Decision-Making during the Pandemic: Compassion, Trust, and the Altruistic Paradox

The occurrence of breakthrough infections with SARS-CoV-2 in vaccinated individuals argues against abandoning mitigation efforts such as social distancing. Some public health messages, however, promote vaccination by increasing psychological distress, which interferes with social distancing. Prosocial messages present an alternative approach that may avoid this problem. Accordingly, the present study examined the relation of pandemic mitigation with scores on prosocial personality traits (i.e., altruism, sympathy, and trust) and vaccination intentions. Regression analyses indicated that while vaccination intentions increased significantly with an increase in trust, distancing increased significantly with increases in altruism and sympathy. Because older adults are much more vulnerable to COVID-19 than younger adults, these findings reveal an altruistic paradox, in which older adults, perhaps the most altruistic portion of the population, may be dependent on the altruistic behavior of younger adults, who may be the least altruistic portion. The challenge for public health messaging will be to motivate younger adults to take the consequences of their mitigation decisions for others into account. [ FROM AUTHOR] Copyright of COVID is the property of MDPI 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.)

COVID-19 Conceptual Modeling: Single-Center Cross-Sectional Study.

BACKGROUND: Conceptual models are abstract representations of the real world. They are used to refine medical and nonmedical health care scopes of service. During the COVID-19 pandemic, numerous analytic predictive models were generated aiming to evaluate the impact of implemented policies on mitigating the spread of the virus. The models also aimed to examine the psychosocial factors that might govern the general population's adherence to these policies and to identify factors that could affect COVID-19 vaccine uptake and allocation. The outcomes of these analytic models helped set priorities when vaccines were available and predicted readiness to resume non-COVID-19 health care services. OBJECTIVE: The objective of our research was to implement a descriptive-analytical conceptual model that analyzes the data of all COVID-19-positive cases admitted to our hospital from March 1 to May 31, 2020, the initial wave of the pandemic, the time interval during which local policies and clinical guidelines were constantly updated to mitigate the local effects of COVID-19, minimize mortality, reduce intensive care unit (ICU) admission, and ensure the safety of health care providers. The primary outcome of interest was to identify factors that might affect mortality and ICU admission rates and the impact of the implemented policy on COVID-19 positivity among health care providers. The secondary outcome of interest was to evaluate the sensitivity of the COVID-19 visual score, implemented by the Saudi Arabia Ministry of Health for COVID-19 risk assessment, and CURB-65 (confusion, urea, respiratory rate, blood pressure, and age >65 years) scores in predicting ICU admission or mortality among the study population. METHODS: This was a cross-sectional study. The relevant attributes were constructed based on research findings from the first wave of the pandemic and were electronically retrieved from the hospital database. Analysis of the conceptual model was based on the International Society for Pharmacoeconomics and Outcomes Research guidelines and the Society for Medical Decision-Making. RESULTS: A total of 275 individuals tested positive for COVID-19 within the study design interval. The conceptualization model revealed a low-risk population based on the following attributes: a mean age of 42 (SD 19.2) years; 19% (51/275) of the study population being older adults ≥60 years of age; 80% (220/275) having a CURB-65 score <4; 53% (147/275) having no comorbidities; 5% (13/275) having extreme obesity; and 20% (55/275) having a significant hematological abnormality. The overall rate of ICU admission for the study population was 5% (13/275), and the overall mortality rate was 1.5% (4/275). The multivariate correlation analysis revealed that a high-selectivity approach was adopted, resulting in patients with complex medical problems not being sent to MOH isolation facilities. Furthermore, 5% of health care providers tested positive for COVID-19, none of whom were health care providers allocated to the COVID-19 screening areas, indicating the effectiveness of the policy implemented to ensure the safety of health care providers. CONCLUSIONS: Based on the conceptual model outcome, the selectivity applied in retaining high-risk populations within the hospital might have contributed to the observed low mortality rate, without increasing the risk to attending health care providers.

