Machine Learning and Laboratory Values in the Diagnosis, Prognosis and Vaccination Strategy of COVID-19

In March 2020, the World Health Organization (WHO) declared a pandemic status for COVID-19 disease caused by SARS-CoV-2 infection. Early diagnosis undoubtedly plays a fundamental role in the management of emergencies for the treatment of infected patients, whose prognosis may benefit from early treatment and containment of contagions in asymptomatic or pauci-symptomatic subjects. To date, the gold-standard technique for diagnosing SARS-CoV-2 infection is the identification of viral genomic material (RNA) by molecular diagnosis. To improve the pandemic management, the need for enhanced diagnostic capacity for SARS-CoV-2 infections soon emerged, with rapid, accurate, and easily accessible methods. Machine learning (ML) models could help define the diagnosis and, in some cases, even the prognosis of COVID-19 patients. This chapter describes ML models based on laboratory tests combined with other biometric parameters;the applications aimed at optimizing diagnosis and prognosis were mainly described. Finally, the vaccination campaign against SARS-CoV-2. The fields most considered were the heterogeneity in patient selection, laboratory parameters used, the machine learning models and their validation and implementation. Furthermore, we briefly describe artificial intelligence's potentialities in planning different strategies for the vaccination campaigns against SARS-COV-2 through laboratory tests. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

[Clinical quality governance-a practical approach in general practice with the example of influenza vaccination]. / Klinische Qualitätssteuerung ­ ein praktischer Versuch in der Hausarztpraxis am Beispiel der Influenzaimpfung.

We understand clinical quality governance (CQG) as quality management in the clinical domain. In 2020, presumably due to the coronavirus pandemic, more patients requested to be vaccinated against influenza as compared to previous years so that it became apparent that there would be a shortage for high-risk patients. To meet the problem, we started a CQG process. This article is explicitly not a research article but an exemplary description of a CQG process intended as a stimulus and for discussion. We initiated the following process: (1) evaluation of the present state, (2) patients who already had requested a vaccination were prioritized and vaccinated first, and (3) contacting via telephone and vaccination of high-risk patients not on the list. We chose patients with chronic obstructive pulmonary disease (COPD) older than 60 years as an indicator for the group of highest priority. In the beginning only 3 (8%) of our 38 patients with COPD were vaccinated against influenza. After prioritization and vaccination of the high-risk collective in the list of those who had requested to be vaccinated, 25 (66%) of our 38 patients with COPD were vaccinated. After a phone call of high-risk patients not on the list, 28 (74%) patients were vaccinated. This represents an increase of vaccination coverage from 8% to 74% which is close to the rate recommended by the World Health Organization (WHO). In times of a pandemic, family physicians occasionally have to deal with a scarcity of resources and have to develop strategies for fair resource allocation. Not only in this context is CQG worth the effort. The generation of list queries could be improved by the providers of electronic patient records.

The Impact of the COVID-19 Pandemic on Neurosurgery Worldwide

The novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory distress syndrome coronavirus 2 (SARS-CoV-2), first appeared in December 2019 and was declared a pandemic by the World Health Organization on March 11, 2020 (World Health Organization. WHO director-general's opening remarks at the media briefing on COVID-19—11 March 2020. https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19%2D%2D-11-march-2020. Accessed 2020). By September 9, 2020, 27.7 million cases and 0.9 million deaths were confirmed globally (Center for Systems Science and Engineering – Johns Hopkins Coronavirus Resource Center: COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. https://coronavirus.jhu.edu/map.html. Accessed 2020). This disease placed an unprecedented strain on healthcare systems around the world (Remuzzi and Remuzzi. Lancet. 395(10231):1225–8, 2020) and had a substantial effect on clinical practice across all surgical specialties, with neurosurgery being no exception (Bernstein. J Neurosurg. 2020:1–2. https://doi.org/10.3171/2020.4.JNS201031). Many hospitals implemented no-visitor policies and COVID-19 testing for all inpatients in order to prevent spread and protect patients and healthcare workers (Calderwood. Infect Control Hosp Epidemiol. 2020:1–9. https://doi.org/10.1017/ice.2020.303). To conserve beds, workforce, and valuable resources such as masks, gowns, and ventilators, surgeons had to restrict operations to emergency and essential interventions. Some neurosurgeons were redeployed to new intradepartmental roles, others lateralized to provide care for coronavirus patients. In order to limit in-person interactions and contagion, there was a surge in telehealth and digital innovation for remote monitoring and management. Research laboratories were closed for prolonged periods. Medical education and residency training were also substantially altered, with cancellation of many in-person events and a transformation to online meetings and educational sessions. In this chapter, we discuss the impact of COVID-19 on the global neurosurgery community with respect to clinical care, education, and research. While the pandemic has caused tremendous disruption in global neurosurgery already, there is hope that many of the lessons learned during this time have contributed to our resilience and preparedness for the future, be it a second wave of COVID-19 or a new unexpected challenge. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

