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The deadly coronavirus disease 2019 (COVID-19) has recently affected each corner of the world. Many governments of different countries have imposed strict measures in order to reduce the severity of the infection. In this present paper, we will study a mathematical model describing COVID-19 dynamics taking into account the government action and the individuals reaction. To this end, we will suggest a system of seven fractional deferential equations (FDEs) that describe the interaction between the classical susceptible, exposed, infectious, and removed (SEIR) individuals along with the government action and individual reaction involvement. Both human-to-human and zoonotic transmissions are considered in the model. The well-posedness of the FDEs model is established in terms of existence, positivity, and boundedness. The basic reproduction number (BRN) is found via the new generation matrix method. Different numerical simulations were carried out by taking into account real reported data from Wuhan, China. It was shown that the governmental action and the individuals' risk awareness reduce effectively the infection spread. Moreover, it was established that with the fractional derivative, the infection converges more quickly to its steady state.

This study adopts a resilience perspective to explain how companies managed to contribute innovative solutions to fight the COVID-19 crisis. We studied how five companies operating in different industries (three in automotive, one in printing, and one in rubber and plastic products manufacturing) managed to reorganize activities and employ their R&D and innovation capabilities to enhance their resilience. Simultaneously, they increased the health system’s capacity to cope with the outbreak. Through a qualitative inductive study, based on interviews with company managers, we found that the firms mobilized their resources and capabilities to expand their ability to adapt and cope with adversity at the organizational level. In addition, moved by the sensitivity to the extreme context and a perceived sense of urgency, the firms deployed the same endowments to strengthen the community’s response to a crisis. Our study shows that an organization can directly and positively foster the broader social system’s resilience. This study contributes to the innovation literature by identifying innovation capabilities as fundamental antecedents of resilience building for organizational response, paving the way for strengthening the link between resilience and innovation.

Background Adherence to ADHD medication is a complex phenomenon as the decision to adhere is influenced by a range of factors. To design tailored interventions to promote adherence, it is important to understand the factors that influence adherence in the context of its three phases: initiation, implementation and discontinuation. Objective The objective of this study was to explore the phase‐specific factors that influence adherence to medication in adults who have a diagnosis of ADHD. Methods Three focus groups (FGs) were conducted with twenty adults with ADHD in different metropolitan areas of Sydney, Australia. FGs were transcribed verbatim and thematically analysed. Results Participants’ decision to initiate medication (the initiation phase) was influenced by their perceived needs (desire to improve academic and social functioning) and concerns (fear of side‐effects) about medication following a similar process as defined by the Necessity‐Concerns Framework (NCF). The balance between benefits of medication (needs) and side‐effects (concerns) continued to determine participants’ daily medication‐taking (the implementation phase) and persistence (or discontinuation) with their medication. Forgetfulness and stigma were reported as concerns negatively impacting the implementation phase, while medication cost and dependence influenced the discontinuation phase of adherence. Conclusions Adults’ decision to initiate, continue or discontinue medication is influenced by a range of factors; some are unique to each phase while some are common across the phases. Participants balanced the needs for the medication against their concerns in determining whether to adhere to medication at each phase. It appears that the NCF has applicability when decision making about medication is explored at the three phases of adherence.

This paper explores the methodological aspects of a user‐led study investigating mental health service user experiences of targeted violence and abuse (often called 'hate crime'). 'Keeping Control' was a 16‐month qualitative study, undertaken in the context of adult safeguarding reforms in England. By collecting data on service user concepts and experiences, the research sought to address a gap in research and practice knowledge relating to targeted violence, abuse and hostility against people with mental health problems. In this paper, we discuss the significance of the design and methodology used for this study, with a particular focus on the interviews with service users. The research was both user‐led and carried out in collaboration with practitioners and academics, a form of research co‐production. Our aim is to inform researchers, practitioners and policymakers about the value of user leadership in co‐productive research with practitioners, particularly for a highly sensitive and potentially distressing topic.

The novel coronavirus has created one of the biggest social and economic challenges in recent decades. Since a critical issue in overcoming a large‐scale pandemic involves finding effective treatments for the disease, there is typically urgent pressure on the health‐care sector to develop innovations to combat the pandemic. Recently, scholars have argued that repurposing – that is, reusing an existing innovation in a different context – allows for such rapid innovation responses and can reduce costs, as the groundwork has already been laid. In this paper, we compare these benefits with the considerable disadvantages associated with innovation repurposing, including lowered barriers to entry, which can lead to declining average quality and duplicate work. Using data on 2,456 COVID‐19‐related clinical trials initiated between December 2019 and July 2020, we find that merely one‐third of the trials actually investigated drugs or vaccines, whereas the rest focused on diagnostics and crisis management issues. In the trials concerning drug testing, we find that drug repurposing is a predominant innovation strategy, but many trials tested the same (combination of) drugs. This indicates an inefficient use of resources and reductions in the average variety and novelty of clinical trials. Furthermore, the small percentage of biopharmaceutical firms involved in the search for COVID‐19 treatments raises the question of whether firms may have insufficient incentives to redirect innovation efforts to respond to the pandemic. Our paper contributes to crisis management research, the nascent debate on COVID‐19, and the emerging literature on innovation repurposing.

