-level databases, information filtering and relevancies of the content. Through analysis of 347 titles and s and 40 full papers, the authors showed and discussed the supply chain digitalization: transformation factors.FindingsThe results generated three main themes: technology, people and processes. The study also generated ten subthemes/primary drivers for assessing the readiness for supply chain digitalization in organizations: IT infrastructure, cybersecurity systems, digitalization reskilling and upskilling, digitalization culture, top management support, digitalization and innovation strategy, integrated supply chain, digital innovation management, big data management and data analytics and government regulations. The importance of each factor was discussed, and future research agenda was presented.Research limitations/implicationsWhile the key drivers of the supply chain digitalization were identified, there is still a need to study the statistical correlation to confirm the interrelationships among factors. This study is also limited by the articles available in the databases and content extraction.Practical implicationsThis study supports decision-makers in understanding the critical drivers in digitalizing the supply chain. Once these factors are studied and comprehended, managers and decision-makers could better anticipate and allocate the proper resources to embark on the digitalization journey and make informed decisions.Originality/valueThe digitalization of the supply chain is more critical nowadays due to the global disruptions caused by the Coronavirus (COVID-19) pandemic and the surge of organizations moving toward the digital economy. There is a gap between the digital transformation pilot studies and implementation. The themes and factors unearthed in this study will serve as a foundation and guidelines for further theoretical research and practical implications.

Matching medical staff to long term care facilities to respond to COVID-19 outbreak.

BACKGROUND: Staff shortage is a long-standing issue in long term care facilities (LTCFs) that worsened with the COVID-19 outbreak. Different states in the US have employed various tools to alleviate this issue in LTCFs. We describe the actions taken by the Commonwealth of Massachusetts to assist LTCFs in addressing the staff shortage issue and their outcomes. Therefore, the main question of this study is how to create a central mechanism to allocate severely limited medical staff to healthcare centers during emergencies. METHODS: For the Commonwealth of Massachusetts, we developed a mathematical programming model to match severely limited available staff with LTCF demand requests submitted through a designed portal. To find feasible matches and prioritize facility needs, we incorporated restrictions and preferences for both sides. For staff, we considered maximum mileage they are willing to travel, available by date, and short- or long-term work preferences. For LTCFs, we considered their demand quantities for different positions and the level of urgency for their demand. As a secondary goal of this study, by using the feedback entries data received from the LTCFs on their matches, we developed statistical models to determine the most salient features that induced the LTCFs to submit feedback. RESULTS: We used the developed portal to complete about 150 matching sessions in 14 months to match staff to LTCFs in Massachusetts. LTCFs provided feedback for 2,542 matches including 2,064 intentions to hire the matched staff during this time. Further analysis indicated that nursing homes and facilities that entered higher levels of demand to the portal were more likely to provide feedback on the matches and facilities that were prioritized in the matching process due to whole facility testing or low staffing levels were less likely to do so. On the staffing side, matches that involved more experienced staff and staff who can work afternoons, evenings, and overnight were more likely to generate feedback from the facility that they were matched to. CONCLUSION: Developing a central matching framework to match medical staff to LTCFs at the time of a public health emergency could be an efficient tool for responding to staffing shortages. Such central approaches that help allocate a severely limited resource efficiently during a public emergency can be developed and used for different resource types, as well as provide crucial demand and supply information in different regions and/or demographics.

Counterterrorism resource allocation during a pandemic: The effects of dynamic target valuations when facing a strategic terrorist.

The outbreak of pandemics such as COVID-19 can result in cascading effects for global systemic risk. To combat an ongoing pandemic, governmental resources are largely allocated toward supporting the health of the public and economy. This shift in attention can lead to security vulnerabilities which are exploited by terrorists. In view of this, counterterrorism during a pandemic is of critical interest to the safety and well-being of the global society. Most notably, the population flows among potential targets are likely to change in conjunction with the trend of the health crisis, which leads to fluctuations in target valuations. In this situation, a new challenge for the defender is to optimally allocate his/her resources among targets that have changing valuations, where his/her intention is to minimize the expected losses from potential terrorist attacks. In order to deal with this challenge, in this paper, we first develop a defender-attacker game in sequential form, where the target valuations can change as a result of the pandemic. Then we analyze the effects of a pandemic on counterterrorism resource allocation from the perspective of dynamic target valuations. Finally, we provide some examples to display the theoretical results, and present a case study to illustrate the usability of our proposed model during a pandemic.

The Effect of Fangcang Shelter Hospitals under Resource Constraints on the Spread of Epidemics.

