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1.
22nd Annual General Assembly of the International Association of Maritime Universities Conference, AGA IAMUC 2022 ; 2022-October, 2022.
Article in English | Scopus | ID: covidwho-2169937

ABSTRACT

The article discusses and analyses the importance of the Enterprise Resource Planning (ERP) principles in the University environment. It is crucial to properly evaluate and assess the effectiveness of the university's administrative and academic staff performances as an organizational form. In the last couple of years, besides the negative impact, COVID-19 positively impacted the technological development at the HEIs. So, the ERP system remains a powerful program that allows businesses to systematize all important processes. Drastically, doing so reduces costs and the possibility of making mistakes, thereby increasing efficiency and profitability. The ERP system comprises the main sections of an organization, such as financial, human resources, production, logistics, etc., but to the present does not cover the whole university's administrative and learning activities. Thus, most HEIs effectively implement technologies and E-Systems but, in most cases, are fragmented and do not cover whole university activities. The purpose of the article is to create one united platform based on an ERP system, whereby we will get the new model of the University Recourse Planning (URP) system and will be implemented and facilitate the: unified digital database, improvement of university efficiency, less bureaucracy, delayed decisions and the and quality improvement. As a result, all the data will be shown as a "Dashboard" to the top management of HEI, which will perceive information about the weaknesses and gaps for achieving specific objectives. © 2022 IAMUC. All Rights Reserved.

2.
3rd International Symposium on Artificial Intelligence for Medical Sciences, ISAIMS 2022 ; : 369-375, 2022.
Article in English | Scopus | ID: covidwho-2194145

ABSTRACT

Public health emergencies will pose an enormous challenge to healthcare service systems. As COVID-19 rage across the globe, we realize that COVID-19 exposes the problem of inadequate research on the dispatch of emergency medical personnel in response to a major epidemic outbreak. In the face of major public health emergencies, failure in timely satisfaction of healthcare demands by local healthcare professionals necessitates human resource support from other regions. To address this issue, further research is needed to gain better insights into interregional emergency human resource allocation. This paper aims to offer attention to patients' medical needs and suppose that there are support hubs outside the outbreak region offering an external supply of medical personnel. The hospitals in these support hubs are categorized based on variables such as capacity, medical capability, and the number of dispatched personnel per day. An interregional emergency allocation model was established to consider the proper doctor-patient ratio and nurse-patient ratio in emergency response using methods such as mathematical programming. And relevant management suggestions were then offered via analysis. Research in this paper provides allocation models and proposals that healthcare professionals can refer to when making resource allocation decisions in emergency response. © 2022 ACM.

3.
Asia-Pacific Journal of Business Administration ; 2023.
Article in English | Web of Science | ID: covidwho-2191293

ABSTRACT

PurposeDrawing on the concept of superior resource, capability and processes of the resource-based theory of the firm, the purpose of the current study is to analyze the influence of firms' winner-picking strategic approach on firm performance (FP) via a direct and indirect mechanism.Design/methodology/approachUsing survey data of 104 diversified manufacturing firms, the current study analyzed the conditional indirect effect of firms' strategic approach on efficient resource allocation with the help of Statistical Analysis Software (SAS) process macros.FindingsThe study found that firms' choices of winner-picking approach can undermine the resource allocation efficiency when not perfectly blended with firms' access to the resource. Furthermore, the effect of winner-picking strategy (WPS) on resource allocation efficiency via firms' competitive advantage (CA) can be greater when both strategic choice and resources are employed adequately.Research limitations/implicationsDespite making a unique contribution, the present study has a few limitations requiring researchers' attention to be tackled in the forthcoming. This includes a little amount of data, a self-reporting technique and failure to include all the possible reasons that could lead to inefficient resource allocation.Practical implicationsThe present research has potential applications for managers of the manufacturing industry in a period of sheer uncertainty [coronavirus disease 2019 (COVID-19)]. First, the study alerts managers about the challenges of underinvestment and overinvestment while allocating resources. At the same time, this study provides an important implication for managing the importance of firms' access to capital (AC).Originality/valueThe current study has made a sizeable impression in the literature on internal resource allocation and resource-based theory of the firm by recommending a model that augments the theoretical foundation of strategic management of the firms. As there are only a handful of studies on this grave issue in the context of developing economies, thus, closely considering these insights would be helping for the firms for allocating resources efficiently in the manufacturing industry.