The preeminent target of present study is to reveal the speed characteristic of ongoing outbreak COVID-19 due to novel coronavirus. On January 2020, the novel coronavirus infection (COVID-19) detected in India, and the total statistic of cases continuously increased to 7 128 268 cases including 109 285 deceases to October 2020, where 860 601 cases are active in India. In this study, we use the Hermite wavelets basis in order to solve the COVID-19 model with time- arbitrary Caputo derivative. The discussed framework is based upon Hermite wavelets. The operational matrix incorporated with the collocation scheme is used in order to transform arbitrary-order problem into algebraic equations. The corrector scheme is also used for solving the COVID-19 model for distinct value of arbitrary order. Also, authors have investigated the various behaviors of the arbitrary-order COVID-19 system and procured developments are matched with exiting developments by various techniques. The various illustrations of susceptible, exposed, infected, and recovered individuals are given for its behaviors at the various value of fractional order. In addition, the proposed model has been also supported by some numerical simulations and wavelet-based results.

BackgroundHistorically, safety mental health research has tended to focus on risks of homicide, suicide and deaths. Although wider safety issues are now recognized in regards to mental health services, the safety of mental health transitions, a key research and policy priority according to World Health Organisation, has not been explored.ObjectiveThe purpose of this study was to investigate perceptions of safety in mental health transitions (hospital to community) amongst five stakeholder groups.Design and settingAn online, international cross‐sectional, open‐ended questionnaire.ParticipantsThere were five stakeholder participant groups: service users; families/carers; mental health‐care professionals; researchers; and end users of research.ResultsNinety‐three participants from 12 different countries responded. Three overarching themes emerged: ‘individual/clinical’, ‘systems/services’ and ‘human, behavioural and social’ elements of safe mental health transitions. Whilst there was a great focus on clinical elements from researchers and healthcare professionals, service users and carers considered safety in terms of human, behavioural and social elements of transitional safety (ie loneliness, emotional readiness for discharge) and systems/services (ie inter‐professional communication).DiscussionSafety in mental health‐care transitions is perceived differently by service users and families compared to healthcare professionals and researchers. Traditional safety indicators for care transitions such as suicide, self‐harm and risk of adverse drug events are raised as important. However, service users and families in particular have a much wider perception of transitions safety.ConclusionFuture quality and safety research and policy should consider including a service user voice and consider integration of psychosocial elements in discharge interventions.

Innovation systems have seen diverse actors attempting to tame the Covid‐19 crisis, under varying degrees of government direction. Largely neglected in scholarly and public attention, however, are ‘bottom‐up’ solutions arising from the periphery of innovation systems. Drawing on inductive case research on a fringe doctor who invented the idea of the drive‐through testing system, and two university student teams that developed coronavirus applications, this study examines how peripheral actors generate innovative, bottom‐up solutions at speed in a time of crisis. Our findings reveal that, in a crisis situation, bottom‐up solutions transpire on the basis of three innovation drivers: (a) peripheral status, expediting the commence of innovation activities; (b) interdisciplinary collaboration, enabling access to a greater spectrum of knowledge and perspectives; and (c) prior knowledge, prescribing the direction of solution generation. We also identify that system intermediaries support the innovation activities of peripheral actors, thereby helping bottom‐up solutions to become more customer facing. Such functions of intermediaries include demand articulation, technical assistance, and promulgation of generated solutions. Our findings offer theoretical implications for the literature on innovation in a time of crisis and practical implications for governments and organizations preparing themselves for the potential second wave of coronavirus emergencies, or even a completely new form of future crisis.

Background Individual patient data meta-analyses (IPD-MA) are regarded as the gold standard for systematic reviews, which also applies to systematic reviews of diagnostic test accuracy (DTA) studies. An increasing number of DTA systematic reviews with IPD-MA have been published in recent years, but there is much variation in how these IPD-MA were performed. A number of existing methods were found, but there is no consensus as to which methods are preferred as the standard methods for statistical analysis in DTA IPD-MA. Objectives To create a web-based tool which integrates recommended statistical analyses for DTA IPD-MA, and allows researchers to analyse the data and visualize the results with interactive plots. Methods A systematic methodological review was performed to identify statistical analyses and data visualization methods used in DTA IPD-MA. Methods were evaluated by the authors and recommended analyses were integrated into the IPDmada tool which is freely available online with the user interface developed with R Shiny package. Results IPDmada allows users to upload their own data, perform the meta-analysis with both continuous and dichotomized tests, and incorporate individual level covariate-adjusted analysis. All tables and figures can be exported as .csv or .pdf files. A hypothetical dataset was used to illustrate the application of IPDmada. Conclusions IPDmada will be very helpful to researchers doing DTA IPD-MA, since it not only facilitates the statistical analysis but also provides a standard framework. The introduction of IPDmada will harmonize the methods used in DTA IPD-MA and ensure the quality of such analyses. Highlights IPDmada is a newly developed web-based tool for performing statistical analysis of individual patient data meta-analysis of diagnostic accuracy and visualizing the results. The tool is freely available to all the researchers, and requiring no installation of statistical software/packages. The tool has an user-friendly interface, and allows meta-analysis on both dichotomized and continuous test results. Researchers can easily use this tool to investigate the threshold effect and covariate effect on the summary accuracy. The introduction and implementation of IPDmada will serve as a useful tool for DTA IPD-MA and increase the quality of such studies.