A Delphi consensus checklist helped assess the need to develop rapid guideline recommendations.

BACKGROUND AND OBJECTIVES: We aimed to develop a checklist to aid guideline developers in determining which scientific or societal cause ("triggers") are relevant when considering to initiate a rapid recommendation procedure. METHODS: We conducted a two-round modified Delphi procedure with a panel of Dutch guideline experts, clinicians, and patient representatives. A previously conducted systematic literature review and semistructured interviews with four science journalists were used to generate a list of potential items. This item list was submitted to the panel for discussion, reduction and refinement into a checklist. RESULTS: Thirteen experts took part. Two questionnaires were completed in which participants scored an initial list of 64 items based on relevance. During two online meetings, the scores were discussed, irrelevant items were removed, and relevant items were reformulated into seven questions. The final "quickscan assessment of the need for a rapid recommendation" covers user perspective, scientific evidence, clinical relevance, clinical practice variation, applicability, quality of care and public health outcomes, and ethical/legal considerations. CONCLUSION: The quickscan aids guideline developers in systematically assessing whether a trigger expresses a valid need for developing a rapid recommendation. Future research could focus on the applicability and validity of the checklist within guideline development programs.

A novel mathematical model for prioritization of individuals to receive vaccine considering governmental health protocols.

Infectious diseases drive countries to provide vaccines to individuals. Due to the limited supply of vaccines, individuals prioritize receiving vaccinations worldwide. Although, priority groups are formed based on age groupings due to the restricted decision-making time. Governments usually ordain different health protocols such as lockdown policy, mandatory use of face masks, and vaccination during the pandemics. Therefore, this study considers the case of COVID-19 with a SEQIR (susceptible-exposed-quarantined-infected-recovered) epidemic model and presents a novel prioritization technique to minimize the social and economic impacts of the lockdown policy. We use retail units as one of the affected parts to demonstrate how a vaccination plan may be more effective if individuals such as retailers were prioritized and age groups. In addition, we estimate the total required vaccine doses to control the epidemic disease and compute the number of vaccine doses supplied by various suppliers. The vaccine doses are determined using optimal control theory in the solution technique. In addition, we consider the effect of the mask using policy in the number of vaccine doses allocated to each priority group. The model's performance is evaluated using an illustrative scenario based on a real case.

Closing the Gap Between Decision Analysis and Policy Analysts Before the Next Pandemic

Decision analysis (DA) is an explicitly prescriptive discipline that separates beliefs about uncertainties from value preferences in modeling to support decision making. Researchers have been advancing DA tools for the last 60 years to support decision makers handling complex decisions requiring subjective judgments. Recently, some DA researchers and practitioners wondered whether the difficult decisions made during the COVID-19 pandemic regarding testing, masking, closing and reopening businesses, allocating ventilators, and prioritizing vaccines would have been improved with more DA involvement. With its focus on quantifying uncertainties, value trade-offs, and risk attitudes, DA should have been a valuable tool for decision makers during the pandemic. To influence decisions, DA applications require interactions with policymakers and experts to construct formal representations of the decision frame, elicit uncertainties, and assess risk tolerances and trade-offs among competing objectives. Unfortunately, such involvement of decision analysts in the process of decision making and policy setting did not occur during much of the COVID-19 pandemic. This lack of participation may have been partly because many decision makers were unaware of when DA could be valuable in helping with the challenges of the COVID-19 pandemic. In addition, decision analysts were perhaps not sufficiently adept at inserting themselves into the policy process at critical junctures when their expertise could have been helpful.