4.
Critical Care Medicine ; 51(1 Supplement):127, 2023.
Article in English | EMBASE | ID: covidwho-2190504

ABSTRACT

INTRODUCTION: In 2011, the task force on Pediatric Emergency Mass Critical Care (PEMCC) noted that no North American emergency had overwhelmed ICU services since the modern development of critical care. During the COVID-19 global pandemic, resource allocation became a challenge and healthcare workers' opinions of resource allocation had not been rigorously addressed in the literature. Our primary goal was to elucidate PICU providers' opinions of various resource allocation strategies. METHOD(S): An anonymous, electronic survey was sent to 173 PICU providers at a single institution - 47 MDs/APRNs and 126 RNs. Seven strategies for resource allocation were surveyed: (1) likelihood of survival;(2) age;(3) baseline neurologic status;(4) predicted length of time requiring resource;(5) lottery system;(6) first come first served;(7) immigration status. Each strategy was surveyed by three methods. First, a simple yes/no format was used to survey support of each resource allocation strategy. Second, the survey presented case scenarios and asked participants to choose a patient to receive the resource. Finally, participants ranked the strategies by importance. We analyzed data using descriptive statistics and average numerical rank. RESULT(S): Respondents included 19 MD/DOs/APRNs and 23 RNs. 85.7% believed the hospital should have a scarce resource allocation protocol. In response to various resource allocation strategies, participant "yes" responses were: 100% likelihood of survival;83% baseline neurologic status;81% time scarce resource required;64% age;5% first come first served;5% lottery system;2% immigration status. For case scenarios, majority of participants chose patients with a higher likelihood of survival, shorter time requiring scare resource and/or previously healthy status. The average rank of each strategy (most important scored 1): Likelihood of survival 1.14;Baseline neurologic status 2.98;Predicted length of time requiring resource 3.02;Age 3.9;Lottery system 4.81;First come first served 5.29;Immigration status 6.86. CONCLUSION(S): Likelihood of survival plays a consistently important role in determining resource allocation for PICU providers. Majority of providers believe a lottery system should not be used, yet many select a lottery system approach when faced with clinical scenarios.

5.
Nonlinear Dyn ; 110(3): 2913-2929, 2022.
Article in English | MEDLINE | ID: covidwho-1965564

ABSTRACT

In the pandemic of COVID-19, there are exposed individuals who are infected but lack distinct clinical symptoms. In addition, the diffusion of related information drives aware individuals to spontaneously seek resources for protection. The special spreading characteristic and coevolution of different processes may induce unexpected spreading phenomena. Thus we construct a three-layered network framework to explore how information-driven resource allocation affects SEIS (susceptible-exposed-infected-susceptible) epidemic spreading. The analyses utilizing microscopic Markov chain approach reveal that the epidemic threshold depends on the topology structure of epidemic network and the processes of information diffusion and resource allocation. Conducting extensive Monte Carlo simulations, we find some crucial phenomena in the coevolution of information diffusion, resource allocation and epidemic spreading. Firstly, when E-state (exposed state, without symptoms) individuals are infectious, long incubation period results in more E-state individuals than I-state (infected state, with obvious symptoms) individuals. Besides, when E-state individuals have strong or weak infectious capacity, increasing incubation period has an opposite effect on epidemic propagation. Secondly, the short incubation period induces the first-order phase transition. But enhancing the efficacy of resources would convert the phase transition to a second-order type. Finally, comparing the coevolution in networks with different topologies, we find setting the epidemic layer as scale-free network can inhibit the spreading of the epidemic.

6.
Siberian Electronic Mathematical Reports-Sibirskie Elektronnye Matematicheskie Izvestiya ; 19(2):835-851, 2022.
Article in English | Web of Science | ID: covidwho-2164679

ABSTRACT

We discuss the possibilities of the new approach to the interindustry linkages modeling for the analysis of regional macroeconomic effects of Covid-19. Our approach is based on the mathematical framework of nonlinear input-output balance that allows to find the equilibrium point in the set of industry inputs and prices by solving the primal nonlinear resolute allocation problem and the Young dual problem of prices formation. We identify and calibrate the model on the base of aggregated official input-output statistics of Kazakhstan. Given the scenario conditions for primal factors prices and final consumption in the economy the model allows to evaluate the new competitive equilibrium in the production network. The advantage of the model is non-linearity of balances and technologies that allows substitution of industry inputs. In the case of technologies with constant elasticity of substitution (CES) we apply the model to analysis of macroeconomic responses of the Kazakhstan economy to the Covid-19 pandemic.