Background Psychiatric rehospitalization is a complex phenomenon in need of more person-centred approaches. The current paper aimed to explore how community-based actions and daily life influence mental health and rehospitalization. Design, setting and participants The qualitative study included focus group data from six European countries including 59 participants. Data were thematically analysed following an inductive approach deriving themes and subthemes in relation to facilitators and barriers to mental health. Results Barriers consisted of subthemes (financial difficulty, challenging family circumstances and stigma), and facilitators consisted of three subthemes (complementing services, signposting and recovery). The recovery subtheme consisted of a further five categories (family and friends, work and recreation, hope, using mental health experience and meaning). Discussion Barriers to mental health largely related to social determinants of mental health, which may also have implications for psychiatric rehospitalization. Facilitators included community-based actions and aspects of daily life with ties to personal recovery. By articulating the value of these facilitators, we highlight benefits of a person-centred and recovery-focused approach also within the context of psychiatric rehospitalization. Conclusions This paper portrays how person-centred approaches and day-to-day community actions may impact psychiatric rehospitalization via barriers and facilitators, acknowledging the social determinants of mental health and personal recovery. Patient or public contribution The current study included participants with experience of psychiatric rehospitalization from six different European countries. Furthermore, transcripts were read by several of the focus group participants, and a service user representative participated in the entire research process in the original study.

Evidence-based decision-making is predicated on the ability of users to find and comprehend results from systematic review. Evidence producers have an obligation to support evidence users in this process. The Agency for Healthcare Research and Quality (AHRQ) Evidence-based Practice Center (EPC) program-a producer of rigorous and comprehensive systematic reviews for two decades-has set a gold standard for reliability in health evidence reviews in the United States. It has recently begun a program of active support for evidence dissemination and uptake beyond mere publication of lengthy reports. This Brief Methods Note critiques the current paper-based format for systematic reviews and describes the development of a next generation (NxGen) AHRQ EPC Effective Health Care website. This redesigned platform will allow end-users of all types to find and share the evidence they need through data visualizations and other interactive displays. Several design principles guided the development of NxGen to make systematic review findings more accessible, customizable, adaptable, interactive, and shareable. NxGen will include visualizations for report results that are expressed as meta-analyses as well as those with narrative syntheses, through forest or bubble plots, respectively. Visual and interactive evidence heat maps are also planned. The NxGen version of the Effective Health Care website is planned to go live in the latter half of 2020 or early 2021.

Introduction Interrupted Time Series (ITS) studies may be used to assess the impact of an interruption, such as an intervention or exposure. The data from such studies are particularly amenable to visual display and, when clearly depicted, can readily show the short‐ and long‐term impact of an interruption. Further, well‐constructed graphs allow data to be extracted using digitizing software, which can facilitate their inclusion in systematic reviews and meta‐analyses. Aim We provide recommendations for graphing ITS data, examine the properties of plots presented in ITS studies, and provide examples employing our recommendations. Methods and results Graphing recommendations from seminal data visualization resources were adapted for use with ITS studies. The adapted recommendations cover plotting of data points, trend lines, interruptions, additional lines and general graph components. We assessed whether 217 graphs from recently published (2013‐2017) ITS studies met our recommendations and found that 130 graphs (60%) had clearly distinct data points, 100 (46%) had trend lines, and 161 (74%) had a clearly defined interruption. Accurate data extraction (requiring distinct points that align with axis tick marks and labels that allow the points to be interpreted) was possible in only 72 (33%) graphs. Conclusion We found that many ITS graphs did not meet our recommendations and could be improved with simple changes. Our proposed recommendations aim to achieve greater standardization and improvement in the display of ITS data, and facilitate re‐use of the data in systematic reviews and meta‐analyses., Highlights Application of data visualization recommendations can improve quality of interrupted time series graphs.Well‐designed graphs accurately depict time series data, any impact of the interruption, and the results of the analysis.Well‐designed graphs facilitate data extraction for use in systematic reviews and reproducibility.An assessment of graphs included in interrupted time series studies (published between 2013 and 2017) found that graphs often do not meet core graphing recommendations.