Venovenous extracorporeal membrane oxygenation and late ductal stenting for infant with transposition of the great arteries, pulmonary stenosis and SARS, -CoV-2 pneumonia

Introduction: The SARS-CoV-2 pandemic has affected medical decision-making in all practice areas, including the pediatric cardiac intensive care unit (CICU), sometimes necessitating the use of innovative management strategies. Venovenous extracorporeal membrane oxygenation (VV-ECMO) and, particularly, late ductal stenting are infrequently applied interventions in the CICU. Here we present a critically ill infant with d-transposition of the great arteries (d-TGA), ventricular septal defect (VSD), pulmonary stenosis (PS), and patent ductus arteriosus (PDA), in which VV-ECMO and late ductal stenting were utilized successfully in the setting of active SARS-CoV-2 infection to treat worsening PS and pulmonary venous desaturation, thereby delaying surgical intervention and its associated risks during active infection. Case Description: A 3 month old male with d-TGA, VSD, and PS, initially managed with a balloon atrial septostomy at birth, was admitted to the CICU after presenting with respiratory distress and hypoxemia. He was found to be SARS-CoV-2 positive, requiring only nasal cannula initially. Admission echocardiogram demonstrated known d-TGA, VSD, severe pulmonary stenosis (peak gradient 95-110mmHg), unrestrictive atrial communication, and preserved systolic function. A tiny, hemodynamically insignificant PDA was also noted. While admitted, the patient exhibited intermittent, severe desaturations requiring escalating respiratory support. He was started on a prostaglandin infusion with aim to promote additional pulmonary blood flow through the PDA, thereby limiting the severity and frequency of desaturations. However, the patient ultimately became severely hypoxemic, despite multiple interventions to improve oxygenation. Echocardiogram at this time demonstrated preserved ventricular function, so the decision was made to escalate to VVECMO therapy. Following ECMO cannulation, the patient's hypoxemia quickly resolved, and he remained hemodynamically stable. Given the persistence of his PDA and the desire to avoid the risks of cardiac surgery in the setting of acute COVID infection, percutaneous intervention to augment pulmonary blood flow was attempted. Despite its diminutive size, his PDA was able to be successfully cannulated and stented the day after ECMO initiation. He was able to be quickly weaned from ECMO support and was decannulated the following day. He was subsequently extubated and ultimately discharged home with planning for definitive surgical intervention underway. Discussion(s): Here we present an interesting case of an infant with d-TGA, VSD, PS, and PDA in which VV-ECMO and PDA stenting were successfully applied to treat acute hypoxemia in the setting of SARS-CoV-2 infection and severe pulmonary stenosis. These therapies may be considered in appropriate patients for whom the risks of cardiac surgery are significant.

Overview: Scott M. Hammer Workshop for New Investigators and Trainees

Over the past four decades, remarkable progress has been made in understanding HIV epidemiology, pathogenesis, treatment, and prevention from the combined efforts of community members, clinicians, investigators, and funding agencies worldwide. Yet more work and new approaches are needed to achieve the ambitious goal of ending the epidemic and ensuring optimal quality of life for those living with HIV. To encourage and stimulate the next generation of investigators, the CROI Program Committee organizes an annual Workshop for New Investigators and Trainees comprised of expert and comprehensible talks to cover current knowledge and controversies in basic, clinical, and public health investigation into HIV and related infections, and to highlight relevant work to be presented over the ensuing days at CROI. This year, the program will begin with a presentation by Dr Stuart Neil on novel aspects of the molecular virology of HIV-1 and SARS-CoV-2. Following this, Dr Guido Silvestri will cover the immune responses against HIV and SARS-CoV-2. Ms Dawn Averitt, an HIV treatment policy advocate and activist will provide a community perspective on the power of community engagement in research. In the following presentation, Dr Monica Gandhi will review novel therapeutic strategies for HIV. Dr Raphael Landovitz will address advances in different strategies for preventing HIV transmission. Dr John Mellors will review advances in preclinical and clinical approaches for functional or sterilizing HIV-1 cure. The workshop will end with an intervention by Dr Rochelle Walensky, who will discuss career opportunities in research and public health. By completing the workshop, attendees will have achieved a head start toward maximizing the knowledge gained and research ideas arising from CROI 2022.

The Embedment of Palliative Care Services on a Neuro-Surgical Unit (CS359)