Optimal Hospital Care Scheduling During the SARS-CoV-2 Pandemic

The COVID-19 pandemic has seen dramatic demand surges for hospital care that have placed a severe strain on health systems worldwide. As a result, policy makers are faced with the challenge of managing scarce hospital capacity to reduce the backlog of non-COVID patients while maintaining the ability to respond to any potential future increases in demand for COVID care. In this paper, we propose a nationwide prioritization scheme that models each individual patient as a dynamic program whose states encode the patient's health and treatment condition, whose actions describe the available treatment options, whose transition probabilities characterize the stochastic evolution of the patient's health, and whose rewards encode the contribution to the overall objectives of the health system. The individual patients' dynamic programs are coupled through constraints on the available resources, such as hospital beds, doctors, and nurses. We show that the overall problem can be modeled as a grouped weakly coupled dynamic program for which we determine near-optimal solutions through a fluid approximation. Our case study for the National Health Service in England shows how years of life can be gained by prioritizing specific disease types over COVID patients, such as injury and poisoning, diseases of the respiratory system, diseases of the circulatory system, diseases of the digestive system, and cancer.

Benchmarking of COVID-19 testing facilities: a case in the Philippines

PurposeWith the aggressive movement towards testing for COVID-19 across the globe, this study aims to shed light on how testing facilities perform in an operational perspective.Design/methodology/approachWith 102 testing facilities in the Philippines, the relative efficiencies of each facility are quantified using a data envelopment analysis technique. Afterwards, a best-worst method was conducted to assign priority weights to each testing facility.FindingsResults show that the proposed approach effectively prioritizes testing facilities that most likely have high utilization.Research limitations/implicationsThe findings in this study would be significant to the literature in a number of respects. For one, it reveals results that would stimulate the interest among scholars in a wide variety of disciplines such as management, data mining, policymaking, decision science and epidemiology, among others.Originality/valueThis study differs from previous works in a number of respects, particularly, in that to the best of the authors' knowledge, this is the first study to examine the relative efficiencies of COVID-19 testing facilities.

Prioritization of Supply Chain Capabilities Using the FAHP Technique

With the great challenges that the latest pandemic (COVID-19) has imposed on manufacturing companies, the need to overcome and cope with such situations is becoming crucial. Supply chain resilience is one of the main aspects that enables manufacturers to cope with change and uncertainty;therefore, it is essential to develop the capabilities necessary to do so. This study aimed to ensure supply chain resilience in light of the COVID-19 pandemic through prioritizing main supply chain capabilities. After surveying (30) experts in supply chain from leading manufacturing companies in Jordan, a Fuzzy Analytic Hierarchy Process (FAHP) analysis was conducted to prioritize main supply chain capabilities that were derived from the related literature. The results of this study showed that proactive capabilities, followed by reactive capabilities, were the most dominant capabilities that could ensure supply chain resilience, while efficiency-based capabilities were the least significant. Therefore, manufacturing companies should place their focus and emphasis on reacting to this pandemic in a more systematic manner. © 2023 by the authors.