Since the outbreak of the COVID-19 pandemic, Fangcang shelter hospitals have been built and operated in several cities, and have played a huge role in epidemic prevention and control. How to use medical resources effectively in order to maximize epidemic prevention and control is a big challenge that the government should address. In this paper, a two-stage infectious disease model was developed to analyze the role of Fangcang shelter hospitals in epidemic prevention and control, and examine the impact of medical resources allocation on epidemic prevention and control. Our model suggested that the Fangcang shelter hospital could effectively control the rapid spread of the epidemic, and for a very large city with a population of about 10 million and a relative shortage of medical resources, the model predicted that the final number of confirmed cases could be only 3.4% of the total population in the best case scenario. The paper further discusses the optimal solutions regarding medical resource allocation when medical resources are either limited or abundant. The results show that the optimal allocation ratio of resources between designated hospitals and Fangcang shelter hospitals varies with the amount of additional resources. When resources are relatively sufficient, the upper limit of the proportion of makeshift hospitals is about 91%, while the lower limit decreases with the increase in resources. Meanwhile, there is a negative correlation between the intensity of medical work and the proportion of distribution. Our work deepens our understanding of the role of Fangcang shelter hospitals in the pandemic and provides a reference for feasible strategies by which to contain the pandemic.

Inter-hospital Transfer Decision-making During the COVID-19 Pandemic: a Qualitative Study.

BACKGROUND: Inter-hospital patient transfers to hospitals with greater resource availability and expertise may improve clinical outcomes. However, there is little guidance regarding how patient transfer requests should be prioritized when hospital resources become scarce. OBJECTIVE: To understand the experiences of healthcare workers involved in the process of accepting inter-hospital patient transfers during a pandemic surge and determine factors impacting inter-hospital patient transfer decision-making. DESIGN: We conducted a qualitative study consisting of semi-structured interviews between October 2021 and February 2022. PARTICIPANTS: Eligible participants were physicians, nurses, and non-clinician administrators involved in the process of accepting inter-hospital patient transfers. Participants were recruited using maximum variation sampling. APPROACH: Semi-structured interviews were conducted with healthcare workers across Michigan. KEY RESULTS: Twenty-one participants from 15 hospitals were interviewed (45.5% of eligible hospitals). About half (52.4%) of participants were physicians, 38.1% were nurses, and 9.5% were non-clinician administrators. Three domains of themes impacting patient transfer decision-making emerged: decision-maker, patient, and environmental factors. Decision-makers described a lack of guidance for transfer decision-making. Patient factors included severity of illness, predicted chance of survival, need for specialized care, and patient preferences for medical care. Decision-making occurred within the context of environmental factors including scarce resources at accepting and requesting hospitals, organizational changes to transfer processes, and alternatives to patient transfer including use of virtual care. Participants described substantial moral distress related to transfer triaging. CONCLUSIONS: A lack of guidance in transfer processes may result in considerable variation in the patients who are accepted for inter-hospital transfer and in substantial moral distress among decision-makers involved in the transfer process. Our findings identify potential organizational changes to improve the inter-hospital transfer process and alleviate the moral distress experienced by decision-makers.

The impact of resource allocation during the COVID-19 pandemic on cardiac surgical practice and patient outcomes: a systematic review.

OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic has shaken the world and placed enormous strain on healthcare systems globally. In this systematic review, we investigate the effect of resource allocation on cardiac surgery programs and the impact on patients awaiting elective cardiac surgery. METHODS: PubMed and Embase were systematically searched for articles published from 1 January 2019 to 30 August 2022. This systematic review included studies investigating the impact of the COVID-19 pandemic on resource allocation and the subsequent influence on cardiac surgery outcomes. A total of 1676 abstracts and titles were reviewed and 20 studies were included in this review. RESULTS: During the COVID-19 pandemic, resources were allocated away from elective cardiac surgery to help support the pandemic response. This resulted in increased wait times for elective patients, increased rates of urgent or emergent surgical intervention and increased rates of mortality or complications for patients awaiting or undergoing cardiac surgery during the pandemic. CONCLUSIONS: While the finite resources available during the pandemic were often insufficient to meet the needs of all patients as well as the influx of new COVID-19 patients, resource allocation away from elective cardiac surgery resulted in prolonged wait times, more frequent urgent or emergent surgeries and negative impacts on patient outcomes. Understanding the impacts of delayed access to care with regards to urgency of care, increased morbidity and mortality and increased utilization of resources per indexed case needs to be considered to navigate through pandemics to minimize the lingering effects that continue to negatively impact patient outcomes.