Background People with SMI have often been excluded in advocacy efforts focused on physical health, health care and health and social policy. Objective Following a Photovoice project focused on barriers to healthy eating and physical activity in urban neighbourhoods, participant‐researchers were invited to present their insights in community advocacy settings. The purpose of this study was to explore the feasibility and participant–researchers’ experience of these community advocacy activities. Design We held four focus groups with the eight participant‐researchers after each community advocacy activity to explore their experience with public speaking, presenting their experiences and advocating. Setting and Participants People with serious mental illness who were overweight/obese living in supportive housing. Analysis approach Qualitative analysis of the focus group transcripts, using a modified grounded theory approach followed by structured coding focused on empowerment, participation and non‐discrimination. Results Participant‐researchers gave three oral presentations of their photographs at a variety of community‐based programmes and settings and participated in a rally to advocate for SNAP benefits. Two themes emerged from analysis: (a) Empowerment (the level of choice, influence and control that users of mental health services can exercise over events in their lives) and (b) Barriers to Empowerment (obstacles to participation and well‐being). Conclusions This evaluation strengthens the evidence that it is feasible for participant‐researchers in Photovoice projects to engage in robust advocacy activities, such as presentations and discussions with local policymakers. During focus groups, participant‐researchers demonstrated realistic optimism towards their roles as change agents and influencers in spite of acknowledged systemic barriers.

Objectives To describe and reflect on the methods and influence of involvement of young people with lived experience within a complex evidence synthesis. Study design and setting Linked syntheses of quantitative and qualitative systematic reviews of evidence about interventions to improve the mental health of children and young people (CYP) with long‐term physical conditions (LTCs). Methods Involvement was led by an experienced patient and public involvement in research lead. Young people with long‐term physical conditions and mental health issues were invited to join a study‐specific Children and Young People's Advisory Group (CYPAG). The CYPAG met face to face on four occasions during the project with individuals continuing to contribute to dissemination following report submission. Results Eight young people joined the CYPAG. Their views and experiences informed (a) a systematic review evaluating the effectiveness of interventions intended to improve the mental health of CYP with LTCs, (b) a systematic review exploring the experiences of interventions intended to improve the mental well‐being of CYP with LTCs and (c) an overarching synthesis. The CYPAG greatly contributed to the team's understanding and appreciation of the wider context of the research. The young people found the experience of involvement empowering and felt they would use the knowledge they had gained about the research process in the future. Conclusion Creating an environment that enabled meaningful engagement between the research team and the CYPAG had a beneficial influence on the young people themselves, as well as on the review process and the interpretation, presentation and dissemination of findings.

Background Deeply divided ideological positions challenge collaboration when engaging youth with mental disorders, caregivers and providers in mental health research. The integrative dynamics (ID) approach can restructure relationships and overcome ‘us vs them’ thinking. Objective To assess the extent to which an experience‐based co‐design (EBCD) approach to patient and family engagement in mental health research aligned with ID processes. Methods A retrospective case study of EBCD data in which transitional‐aged youth (n = 12), caregivers (n = 8) and providers (n = 10) co‐designed prototypes to improve transitions from child to adult services. Transcripts from focus groups and a co‐design event, co‐designed prototypes, the resulting model, evaluation interviews and author reflections were coded deductively based on core ID concepts, while allowing for emergent themes. Analysis was based on pattern matching. Triangulation across data sources, research team, and youth and caregiver reflections enhanced rigour. Findings The EBCD focus group discussions of touchpoints in experiences aligned with ID processes of acknowledging the past, by revealing the perceived identity mythos of each group, and allowing expression of and working through emotional pain. These ID processes were briefly revisited in the co‐design event, where the focus was on the remaining ID processes: building cross‐cutting connections and reconfiguring relationships. The staged EBCD approach may facilitate ID, by working within one's own perspective prior to all perspectives working together in co‐design. Conclusion Researchers can augment patient engagement approaches by applying ID principles with staged integration of groups to improve relations in mental health systems, and EBCD shows promise to operationalize this.

Background: There is limited evidence of genuine equal partnership where power is shared with young people with mental health difficulties throughout all research stages, particularly in data collection and analysis.\ud Objective: To describe how our qualitative study, exploring young peoples’ perceptions on the feasibility of using technology to detect mental health deterioration, was co- produced using principles of co-production, whilst reflecting on impact, challenges and recommendations.\ud Methods: Young people with experience of mental health difficulties were appointed and then worked with researchers throughout all research stages. The study was evaluated against the five principles of co-production. Reflections from researchers and young people were collected throughout.\ud Results: Seven young people formed an initial Young People's Advisory Group (YPAG); three became co-researchers. Reflection was key throughout the process. Sharing power became easier and more evident as trust, confidence and mutual re- spect grew over time, particularly after a safe space was established. The safe space was crucial for open discussions, and our WhatsApp group enabled continual com- munication, support and shared decision-making. The resulting co-produced topic guide, coding framework, thematic map, papers and presentations demonstrated significant impact.\ud Conclusions: To our knowledge, this is the first qualitative mental health study to be co-produced using the principles of co-production. Our rigorous assessment can be utilized as an informative document to help others to produce meaningful co- produced future research. Although co-production takes time, it makes significant impact to the research, researchers and co-researchers. Flexible funding for spon- taneous suggestions from co-researchers and more time for interview training is recommended.