Outcomes: 1. A better understanding of the benefits of embedded palliative care into a neuro surgery unit at a large academic hospital 2. An understanding of the financial impact related to embedding a palliative care APC into the neuro surgery unit at a large academic hospital Problem: Palliative care needs of patients admitted to neurology ICUs are often unmet. Patients with palliative care needs identified were more likely to die in an ICU setting or be transferred to the floor with comfort measures only. These patients were noted to have a longer length of stay. Because of the known benefits of palliative care, specifically, with this vulnerable population of patients, there was a desire to increase the palliative care presence on the neuro surgical service. Intervention(s): One APC palliative care position specific to the neuro ICU team was created. Responsibilities included symptom management, family support, medical decision making, managing conflicts over care goals, and disposition planning. Outcome(s): Outcomes included involvement in interdisciplinary rounds, increased donor opportunities, and increased billing by 28% in 2021. There was a 46% increase in palliative care consults from 2020 to 2021 and an increase in percentage of DNR/DNI orders obtained during admission from 2020 to 2021. An increase in deaths during hospitalization with active palliative care consults on comfort care was noted. Statistics were collected specific to mortality, ICU LOS, diagnosis, COVID status, social work involvement, as well as spiritual care involvement. Conclusion(s): Patients are seen earlier in their hospitalization and their medical wishes are now widely known and discussed by all interdisciplinary team members. The need for the involvement of the APC in these cases has only solidified with increased exposure to the palliative care team as consults increase. Patients are benefitting from the quality care being provided that now better aligns with their personal medical goals. Implications for nursing: There are many vulnerable patient populations for whom palliative care could be just as impactful;additional research should be completed to investigate further. Palliative care embedded on an ICU improves collaboration and increases exposure and understanding of the intent of palliative care.Copyright © 2023

Quantitative methods for improving medical decision-making

Innovation in causal inference and implementation of electronic health record systems are rapidly transforming medical care. In this dissertation, we present three examples in which use of methods in causal inference and large electronic health record data address existing challenges in medical decision-making. First, we use principles of causal inference to examine the structure of randomized trials of biomarker targets, which have produced divergent results and controversial clinical guidelines for management of hypertension and other chronic diseases. We discuss four key threats to the validity of trials of this design. Second, we use methods in causal inference for adjustment of time-varying confounding to estimate the effect of time-varying treatment strategies for hypertension. We report the results of a study which used longitudinal electronic health record data from a prospective virtual cohort of veterans. Third, we use individual-level electronic health record data to predict the need for critical care resources during surges in COVID-19 cases, to aid hospital administrators with resource allocation in periods of crisis. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Identifying and Examining Ethical Considerations Facing Data Monitoring Committees

The COVID-19 pandemic has exposed numerous unresolved research ethics challenges particularly for Data Monitoring Committees (DMCs). DMCs have worked to ensure the ongoing social value of research as rapid changes occur in health policy and epidemiology and there is substantial pressure to release early findings to the public. Unlike Institutional Review Boards, DMCs are charged with carefully monitoring ongoing research, but with limited ethical guidance and often without representation from all host countries. This article highlights ethical challenges for DMCs and lessons learned from the COVID-19 pandemic. DMCs have long faced high-stakes decisions in clinical trials including whether to continue, modify, or terminate a trial based on emerging trial data. Trial protocols, statistical analysis plans, and data monitoring charters establish principles for DMC decisionmaking;however, there has not been a great deal of systematic examination of the ethical issues faced by DMCs. For example, which ethical considerations should be addressed by DMCs as opposed to Institutional Review Boards or researchers is often unclear. Formal guidance rarely addresses whether DMCs should monitor the representativeness of trial participants as compared with the target population for the intervention. Furthermore, post-trial issues have received limited attention. Should DMCs ensure the accuracy of press releases and manuscripts detailing study findings? How should DMCs determine when to unblind participants after a study is over if it is relevant for their medical decision-making? In this presentation, we will report preliminary results of a qualitative study of DMC members (i.e. statisticians, clinicians, and ethicists). We will highlight persistent controversies, the range of roles DMCs are expected to play in monitoring clinical trials, and variation in formal guidance about the ethical obligations of DMCs. We will also examine the question of whether and when ethicists should serve on DMCs. We will conclude by identifying critical ethical issues facing DMCs that warrant further attention.

Evidence-based medicine and physicians' institutional agency in Russian clinical settings

In 1990s' Russia, a wave of internationalization brought an evidence-based medical paradigm to Russian healthcare. Whilst there has been considerable critical commentary on the consequences of adopting this paradigm for medical decision-making, much of this relates to specific contexts in Europe, north America and Australasia, with little research addressing post-Soviet clinical practice. Drawing on semi-structured qualitative interviews with Russian physicians, this article explores the entanglements between the introduction of evidence-based medicine (EBM) in the country and the transformation of post-socialist medical professionalism. I single out physicians' efforts to reconcile the EBM paradigm with organizational constraints as indicative of professionals' ground-level agency. I define the following components of such agency: (1) selective application of guidelines and use of foreign clinical recommendations;(2) establishing local professional solidarity;(3) developing relationships based on personalized trust with the patients. The study employs two sets of data (gathered in 2018 and in 2020) to trace the EBM-related agency of medical professionals both before and during COVID-19 pandemic. By offering analytical insights from post-socialist healthcare, where doctors' discretion has historically been limited by excessive state control, the article contributes to academic debate on medical professionals' autonomy and agency in the era of EBM-related standardization.Copyright © 2023 Informa UK Limited, trading as Taylor & Francis Group.