Optimal capacity sharing for global genomic surveillance.

Recent technological advances and substantial cost reductions have made the genomic surveillance of pathogens during pandemics feasible. Our paper focuses on full genome sequencing as a tool that can serve two goals: the estimation of variant prevalences, and the identification of new variants. Assuming that capacity constraints limit the number of samples that can be sequenced, we solve for the optimal distribution of these capacities among countries. Our results show that if the principal goal of sequencing is prevalence estimation, then the optimal capacity distribution is less than proportional to the weights (e.g., sizes) of countries. If, however, the main aim of sequencing is the detection of new variants, capacities should be allocated to countries or regions that have the most infections. Applying our results to the sequencing of SARS-CoV-2 in 2021, we provide a comparison between the observed and a suggested optimal capacity distribution worldwide and in the EU. We believe that following such quantifiable guidance will increase the efficiency of genomic surveillance for pandemics.

Mitigating the COVID-19 pandemic through data-driven resource sharing

COVID-19 outbreaks in local communities can result in a drastic surge in demand for scarce resources such as mechanical ventilators. To deal with such demand surges, many hospitals (1) purchased large quantities of mechanical ventilators, and (2) canceled/postponed elective procedures to preserve care capacity for COVID-19 patients. These measures resulted in a substantial financial burden to the hospitals and poor outcomes for non-COVID-19 patients. Given that COVID-19 transmits at different rates across various regions, there is an opportunity to share portable healthcare resources to mitigate capacity shortages triggered by local outbreaks with fewer total resources. This paper develops a novel data-driven adaptive robust simulation-based optimization (DARSO) methodology for optimal allocation and relocation of mechanical ventilators over different states and regions. Our main methodological contributions lie in a new policy-guided approach and an efficient algorithmic framework that mitigates critical limitations of current robust and stochastic models and make resource-sharing decisions implementable in real-time. In collaboration with epidemiologists and infectious disease doctors, we give proof of concept for the DARSO methodology through a case study of sharing ventilators among regions in Ohio and Michigan. The results suggest that our optimal policy could satisfy ventilator demand during the first pandemic's peak in Ohio and Michigan with 14% (limited sharing) to 63% (full sharing) fewer ventilators compared to a no sharing strategy (status quo), thereby allowing hospitals to preserve more elective procedures. Furthermore, we demonstrate that sharing unused ventilators (rather than purchasing new machines) can result in 5% (limited sharing) to 44% (full sharing) lower expenditure, compared to no sharing, considering the transshipment and new ventilator costs.

Current status and prospect of global development of lung transplantation

During the novel coronavirus pneumonia (COVID-19) pandemic from 2020 to 2021, lung transplantation entered a new stage of development worldwide. Globally, more than 70 000 cases of lung transplantation have been reported to the International Society for Heart and Lung Transplantation (ISHLT). With the development of medical techniques over time, the characteristics of lung transplant donors and recipients and the indications of pediatric lung transplantation recipients have undergone significant changes. Application of lung transplantation in the treatment of COVID-19-related acute respiratory distress syndrome (ARDS) has also captivated worldwide attention. Along with persistent development of lung transplantation, it will be integrated with more novel techniques to make breakthroughs in the fields of artificial lung and xenotransplantation. In this article, research progresses on the characteristics of lung transplant donors and recipients around the world were reviewed and the development trend was predicted, enabling patients with end-stage lung disease to obtain more benefits from the development of lung transplantation technique.Copyright © 2022 Organ Transplantation. All rights reserved.

Development and prospect of clinical research on lung transplantation in post-COVID-19 era

Lung transplantation has been advanced for nearly half a century around the globe, and it has been developed rapidly for over 20 years in China. The field of lung transplantation in China has been gradually integrated into the international community. The outbreak of novel coronavirus pneumonia (COVID-19) in 2020 brought big challenges, as well as diverted the worldwide attention to the development of lung transplantation in China, accelerating international communication and cooperation. With the steadily deepening of clinical and basic research on lung transplantation for severe cases of COVID-19, organ transplant physicians have deepened the understanding and thinking of the maintenance of donors, selection of elderly and pediatric candidates, and perioperative management of recipients, as the future perspective of lung transplantation in China. For interdisciplinary research related to lung transplantation, it is necessary to carry out multi-center clinical trials with qualified study design and constantly promote the theoretic and practical innovation.Copyright © 2021 The authors.