7.
2022 IEEE European Technology and Engineering Management Summit, E-TEMS 2022 ; : 136-141, 2022.
Article in English | Scopus | ID: covidwho-2161389

ABSTRACT

The use of technology enhanced learning in education institutions has been developing at a rapid pace. Higher education administration affirms the value of digitization in learning platforms, especially in view of the response to the COVID-19 pandemic. The conventional education system is not consistent with the changing demands of modern education concepts, which are the driving factors towards formation and development of the digital educational ecosystem. To digitally transform education systems within a cross organizational environment, educational institutions should be interconnected, and the learners are able to access the learning modules from anywhere in the world. The effective allocation of learning modules to the students is of crucial importance due to the scarceness of such resources. The main objective of this paper is to identify the methods for assigning digital learning modules from a set of existing solutions. Facilitated by optimal resource allocation, the digital ecosystem can provision learning instances via virtual machines or containers and allocate it based on demand. Through the optimal resource allocation, the overall cost and power consumption shall be decreased and the availability of the learning service shall be increased. In this paper, literature research of different scheduling and allocation policies are discussed under varying statistical processes, priority, and performance metrics to increase efficiency and reduce operating cost of servers with no allocated task. © 2022 IEEE.

8.
Acm Computing Surveys ; 55(3), 2023.
Article in English | Web of Science | ID: covidwho-2153113

ABSTRACT

Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT) have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may be infeasible in realistic healthcare scenarios due to the high scalability of modern healthcare networks and growing data privacy concerns. Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training without sharing raw data. Accordingly, we provide a comprehensive survey on the use of FL in smart healthcare. First, we present the recent advances in FL, the motivations, and the requirements of using FL in smart healthcare. The recent FL designs for smart healthcare are then discussed, ranging from resource-aware FL, secure and privacy-aware FL to incentive FL and personalized FL. Subsequently, we provide a state-of-the-art review on the emerging applications of FL in key healthcare domains, including health data management, remote health monitoring, medical imaging, and COVID-19 detection. Several recent FL-based smart healthcare projects are analyzed, and the key lessons learned from the survey are also highlighted. Finally, we discuss interesting research challenges and possible directions for future FL research in smart healthcare.

9.
Sustainability ; 14(22):14979, 2022.
Article in English | ProQuest Central | ID: covidwho-2143547

ABSTRACT

Public–private partnership (PPP) policy is essential to alleviating the local government debt burden and improving resource allocation efficiency. This paper empirically examines the impact of fiscal pressure on PPP investment in Chinese prefecture-level cities from 2014 to 2019 using the ordinary least-squares (OLS) module. Moreover, we also investigate how fiscal pressure influences PPP investment and test the influenced mechanism from other perspectives. The results show the following. (1) Fiscal pressure on the government has a significant positive effect on PPP investment at the prefectural level. (2) The marketization process is the mediated effect of the relationship between fiscal pressure and PPP investment. Fiscal pressure will stimulate the regional marketization process, thus promoting PPP investment. (3) Fiscal pressure has a significant positive effect on PPP investment in the middle region, while the effect is not significant in the eastern and western regions. Meanwhile, the effect is not significant in central cities, but there is a significant positive effect in ordinary cities. (4) The effect of fiscal pressure on PPP investment is not significant in the private reward mode of government payment, but there is a significant positive effect in the mode of user payment and feasibly insufficient subsidies. Our studies could also provide practical suggestions for sustainable development of PPP policy and solving the fiscal pressure of the current economic recession under the COVID-19 pandemic.