Background Patient and public involvement (PPI) is increasingly required in mental health services research. To empower patients to actively address depression, the GET.FEEDBACK.GP study evaluates a patient-targeted feedback intervention after depression screening using the Patient Health Questionnaire (PHQ-9). Objective To refine the patient-targeted feedback from a previous study within a participatory research team (PRT) by conducting workshops to investigate patients' needs and preferences for feedback. To evaluate the process and outcome of PPI. Design Patient and public involvement was carried out on the levels of collaboration and consultation. A PRT of patient partners and researchers planned and conducted three workshops with patients. Patients' needs were investigated using a focus group. Participants prioritized needs, discussed feedback drafts and evaluated two drafts using cognitive debriefings. Researchers of the PRT communicated the results at project level. PPI was evaluated using the Public and Patient Engagement Evaluation Tools (PPEET). Setting and participants A purposeful sampling of N = 12 patients with experiences of depression participated in at least one workshop. Results Relevant content-related needs about feedback (eg no distinction between severe and moderate symptoms), recommendations for action and patient-relevant information were considered. Needs for comprehensible, valuing, nonstigmatizing language and design elements (eg dimensional bar) were implemented. Workshops and PRT were positively evaluated. Discussion and conclusions Patient and public involvement influenced the content, wording and design of the feedback. Strengths include two levels of PPI, methodical diversity and purposeful sampling. Limitations include the lack of inclusion of patients who are unaware of their depression. The evaluated PPI concept can be useful for future studies.

Aim The aim of the study was to assess the influence of the number of hours of daily nursing care for NHPPD in medical departments on missed care and the correlation between NHPPD and in-hospital mortality. Background Patient mortality can be a consequence of missed care as it correlates with the nurse-patient ratio. One of the methods to measure missed care is the Nursing Hours per Patient Day rating. Methods The study sample included 44,809 patients including 971 deaths in 8 wards. The influence of nursing hours, nursing education, and the percentage of patients' classification on in-hospital mortality were evaluated with backward stepwise linear regression. Results One hour added to the average NHPPD in medical departments was related to a decrease in mortality rate by 6.8 per 1,000 patient days and a lower chance for the emergence of unplanned death by 36%. Conclusions The number of NHPPD and the percentage of professional nurses are factors influencing missed care and in-hospital mortality. Implications for nursing management The severe consequences of missed care, that is mortality, and the correlation between in-hospital mortality, nursing education and nursing-patient ratio, which are indicators of care quality, are arguments for maintaining adequate staffing levels to avoid missed care.

Being a grand challenge of global scale, the COVID‐19 pandemic requires collective and collaborative efforts from a variety of actors to enable the expected scientific advancement and technological progress. To achieve such an open innovation approach, several initiatives have been launched in order to leverage potential distributed knowledge sources that go beyond those available to any single organization. A particular tool that has gained some momentum during COVID‐19 times is hackathons, which have been used to unleash the innovation potential of individuals who voluntarily came together, for a relatively short period of time, with the aim to solve specific problems. In this paper, we describe and analyze the case of the hackathon EUvsVirus, led by the European Innovation Council. EUvs Virus was a 3‐day online hackathon to connect civil society, innovators, partners, and investors across Europe and beyond in order to develop innovative solutions to coronavirus‐related challenges. We have identified four dimensions to explore hackathons as a crowdsourcing tool for practicing effective open innovation in the face of COVID‐19: broad scope, participatory architecture, online setting, and community creation. We discuss how these four elements can play a strategic role in the face of grand challenges, which require, as in the case of the COVID‐19 pandemic, both urgent action and long‐term thinking. Our case analysis also suggests the need to look beyond the ‘usual suspects’, through knowledge recombination with atypical resources (e.g., retired experts, graduate students, and the general public). On this basis, we call for a broader perspective on open innovation, to be extended beyond openness across organizational boundaries, and to explore the role of openness at societal level.