SARS-CoV-2 Mortality Risk Prediction Using Machine Learning Algorithms to Aid Medical Decision-Making

The coronavirus has affected the world in every possible aspect such as loss of economy, infrastructure, and moreover human life. In the era of growing technology, artificial intelligence and machine learning can help find a way in reducing mortality so, we have developed a model which predict the mortality risk in patients infected by COVID-19. We used the dataset of 146 countries which consists of laboratory samples of COVID-19 cases. This study presents a model which will assist hospitals in determining who must be given priority for treatment when the system is overburdened. As a result, the accuracy of the mortality rate prediction demonstrated is 91.26%. We evaluated machine learning algorithms namely decision tree, support vector machine, random forest, logistic regression, and K-nearest neighbor for prediction. In this study, the most relevant features and alarming symptoms were identified. To evaluate the results, different performance measures were used on the model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Virtual sarcoma disease multidisciplinary team: A jump into the future?

Background: Sarcomas are rare cancers with complex diagnoses, requiring multimodal management in referral Centers with a dedicated Disease Multidisciplinary Team (DMT). COVID-19 pandemic imposed a sudden revolution in care management leading to activation of digital health tools such as virtual DMT meetings. We investigated how transition from face-to-face (FTF) to virtual meetings (VM) impacted clinical practice in a Sarcoma referral Center in Italy. Method(s): An online survey was administered to the participants of the Sarcoma DMT meetings held between 2020 and 2022 at Regina Elena National Cancer Institute of Rome. The survey consisted of 40 questions inquiring the experience of professionals in DMT meetings, opinions about FTF/VM, future perspectives. Moreover, we retrospectively analyzed the registries of DMT meetings held from March 9th 2019 to March 8th 2022 and we defined three 12-months periods by using the date of National lockdown beginning in Italy (March 9th 2020) as cutoff: a pre-covid, a covid, a post-covid phase. Result(s): A total of 22 healthcare professionals answered the survey: 18 medical doctors, 1 psychologist and 3 data/case managers. The most important changes in VM were better quality of clinical approach and research (22%), technological innovation (50%), and better logistical setting (95%). Between participants, 90% were highly/moderately satisfied with the depth of discussion, 100% declared that attendance was similar (68%) or increased (32%) compared to FTF, 86% thought that decision-making process was not affected, and 95% were still able to interact adequately and access relevant patients' data. 91% thought that VM could be approved hereafter and 100% that would facilitate DMT expansion globally. The median participation rate in Institutional Sarcoma DMT meetings was 58% (95% confidence interval (CI) 55%-60%), 62% (95% CI 48%-71%), and 64% (95% CI 61%-68%) respectively in the pre-covid, covid, and post-covid periods (p 0.0159). Compared to the re-discussed cases, the new ones rose from 30.1% in the pre-covid to 37.9% in the covid and 42.3% in the post-covid period (p<0.0001). Conclusion(s): VM guarantees a great standard of effectiveness in health assistance and should be a suitable or additional tool for the management of patients in clinical practice. Legal entity responsible for the study: IRCCS Regina Elena National Cancer Institute (IRE). Funding(s): Has not received any funding. Disclosure: All authors have declared no conflicts of interest.Copyright © 2023 European Society for Medical Oncology

metaCOVID: A web-application for living meta-analyses of COVID-19 trials.

Outputs from living evidence syntheses projects have been used widely during the pandemic by guideline developers to form evidence-based recommendations. However, the needs of different stakeholders cannot be accommodated by solely providing pre-defined non amendable numerical summaries. Stakeholders also need to understand the data and perform their own exploratory analyses. This requires resources, time, statistical expertise, software knowledge as well as relevant clinical expertise to avoid spurious conclusions. To assist them, we created the metaCOVID application which, based on automation processes, facilitates the fast exploration of the data and the conduct of sub-analyses tailored to end-users needs. metaCOVID has been created in R and is freely available as an R-Shiny application. Based on the COVID-NMA platform (https://covid-nma.com/) the application conducts living meta-analyses of randomized controlled trials related to COVID-19 treatments and vaccines for several outcomes. Several options are available for subgroup and sensitivity analyses. The results are presented in downloadable forest plots. We illustrate metaCOVID through three examples involving well-known treatments and vaccines for COVID-19. The application is freely available from https://covid-nma.com/metacovid/.