10.
Eurohealth ; 27(1):20-25, 2021.
Article in English | GIM | ID: covidwho-2126003

ABSTRACT

During COVID-19, attention was drawn to a lack of functional governance frameworks for health emergencies. Routine governance structures were neither agile, nor flexible enough to operate with the speed required for urgent and coordinated action within complex and far-reaching responses. WHO's Emergency Response Framework has significantly contributed to a stronger WHO response capacity in the European Region by providing accountabilities, responsibilities, delegation of authority, and rapid access to resources for response, while also allowing for participating members to be held accountable for their actions. We argue that now is the time to move health emergency management forwards by supporting States in strengthening their emergency governance architectures.

11.
15th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2022 ; : 50-59, 2022.
Article in English | Scopus | ID: covidwho-2138176

ABSTRACT

Active micro-mobility decreases traffic, bolsters personal health, and helps communities thrive by protecting the environment Moreover, sustainable micro-mobility demand is expected to get boosted in the present and post-COVID society. In this work we highlight the micro-mobility modes of walkability and bicycling to city administrators controlling urban city-space, by adapting the mobility parameters and their use cases through a map-based interface. Software tools and web-based applications are introduced for easy policy decisions by city managers. Present study scope is circumscribed by exploration of different parameters in traditional and state of art data science models, for resource planning like cycle usage prediction and planning. These parameters show hazard safe-distance pedestrian flow, optimal resource planning, amenity reach (10 min cycling and walking distance) and mobility using walking and cycling modes. Parameters of the traditional Social Force Model for Pedestrian Dynamics are also inspected, according to COVID social norms, to capture safe pedestrian flow density. Finally, the analysis of two case studies, of Bhubaneshwar city and New Delhi, in India, are discussed for policy suggestions to enhance mobility via sustainable micro-mobility modes. The developed system assists managers in decisions based on urban data intelligence, and at user end eases commute related mental tension, anxiety and dependencies. The developed application is running live on our server maintained at Edinburgh University. © 2022 ACM.

12.
Ieee Access ; 10:116402-116424, 2022.
Article in English | Web of Science | ID: covidwho-2123156

ABSTRACT

There has been a gigantic stir in the world's healthcare sector for the past couple of years with the advent of the Covid-19 pandemic. The healthcare system has suffered a major setback and, with the lack of doctors, nurses, and healthcare facilities the need for an intelligent healthcare system has come to the fore more than ever before. Smart healthcare technologies and AI/ML algorithms provide encouraging and favorable solutions to the healthcare sector's challenges. An Intelligent Human-Machine Interactive system is the need of the hour. This paper proposes a novel architecture for an Intelligent and Interactive Healthcare System that incorporates edge/fog/cloud computing techniques and focuses on Speech Recognition and its extensive application in an interactive system. The focal reason for using speech in the healthcare sector is that it is easily available and can easily predict any physical or psychological discomfort. Simply put, human speech is the most natural form of communication. The Hidden Markov Model is applied to process the proposed approach as using the probabilistic approach is more realistic for prediction purposes. Ongoing projects and directions for future work along with challenges/issues are also addressed.

14.
J Natl Compr Canc Netw ; 20(11): 1190-1192, 2022 11.
Article in English | MEDLINE | ID: covidwho-2110728

ABSTRACT

No population-based study exists to demonstrate the full-spectrum impact of COVID-19 on hindering incident cancer detection in a large cancer system. Building upon our previous publication in JNCCN, we conducted an updated analysis using 12 months of new data accrued in the pandemic era (extending the study period from September 26, 2020, to October 2, 2021) to demonstrate how multiple COVID-19 waves affected the weekly cancer incidence volume in Ontario, Canada, and if we have fully cleared the backlog at the end of each wave.


Subject(s)
COVID-19 , Neoplasms , Humans , COVID-19/epidemiology , Neoplasms/diagnosis , Neoplasms/epidemiology , Ontario/epidemiology
15.
2022 ACM Conference on Equity andAccess in Algorithms, Mechanisms, and Optimization, EAAMO 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2120520

ABSTRACT

Motivated by COVID-19 vaccine allocation, where vulnerable subpopulations are simultaneously more impacted in terms of health and more disadvantaged in terms of access to the vaccine, we formalize and study the problem of resource allocation when there are inherent access differences that correlate with advantage and disadvantage. We identify reducing resource disparity as a key goal in this context and show its role as a proxy to more nuanced downstream impacts. We develop a concrete access model that helps quantify how a given allocation translates to resource flow for the advantaged vs. the disadvantaged, based on the access gap between them. We then provide a methodology for access-aware allocation. Intuitively, the resulting allocation leverages more vaccines in locations with higher vulnerable populations to mitigate the access gap and reduce overall disparity. Surprisingly, knowledge of the access gap is often not needed to perform access-aware allocation. To support this formalism, we provide empirical evidence for our access model and show that access-aware allocation can significantly reduce resource disparity and thus improve downstream outcomes. We demonstrate this at various scales, including at county, state, national, and global levels. © 2022 Owner/Author.