We formulate a simple susceptible‐infectious‐recovery (SIR) model to describe the spread of the coronavirus under strict social restrictions. The transmission rate in this model is exponentially decreasing with time. We find a formula for basic reproduction function and estimate the maximum number of daily infected individuals. We fit the model to induced death data in Italy, United States, Germany, France, India, Spain, and China over the period from the first reported death to August 7, 2020. We notice that the model has excellent fit to the disease death data in these countries. We estimate the model's parameters in each of these countries with 95% confidence intervals. We order the strength of social restrictions in these countries using the exponential rate. We estimate the time needed to reduce the basic reproduction function to one unit and use it to order the quality of social restrictions in these countries. The social restriction in China was the strictest and the most effective and in India was the weakest and the least effective. Policy‐makers may apply the Chinese successful social restriction experiment and avoid the Indian unsuccessful one. [ FROM AUTHOR] Copyright of Mathematical Methods in the Applied Sciences is the property of John Wiley & Sons, Inc. 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.)

As the COVID‐19 pandemic spread across the globe in the first quarter of 2020, demand for specialised equipment in hospitals soared. As a result, firms from a variety of sectors repurposed their design and manufacturing to create new products in days. By examining 80 cases of this accelerated innovation, the research investigates how a shared purpose drives change in the innovation process. It applies the lens of exaptation – the discovery of unintended functions for technologies – to explain how product complexity and ecosystem structure affect accelerated innovation in this context. The research extends the application of exaption to manufacturing as well as product design; it identifies a relationship between complexity, exaptation and ecosystems. The research suggests that the ability to exapt design and manufacturing can determine a firm’s ecosystem role. These results lead to implications for theory and for practice, during the response to and recovery from the crisis.

Latinos in the United States represent a disproportionate burden of illness and disease and face barriers to accessing health care and related resources. Culturally tailored, evidence‐based interventions hold promise in addressing many of these challenges. Yet, ensuring patient voice is vital in the successful development and implementation of such interventions. Thus, this paper examines the application of analytic hierarchy process (AHP) to inform the augmentation and implementation of an evidence‐based chronic disease self‐management programme for underserved Latinos living with both minor depression and chronic illness. The process of AHP allows for direct input from the individuals that would utilize such a programme, including afflicted individuals, their family members and the health educators/promotores that would be responsible for implementation. Specifically, 45 participants, including 15 individuals with chronic disease, 15 family members/caregivers and 15 promotores, partook in the Stakeholder Values Questionnaire, which elicited preferences and values regarding major goals, processes and content for the intervention. AHP was employed to analyse pairwise comparison ratings and to determine differences and similarities across stakeholder groups. This analytical technique allowed for the adaptation of the EBI to stakeholders' specific priorities and preferences and facilitated complex decision‐making. Findings not only shed light on similarities and differences between stakeholder groups, but also the magnitude of these priorities and preferences and allowed the intervention to be driven by the participants, themselves. Applying AHP was a unique opportunity to optimize the decision‐making process to inform cultural adaptation of an EBI while considering multiple viewpoints systematically.

Background A new diagnosis of complex post-traumatic stress disorder, CPTSD, has been agreed by the World Health Organization, WHO, and evidence is needed for what psychological treatment might be effective, particularly from those with experience of the disorder. We used a novel participatory approach to explore patient views and simultaneously studied the impact on the patient researchers of the research process itself. In this paper, we report on the latter section of the study how the involvement in research of patients with CPTSD affected their mental health. Symptoms of CPTSD may include emotional dysregulation, feelings of self-worthlessness and difficulties in relationships. Objective The aim of this study section was to explore whether patients' mental health could be promoted through empowering them to participate in research on CPTSD. Design The study had a qualitative, participatory design. The clinician who led the research (first author) held group meetings with patient researchers to explore the impact of the research process. The clinician also kept notes on the process in a reflective log. Setting and participants Six patient researchers participated in research with other patients with lived experience of CPTSD in an NHS outpatient unit in a London hospital. Intervention studied The research process itself was analysed in group meetings with researchers which the clinician recorded and transcribed. Findings Participation in research may promote increased self-confidence and social inclusion for those with CPTSD. Conclusion Involvement in research may be seen as an empowering intervention because patients felt it contributed to recovery.

Delineating the spatial extent and the altitudinal lower limit of mountain permafrost is difficult due to complex topo‐climatic and variable ground thermal conditions within short distances. Little information exists regarding sporadic permafrost existence, its thermal characteristics and its long‐term changes at the eastern margin of the European Alps. To reduce this gap, permafrost monitoring was initiated in 2004 in the Seckauer Tauern mountains, Austria. Research was carried out in the summit region of Mt Hochreichart (2416 m a.s.l.) and at several nearby cirques and valleys, all with rock glaciers. Geomorphic mapping, numerical permafrost modeling, measurements of the bottom temperature of the winter snow cover, continuous ground temperature monitoring, electrical resistivity tomography and optical snow cover monitoring were applied. Results indicate sporadic permafrost occurrence in the summit region with mean annual ground temperatures slightly below 0°C at the surface and −1.4°C at 2.5 m depth. Permafrost lenses also exist in the transition zone between the rock glacier and the talus slope behind attributed to coarse‐grained, blocky material causing additional ground cooling. Thanks to long‐term data, statistically significant trends of atmospheric and ground warming were observed in 2000–2018. Permafrost at this site will presumably disappear within the next few decades.