The social security system in health in Colombia and the noncompliance of the principles of universality, solidarity and integrality

The current Pandemic has exposed the crisis in the health system and the urgency that, once the trance is over, structural problems be addressed to increase the quality and opportunity of access to the health system. This article seeks to analyze whether, during the pandemic, the principles of universality, solidarity and integrality, established in the General System of Social Security in Health and that respond to the content of the Political Constitution of 1991, were ignored. For the above, the proposed objectives correspond to the review of the current state of health care and the elements available to the State in the face of the health crisis, to understand the dangers that a global crisis such as Covid-19 can represent for compliance with the principles of the Social Security System in Health in Colombia, as well as the implications in the paradigm shift from a health model as a public policy to a fundamental right, to finally analyze the right to health of the elderly in the face of the pandemic. The social security health system in Colombia was not designed to deal with crises like the current one, and fortunately there have not been the mortality rates that were expected. However, it is necessary to rethink the rights around the health care of the vulnerable population in catastrophic situations, since the government risk of violating the enjoyment of life in decent conditions is latent, given the potential prioritization of intermediate care. and intensive in health centers in the face of the crisis generated by the Coronavirus pandemic. © 2022, Corporacion Universitaria Republicana. All rights reserved.

A Matrix Factorization-Based Drug-Virus Link Prediction Method for SARS-CoV-2 Drug Prioritization

Matrix factorization (MF) has been widely used in drug discovery for link prediction, which aims to reveal new drug-target links by integrating drug-drug and target-target similarity information with a drug-target interaction matrix. The MF method is based on the assumption that similar drugs share similar targets and vice versa. However, one major disadvantage is that only one similarity metric is used in MF models, which is not enough to represent the similarity between drugs or targets. In this work, we develop a similarity fusion enhanced MF model to incorporate different types of similarity for novel drug-target link prediction. We apply the proposed model on a drug-virus association dataset for anti-COVID drug prioritization, and compare the performance with other existing MF models developed for COVID. The results show that the similarity fusion method can provide more useful information for drug-drug and virus-virus similarity and hence improve the performance of MF models. The top 10 drugs as prioritized by our model are provided, together with supporting evidence from literature. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Decision-making, medical resource allocation and the elderly in the context of COVID-19: An anthropology of and for bioethics

In the context of the COVID-19 pandemic, decision-making practices related to the allocation of medical resources and the treatment of the elderly inform us about the ethics present in the health care setting and at the societal level. The comparison between decision-making in the daily context and the particularity of a pandemic ethics highlights the transition between a non-pandemic ethics and a "pandethics”. The public health ethics approach, particularly utilitarian, has been brought forward in a prominent way in the ethical debates and dilemmas surrounding resource allocation and prioritization. By raising the oppositions and issues associated with age rationing discourses and choices, the question of the treatment of the elderly in the context of COVID-19, and the ageism experienced in this context, emerges. At the same time, difficult ethical decisions and choices are intertwined with the caregiver's duty to care, and therefore the possibility of moral injury. Conflict emerges between ethical decision-making practices and the caregiver's personal or professional values, as the balance between various duties is upset. Alternative approaches and ethics are thus put forward in light of the situations experienced, particularly in the context of long-term care. The thesis developed here aims to support the added value of anthropology to decision-making processes and its more formal integration into well-known approaches in bioethics. Using an anthropological perspective, I conclude by exploring avenues of reflection associated with the ethics of discussion, vulnerability, feminism, or care as other ways of approaching decision-making in the context of a pandemic, at a time when ethical and social reflection is essential. © 2022 University of Montreal. All rights reserved.

Prise de décision, répartition des ressources médicales et personnes âgées en contexte de COVID-19: une anthropologie de et pour la bioéthique

In the context of the COVID-19 pandemic, decision-making practices related to the allocation of medical resources and the treatment of the elderly inform us about the ethics present in the health care setting and at the societal level. The comparison between decision-making in the daily context and the particularity of a pandemic ethics highlights the transition between a non-pandemic ethics and a "pandethics”. The public health ethics approach, particularly utilitarian, has been brought forward in a prominent way in the ethical debates and dilemmas surrounding resource allocation and prioritization. By raising the oppositions and issues associated with age rationing discourses and choices, the question of the treatment of the elderly in the context of COVID-19, and the ageism experienced in this context, emerges. At the same time, difficult ethical decisions and choices are intertwined with the caregiver's duty to care, and therefore the possibility of moral injury. Conflict emerges between ethical decision-making practices and the caregiver's personal or professional values, as the balance between various duties is upset. Alternative approaches and ethics are thus put forward in light of the situations experienced, particularly in the context of long-term care. The thesis developed here aims to support the added value of anthropology to decision-making processes and its more formal integration into well-known approaches in bioethics. Using an anthropological perspective, I conclude by exploring avenues of reflection associated with the ethics of discussion, vulnerability, feminism, or care as other ways of approaching decision-making in the context of a pandemic, at a time when ethical and social reflection is essential. © 2022 University of Montreal. All rights reserved.