16.
Int J Environ Res Public Health ; 19(22)2022 Nov 08.
Article in English | MEDLINE | ID: covidwho-2110068

ABSTRACT

Optimizing the allocation of basic medical services and ensuring their equity are necessary to improve the ability to respond to public health emergencies and promote health equity in the context of COVID-19. This study aims to analyze the equity of Guangzhou's basic medical service and identify areas where health resources are relatively scarce. The spatial distribution and patterns of basic medical services were analyzed using kernel density analysis and standard deviation ellipse. The equity was analyzed using the Gini coefficient and Lorenz curve in terms of population and geographical area, respectively. Considering the medical demand and supply sides, the Gaussian two-step floating catchment area method was used to analyze the accessibility to different levels of medical institutions. The kernel density analysis and standard deviation ellipse showed that the spatial distribution of medical and health resources in Guangzhou is unevenly distributed, and high-level hospitals and medical resources are mainly concentrated in the centrum. From the perspective of population, Guangzhou's medical equity is generally reasonable. The accessibility of medical institutions differs with different levels, and the tertiary medical institutions have the best accessibility, while the unclassified, primary, and secondary medical institutions generally have lower accessibility. The accessibility of districts in Guangzhou varies greatly. Areas in the center are most accessible to basic medical services, while accessibility in outskirt areas has gradually decreased. Conclusion: The quantity of per capita medical and health resources in Guangzhou, as evidenced by basic medical services, is sufficient, but the spatial distribution is unequal. The developed city center enjoys more adequate healthcare resources than the distant suburbs. Primary healthcare should be built, especially in distant suburbs, to strengthen basic medical service equity in Guangzhou.


Subject(s)
COVID-19 , Health Services Accessibility , Humans , COVID-19/epidemiology , Health Promotion , Catchment Area, Health , Health Resources
17.
Sustainability ; 14(19):12716, 2022.
Article in English | ProQuest Central | ID: covidwho-2066456

ABSTRACT

In broader terms, a Smart City improves the quality of life of its citizens through the effective use of innovative (digital) solutions. While innovative Smart City solutions keep growing, attention has been paid to resilience-making within Smart Cities, recognising that disasters are unavoidable. In light of the characteristics of a Smart City (smartness requirements) being inchoate and vague, different Smart Cities develop their own smartness criteria. Regardless of the Smart City type, smartness criteria need to adequately embed resilience. Integrating the resilience concept provides a strategic direction for Smart Cities and there is a significant positive relationship between the two concepts, Smart Cities, and urban resilience. Although Smart Cities are increasingly growing in popularity all around the world, there is a lack of research to guide a Smart City to define its smartness reflecting on disaster resilience. This paper intends to address this research gap by setting out a set of smartness criteria (with particular reference to urban (city) resilience) which should compulsorily feature in any type of Smart City that desires to be resilient. The study undertakes a systematic literature review to provide a new dimension, depth, and value to existing research discoveries. The findings are presented by structuring ten urban (city) resilience dimensions built upon six Smart City dimensions: smart economy, smart governance, smart people, smart mobility, smart living, and smart environment. Our findings make a niche contribution to knowledge by guiding Smart Cities that intend to build, enhance, and/or sustain resilience, to develop smartness criteria/smart characteristics reflecting on urban resilience. The research outcomes will be of large importance to Smart City policymakers, administrators, project managers, etc. to efficiently manage extreme events timely with optimal resource allocation and will be of specific interest to all the stakeholders (for instance, the innovators) in a Smart City ecosystem who may use the research outcomes as a decision-making tool.