Frailty goes viral: a critical discourse analysis of COVID-19 national clinical guidelines in the United Kingdom

This paper explores how the rationing of medical care for older people by frailty score was justified and operationalised in the UK during the COVID-19 pandemic. COVID-19 was expected to overwhelm the National Health Service (NHS) in the UK. In March 2020, the National Institute for Health and Care Excellence (NICE) published the ‘COVID-19 rapid guideline: critical care in adults', which advised that clinicians use the Clinical Frailty Score (CFS) to inform decisions about which patients over the age of 65 should be offered ventilatory support. We present a Foucauldian Critical Discourse Analysis of this guidance and the supporting online resources. Analysis shows how the guidance merchandises the CFS as a quick and easy-to-use technology that reduces social and physical complexity into a clinical score. This stratifies older people by frailty score and permits the allocation of resources along these lines. We show how this is justified through epidemiological discourses of risk, which are merged with the language of individual mortality prediction. We discuss the proceduralisation of the CFS alongside a growing body of research that problematises its application in resource allocation. We argue that the pandemic has increased the use of the concept of frailty and that this effectively obfuscates the concept's limitations and ambiguities;the ageism implicit in the response to COVID-19 in the UK;and the relative resource scarcity facing the UK's NHS.

Prediction of COVID-19 & Pneumonia using Machine Learning & Deep Learning Model

As a result of the COVID-19 pandemic, medical examinations (RTPCR, X-ray, CT-Scan, etc.) may be required to make a medical decision. COVID-19's SARS-CoV-2 virus infects and spreads in the lungs, which can be easily recognized by chest X-rays or CT scans. However, along with COVID-19 instances, cases of another respiratory ailment known as Pneumonia began to rise. As a result, clinicians are having difficulty distinguishing between COVID-19 and Pneumonia. So, more tests were required to identify the condition. After a few days, the COVID-19 SARS-CoV-2 virus multiplied in the lungs, causing pneumonia and COVID-19 named Novel Corona virus infected Pneumonia. We employ Machine Learning and Deep Learning models to predict diseases such as COVID-19 Positive, COVID-19 Negative, and Viral Pneumonia in this research. A dataset of data is used in a Machine Learning model. A dataset of 120 images was used in the Machine Learning model. By extracting eight statistical elements from an image texture, we calculated accuracy. Adaboost, Decision Tree & Naive Bayes have overall accuracy of 88.46%, 86.4% and 80%, respectively. When we compared the algorithms, Adaboost algorithm performs the best, with overall accuracy of 88.46%, sensitivity of 84.62%, specificity of 92.31%, F1-score of 88% and Kappa of 0.8277. VGG16 Architecture is used in CNN model for 838 images in Deep Learning model. The model's total accuracy is 99.17 %. © 2022 IEEE.

The political polarization of COVID-19 treatments among physicians and laypeople in the United States.

In the United States, liberals and conservatives disagree about facts. To what extent does expertise attenuate these disagreements? To study this question, we compare the polarization of beliefs about COVID-19 treatments among laypeople and critical care physicians. We find that political ideology predicts both groups' beliefs about a range of COVID-19 treatments. These associations persist after controlling for a rich set of covariates, including local politics. We study two potential explanations: a) that partisans are exposed to different information and b) that they interpret the same information in different ways, finding evidence for both. Polarization is driven by preferences for partisan cable news but not by exposure to scientific research. Using a set of embedded experiments, we demonstrate that partisans perceive scientific evidence differently when it pertains to a politicized treatment (ivermectin), relative to when the treatment is not identified. These results highlight the extent to which political ideology is increasingly relevant for understanding beliefs, even among expert decision makers such as physicians.