Combining PCA-AHP Combination Weighting to Prioritize Design Elements of Intelligent Wearable Masks

Intelligent wearable masks are gaining increasing interest due to COVID-19 and the problems and limitations of existing masks. This paper prioritizes the design elements of personal protective equipment-intelligent wearable masks from the perspective of the product design domain. Using principal component analysis (PCA), the principal components of the design elements were selected first in this paper. Using the combined weights (PCA-AHP) method, the intelligent wearable masks' prioritized design elements at each level were determined. The highest priority among the primary elements is comfort (0.3422), with the adjustable ear strap (0.1870) receiving the highest priority among the primary elements of comfort. The highest priority in functionality (0.2733) is anti-respiratory droplets/air purification (0.1097), the highest priority in usability (0.1686) is the easy removal and replacement of filters (0.0761), the highest priority in the aesthetic design (0.1192) is styling (0.0509), and the highest priority in material (0.0967) is flexible fabric material (0.0355). Finally, the six prioritized design elements were evaluated using fuzzy comprehensive evaluation (FCE), and overall, 76% of the experts considered them "appropriate” or "very appropriate” and 18% considered them "fair.” Therefore, this study's six most prioritized design elements proposed for intelligent wearable masks can satisfy users' needs. © 2023 by the authors.

Shifting towards Antifragile Critical Infrastructure Systems

Antifragility, which is an evolutionary understanding of resilience, has become a predominant concept in academic and industrial fields as the criticality of vital infrastructures (like healthcare and transportation) has become more flexible and varying due the impact of digitization and adverse circumstances, such as changing the prioritization of industrial services while accelerating IoT (Internet of Things) deployment during the COVID-19 pandemic. The crucial role of antifragility is to enable critical infrastructures to gain from disorder to foster their adaptability to real unexpected environmental changes. Thus, this paper aims to provide a comprehensive survey on the antifragility concept while clarifying the difference with the resilience concept. Moreover, it highlights how the COVID-19 crisis has revealed the fragility of critical infrastructures and unintentionally promoted the antifragility concept. To showcase the main concepts, we adopt the blockchain as an example of an antifragile system. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

Minimizing population health loss due to scarcity in OR capacity: validation of quality of life input.

OBJECTIVES: A previously developed decision model to prioritize surgical procedures in times of scarce surgical capacity used quality of life (QoL) primarily derived from experts in one center. These estimates are key input of the model, and might be more context-dependent than the other input parameters (age, survival). The aim of this study was to validate our model by replicating these QoL estimates. METHODS: The original study estimated QoL of patients in need of commonly performed procedures in live expert-panel meetings. This study replicated this procedure using a web-based Delphi approach in a different hospital. The new QoL scores were compared with the original scores using mixed effects linear regression. The ranking of surgical procedures based on combined QoL values from the validation and original study was compared to the ranking based solely on the original QoL values. RESULTS: The overall mean difference in QoL estimates between the validation study and the original study was - 0.11 (95% CI: -0.12 - -0.10). The model output (DALY/month delay) based on QoL data from both studies was similar to the model output based on the original data only: The Spearman's correlation coefficient between the ranking of all procedures before and after including the new QoL estimates was 0.988. DISCUSSION: Even though the new QoL estimates were systematically lower than the values from the original study, the ranking for urgency based on health loss per unit of time delay of procedures was consistent. This underscores the robustness and generalizability of the decision model for prioritization of surgical procedures.