RATIONALE The wide chemical diversity and complex matrices inherent to metabolomics still pose a challenge to current analytical approaches for metabolite screening. Although dedicated front-end separation techniques combined with high-resolution mass spectrometry set the benchmark from an analytical point of view, the increasing number of samples and sample complexity demand for a compromise in terms of selectivity, sensitivity and high-throughput analyses. METHODS Prior to low-field drift tube ion mobility (IM) separation and quadrupole time-of-flight mass spectrometry (QTOFMS) detection, rapid ultrahigh-performance liquid chromatography separation was used for analysis of different concentration levels of dansylated metabolites present in a yeast cell extract. For identity confirmation of metabolites at the MS2 level, an alternating frame approach was chosen and two different strategies were tested: a data-independent all-ions acquisition and a quadrupole broad band isolation (Q-BBI) directed by IM drift separation. RESULTS For Q-BBI analysis, the broad mass range isolation was successfully optimized in accordance with the distinctive drift time to m/z correlation of the dansyl derivatives. To guarantee comprehensive sampling, a broad mass isolation window of 70 Da was employed. Fragmentation was performed via collision-induced dissociation, applying a collision energy ramp optimized for the dansyl derivatives. Both approaches were studied in terms of linear dynamic range and repeatability employing ethanolic extracts of Pichia pastoris spiked with 1 μM metabolite mixture. Example data obtained for histidine and glycine showed that drift time precision (

Blocking is often used to reduce known variability in designed experiments by collecting together homogeneous experimental units. A common modeling assumption for such experiments is that responses from units within a block are dependent. Accounting for such dependencies in both the design of the experiment and the modeling of the resulting data when the response is not normally distributed can be challenging, particularly in terms of the computation required to find an optimal design. The application of copulas and marginal modeling provides a computationally efficient approach for estimating population‐average treatment effects. Motivated by an experiment from materials testing, we develop and demonstrate designs with blocks of size two using copula models. Such designs are also important in applications ranging from microarray experiments to experiments on human eyes or limbs with naturally occurring blocks of size two. We present a methodology for design selection, make comparisons to existing approaches in the literature, and assess the robustness of the designs to modeling assumptions.

Fragmentation of processes and interventions plague the psychotherapies (Gilbert & Kirby, 2019). Part of the problem is that we have not agreed on a framework that could be the basis for integrating knowledge and the scientific enquiry of processes and interventions. This paper outlines an approach that brings together a variety of different disciplines in the service of consilience (Wilson, 1998, Consilience: The unity of knowledge, Vintage, New York, NY; Siegel, 2019). It presents the importance of an evolutionary framework for understanding the proclivities and dispositions for mental suffering and antisocial behaviour, and how they are choreographed in different sociodevelopmental contexts. Building on earlier models (Gilbert, 1989, Human nature and suffering, Routledge, London, UK; Gilbert, 1995, Clin. Psychol. Psychother., 2, 135; Gilbert, 1998, Br. J. Med. Psychol., 71, 353; Gilbert, 2016, Case formulation in cognitive behaviour therapy: The treatment of challenging cases, Wiley, Chichester, UK, pp. 50–89) the call is for an integrative, evolutionary, contextual, biopsychosocial approach to psychology and psychotherapy. Practitioner points Evolutionary functional analysis is part of an evolutionary, contextual, biopsychosocial approach to mental health that can serve as a scientific platform for the future developments of psychotherapy.Therapist skills and training will increasing need to focus on the multidimensional textures of mental states especially the context‐social‐body linkages.Therapies of the future will also focus more on the moral aspects of therapy and address the need to promote prosocial and ethical behaviour to self and others.

In the current article, we aim to study in detail a novel coronavirus (2019-nCoV or COVID-19) mathematical model for different aspects under Caputo fractional derivative. First, from analysis point of view, existence is necessary to be investigated for any applied problem. Therefore, we used fixed point theorem's due to Banach's and Schaefer's to establish some sufficient results regarding existence and uniqueness of the solution to the proposed model. On the other hand, stability is important in respect of approximate solution, so we have developed condition sufficient for the stability of Ulam-Hyers and their different types for the considered system. In addition, the model has also been considered for semianalytical solution via Laplace Adomian decomposition method (LADM). On Matlab, by taking some real data about Pakistan, we graph the obtained results. In the last of the manuscript, a detail discussion and brief conclusion are provided.