18.
American Journal of Public Health ; 112:S237-S240, 2022.
Article in English | ProQuest Central | ID: covidwho-2045804

ABSTRACT

Hailed by some as a paradigm shift in nursing education and practice, this emphasis is not new for public health nurse educators. Since 1965, community and public health nursing content has been part of the required baccalaureate nursing curriculum.11 However, advancing the quality and augmenting the impact of community and public health nursing education, practice, and research is critical for improved local to global health outcomes. [...]we assert the need for the clinical core of the nursing curriculum to include opportunities for intervention at all levels of practice: preparing nurses to design and deliver care at the level of the individual, family, community, systems, and populations.12 To implement this directional change, essential knowledge and skills in systems awareness, change management, cost containment, resource allocation, communication, team building, equity, and inclusion are required for competent, evidence-based practice, as is the development of competencies in informatics, data science, design, and systems thinking. Additionally, effective advocacy requires consideration of the social needs of individuals, which are inextricably connected to structural determinants at the community, society, and policy level. [...]to affect the health of populations, nurses are called upon to make this broader, more integral connection between policies, systems, and environmental impact. Many community and public health nurses work in small, local public health departments unaffiliated with large academic institutions or hospitals and have limited access to evidence-based resources or financial support for professional development. Since 2017, the Nursing Experts Translating the Evidence project, an interprofessional collaborative effort between nurses and librarians, has been educating public health nurses on the acquisition, translation, and application of evidence to inform their practice.19 Through active community-academic-practice partnerships, community and public health nursing educators and governmental and nongovernmental public health agencies can build capacity for community and public health nursing practice for the future, as we continue to apply evidence-based, data-driven problem solving through the pandemic and beyond.

19.
Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics (Icinco) ; : 616-625, 2022.
Article in English | Web of Science | ID: covidwho-2044132

ABSTRACT

The COVID-19 pandemic highlighted the fragility of the world in addressing a global health threat. The available resources of the pre-pandemic national health systems were inadequate to cope with the huge number of infected subjects needing health care and with the rapidity of the infection spread characterizing the COVID-19 outbreak. Indeed, an adequate allocation of the resources could produce in principle a strong reduction of the infection spread and of the hospital burden, preventing the collapse of the health system. In this work, taking inspiration from the COVID-19 and the difficulties in facing the emergency, an optimal problem of resource allocation is formulated on the basis of an ODE multi-group model composed by a network of SEIR-like submodels. The multi-group structure allows to differentiate the epidemic response of different populations or of various subgroups in the same population. In fact, an epidemic does not affect all populations in the same way, and even within the same population there can be epidemiological differences, like the susceptibility to the virus, the level of infectivity of the infectious subjects and the recovery from the disease. The subgroups are selected within the total population based on some peculiar characteristics, like for instance age, work, social condition, geographical position, etc., and they are connected by a network of contacts that allows the virus circulation within and among the groups. The proposed optimal control problem aims at defining a suitable monitoring campaign that is able to optimally allocate the number of swab tests between the subgroups of the population in order to reduce the number of infected patients (especially the most fragile ones) so reducing the epidemic impact on the health system. The proposed monitoring strategy can be applied both during the most critical phases of the emergency and in endemic conditions, when an active surveillance could be crucial for preventing the contagion rise.

20.
Computers & Operations Research ; : 106028, 2022.
Article in English | ScienceDirect | ID: covidwho-2041644

ABSTRACT

We consider the problem of optimizing locations of distribution centers (DCs) and plans for distributing resources such as test kits and vaccines, under spatiotemporal uncertainties of disease spread and demand for the resources. We aim to balance the operational cost (including costs of deploying facilities, shipping, and storage) and quality of service (reflected by demand coverage), while ensuring equity and fairness of resource distribution across multiple populations. We compare a sample-based stochastic programming (SP) approach with a distributionally robust optimization (DRO) approach using a moment-based ambiguity set. Numerical studies are conducted on instances of distributing COVID-19 vaccines in the United States and test kits, to compare SP and DRO models with a deterministic formulation using estimated demand and with the current resource distribution plans implemented in the US. We demonstrate the results over distinct phases of the pandemic to estimate the cost and speed of resource distribution depending on scale and coverage, and show the “demand-driven” properties of the SP and DRO solutions. Our results further indicate that if the worst-case unmet demand is prioritized, then the DRO approach is preferred despite of its higher overall cost. Nevertheless, the SP approach can provide an intermediate plan under budgetary restrictions without significant compromises in demand coverage.

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