Ethical challenges in the prioritization of elective care in pandemic settings: On the significance of time-sensitive scoring.

In times of ongoing resource shortages, appropriate evaluation criteria are crucial for the ethical prioritization of medical care. While the use of scoring models as tools for prioritization is widespread, they are barely discussed in the medical-ethical discourse in the context of the COVID-19 pandemic. During this time, the challenge of providing care for patients in need has promoted consequentialist reasoning. In this light, we advocate for the integration of time- and context-sensitive scoring (TCsS) models in prioritization policies that foster treatment opportunities for patients with subacute and chronic conditions. We argue, first, that TCsSs enable a more efficient use of resources, reducing avoidable harm to patients by preventing arbitrary postponement of necessary but nonurgent interventions. Second, we contend that on an interrelational level, TCsSs render decision-making pathways more transparent, which promotes the information requirement of patient autonomy and raises confidence in the resulting prioritization decision. Third, we claim that TCsS contributes to distributive justice by reallocating available resources to the benefit of elective patients. We conclude that TCsSs promote anticipatory measures that extend the timeframe for responsible action into the future. This strengthens patients' ability to exercise their right to healthcare-primarily during times of crisis, but ultimately in the longer term too.

Optimal interventions in networks during a pandemic.

We develop a model of optimal lockdown policy for a social planner who balances population health with short-term wealth accumulation. The unique solution depends on tolerable infection incidence and social network structure. We then use unique data on nursing home networks in the US to calibrate the model and quantify state-level preference for prioritizing health over wealth. We also empirically validate simulation results derived from comparative statics analyses. Our findings suggest that policies that tolerate more virus spread (laissez-faire) increase state GDP growth and COVID-19 deaths in nursing homes. The detrimental effects of laissez-faire policies are more potent for nursing homes that are more peripheral in networks, nursing homes in poorer counties, and nursing homes that operate on a for-profit basis. We also find that US states with Republican governors have a higher tolerable incidence level, but these policies tend to converge with a high death count. Supplementary Information: The online version contains supplementary material available at 10.1007/s00148-022-00916-y.

Multi-Criteria Decision Analysis to Prioritize People for COVID-19 Vaccination When Vaccines Are in Short Supply.

COVID-19 pandemic underscored the need for a rapid tool supporting decision-makers in prioritizing patients in the immediate and overwhelming context of pandemics, where shortages in different healthcare resources are faced. We have proposed Multi-Criteria Decision Analysis (MCDA) to create a system of criteria and weights to prioritize uses of COVID-19 vaccines in groups of people at significantly higher risk of severe COVID-19 disease or death, when vaccines are in short supply, for use in Tunisia. The prioritization criteria and the levels within each criterion were identified based on available COVID-19 evidence with a focus on the criteria selected by Tunisian scientific committees. To determine the weights for the criteria and levels, reflecting their relative importance, a panel of frontline physicians treating COVID-19 were invited to participate in an online survey using 1,000 minds MCDA software (www.1000minds.com) which implements the PAPRIKA (Potentially All Pairwise RanKings of all possible Alternatives) method. Ten criteria and twenty-three levels have been selected for prioritizing the uses of COVID-19 vaccines in groups of people at significantly higher risk of severe disease or death. Among the invited physicians, sixty have completed the survey. The obtained scores were, in decreasing order of importance (mean weights in parentheses, summing to 100%). Obesity (16.2%), Age (12.7%), Chronic pulmonary diseases (10.8%), Chronic cardiovascular conditions (10.3%), Bone marrow or organ transplantation (10.1%), Immunodeficiency or Immunosuppression (9.6%), Diabetes (9%), Renal failure (8.4%), evolutive cancer (6.9%), and high blood pressure (6%). MCDA-based prioritization scoring system comprising explicit criteria and weights provides an adaptable and multicriteria approach that can assist policy-makers to prioritize uses of COVID-19 vaccines.