Essential Communication Aspects in Telemedicine: Opportunities to Strengthen Digital Health Services in Indonesia Post- Pandemic Era

This study reveals essential aspects of communication done by medical practitioners through telemedicine during the Covid-19 pandemic and their relation to increasing the quality of telemedicine services in Indonesia post-pandemic. The study used a qualitative approach with 22 doctors participating in the Bandung City area, Indonesia. Data collection uses semi-structured interviews, which are then analyzed thematically to reveal essential aspects of communication through telemedicine. The results of the analysis consist of several things, namely: (1) personal aspects;including knowledge, psychological conditions, and socio-cultural background (2) process aspects;including interactivity, time context, spatial context, and sensory context;and also (3) technical aspects covering information systems, quality of communication media, availability of health technology, and work coordination systems. Each aspect relates to instrumental and socioemotional information that plays a role in medical decision-making. The results also suggest that to support the usage of telemedicine services, the literacy of its users needs to enhance. Sensitivity to psychological and socio-cultural variations is also required to communicate effectively through telemedicine. Strengthening the technical aspect of telemedicine services, such as integrating information systems, increasing communication media quality, and providing access to health technology instruments, is also essential to improve the completeness and clarity of information, doctor-patient relationships, and confidence that meet the expectations of all of the users in telemedicine services. Copyright © 2022, Anka Publishers. All rights reserved.

S1 Guideline for prioritisation in gastrointestinal cancer care -scarcity of ressources in the pandemic context

Background: The COVID-19 pandemic has led to deviations in all sectors of cancer care. We present multidisciplinarily approved recommendations for ethically and empirically based prioritisation of procedures in times of scarce resources for patients with colorectal and pancreatic cancer. Method(s): The CancerCOVID consortium conducted qualitative and quantitative studies on ethical challenges and psychosocial stress of patients and health care professionals in cancer care. For empirical analyses we obtained data from AOK Plus, the main health insurance in Saxony, AIO (Arbeitsgemeinschaft internistische Onkologie) cancer centers, the institute of Pathology Bochum, the ColoPredict Registry and data of outpatient care from the BNHO (Berufsverband der Hamatologen und Onkologen) and Onkotrakt AG. A selective literature review of international data and guidelines focussing on the effects of the pandemic on cancer care and allocation of resources was conducted. Structured group discussions on justified criteria for prioritisation were held with experts from oncology, ethics, law and health research. Recommendations for prioritisation were formulated as S1 guideline with approval of 9 AWMF Medical Societies, 22 multidisciplinary experts and patient representatives. Result(s): The main principle for decisions on prioritisation in times of scarce resources is the minimisation of individual and aggregated harm. In case of relevant risk of harm from a possible low priority classification or postponement prioritization decisions should be made individually for the respective patients according to the multiple-eyes principle. Decision making should involve different disciplines and professions depending on local infrastructure. We concretised recommendations for 5 areas in cancer care. Conclusion(s): Guidelines based on a broad multidisciplinary consensus can give ethically and empirically based support in medical decision making when resources are scarce. This can provide relief for decision-makers and facilitate transparency and trust of patients and population.

Automated Diagnostic Radiographs of COVID-19 Based on Deep Learning Method

The COVID-19 outbreak has had a serious impact on Taiwan's health care system. Deep Learning is an effective technology to help doctors make the most appropriate medical decisions for every patient in this crisis. In this study, we select four state-of-the-art Deep Learning-Xception, MobileNetv2, DenseNet169, and DenseNet201. Additionally, Transfer Learning is used for pre-training them before four models individually classify normal and positive COVID-19 chest X-ray images. Lastly, the best results reached 98.58% accuracy, 98.58% precision, and 98.42% recall. © 2022 IEEE.

Testing our way out of pandemics

Objectives: In the face of pandemics, a viable global strategy, beyond relying on the fast discovery of a vaccine or a cure, is needed. We study quantitatively the feasibility and effectiveness of mass testing to contain an epidemic. We also explore the implications of various smart testing strategies to decrease the needed testing rates. Method(s): We use a modified SIR model with testing and extend the model to incorporate mobility patterns in a densely populated area. Result(s): For a pandemic like COVID-19, model simulations show that the rate of testing needed to squash the curve within a month varies between 20-30 percent of the population randomly tested daily to less than 5 percent, combining periodic and group testing. We also show that mobility restrictions can enhance the efficacy of testing. Scale could be as important as accuracy in testing, implying that an epidemiological rather than clinical approach for the approval of tests is needed. The estimated cost of testing is dwarfed by its return, mitigating the economic fallout of the pandemic. Conclusion(s): Without a vaccine or a cure, mass testing is the only viable and less costly strategy to indefinitely "squash the curve" while allowing for major economic activities to resume. Planning and executing a testing strategy is necessary and urgent as an insurance policy against future pandemics. It should be considered as an investment to build a testing and isolation infrastructure, which should be maintained as part of the pandemic preparedness. Copyright © 2022