Since the end of 2019, computed tomography (CT) images have been used as an important substitute for the time‐consuming Reverse Transcriptase polymerase chain reaction (RT‐PCR) test; a new coronavirus 2019 (COVID‐19) disease has been detected and has quickly spread through many countries across the world. Medical imaging such as computed tomography provides great potential due to growing skepticism toward the sensitivity of RT‐PCR as a screening tool. For this purpose, automated image segmentation is highly desired for a clinical decision aid and disease monitoring. However, there is limited publicly accessible COVID‐19 image knowledge, leading to the overfitting of conventional approaches. To address this issue, the present paper focuses on data augmentation techniques to create synthetic data. Further, a framework has been proposed using WoT and traditional U‐Net with EfficientNet B0 to segment the COVID Radiopedia and Medseg datasets automatically. The framework achieves an F‐score of 0.96, which is best among state‐of‐the‐art methods. The performance of the proposed framework also computed using Sensitivity, Specificity, and Dice‐coefficient, achieves 84.5%, 93.9%, and 65.0%, respectively. Finally, the proposed work is validated using three quality of service (QoS) parameters such as server latency, response time, and network latency which improves the performance by 8%, 7%, and 10%, respectively.

The coronavirus disease 2019 (COVID-19) pandemic has led to tremendous loss of human life and has severe social and economic impacts worldwide. The spread of the disease has also caused dramatic uncertainty in financial markets, especially in the early stages of the pandemic. In this paper, we adopt the stochastic actor-oriented model (SAOM) to model dynamic/longitudinal financial networks with the covariates constructed from the network statistics of COVID-19 dynamic pandemic networks. Our findings provide evidence that the transmission risk of the COVID-19, measured in the transformed pandemic risk scores, is a main explanatory factor of financial network connectedness from March to May 2020. The pandemic statistics and transformed pandemic risk scores can give early signs of the intense connectedness of the financial markets in mid-March 2020. We can make use of the SAOM approach to predict possible financial contagion using pandemic network statistics and transformed pandemic risk scores of the COVID-19 and other pandemics.

This study contributes to our understanding of how the emergence of the COVID‐19 pandemic changes the global Banking Financial Services and Insurance (BFSI) landscape. Before the COVID‐19 pandemic, BFSIs corporate strategy was solely aligned to the quest for operational efficiency. However, during the ongoing COVID‐19 pandemic, global BFSIs are forced to adopt digital transformation in their operations due to a rise in transaction volumes. The ongoing COVID‐19 pandemic already triggers holistic innovations concerning the global BFSI's product, process, concept, trend, or idea. Thus, the BFSI cannot survive without efficient and innovative system software for global operations. The study plots the hype cycle to identify relevant technologies to deal with real‐world business problems. The hype cycle indicates that the need for advanced data integration is growing and COVID‐19 pandemic has already triggered it. The study argues that the incorporation of data integration might be challenging initially for BFSIs but eventually it may result in an efficient model to handle these types of pandemic or unexpected circumstances.

COVID-19 pandemic has affected all aspects of people's lives and disrupted the economy. Forecasting the number of cases infected with this virus can help authorities make accurate decisions on the interventions that must be implemented to control the pandemic. Investigation of the studies on COVID-19 forecasting indicates that various techniques such as statistical, mathematical, and machine and deep learning have been utilized. Although deep learning models have shown promising results in this context, their performance can be improved using auxiliary features. Therefore, in this study, we propose two hybrid deep learning methods that utilize the statistical features as auxiliary inputs and associate them with their main input. Specifically, we design a hybrid method of the multihead attention mechanism and the statistical features (ATT_FE) and a combined method of convolutional neural network and the statistical features (CNN_FE) and apply them to COVID-19 data of 10 countries with the highest number of confirmed cases. The results of experiments indicate that the hybrid models outperform their conventional counterparts in terms of performance measures. The experiments also demonstrate the superiority of the hybrid ATT_FE method over the long short-term memory model.

Summary Head pose classification is an important part of the preprocessing process of face recognition, which can independently solve application problems related to multi‐angle. But, due to the impact of the COVID‐19 coronavirus pandemic, more and more people wear masks to protect themselves, which covering most areas of the face. This greatly affects the performance of head pose classification. Therefore, this article proposes a method to classify the head pose with wearing a mask. This method focuses on the information that is helpful for head pose classification. First, the H‐channel image of the HSV color space is extracted through the conversion of the color space. Then use the line portrait to extract the contour lines of the face, and train the convolutional neural networks to extract features in combination with the grayscale image. Finally, stacked generalization technology is used to fuse the output of the three classifiers to obtain the final classification result. The results on the MAFA dataset show that compared with the current advanced algorithm, the accuracy of our method is 94.14% on the front, 86.58% on the more side, and 90.93% on the side, which has better performance.

The Covid‐19 pandemic has emerged as one of the most disquieting worldwide public health emergencies of the 21st century and has thrown into sharp relief, among other factors, the dire need for robust forecasting techniques for disease detection, alleviation as well as prevention. Forecasting has been one of the most powerful statistical methods employed the world over in various disciplines for detecting and analyzing trends and predicting future outcomes based on which timely and mitigating actions can be undertaken. To that end, several statistical methods and machine learning techniques have been harnessed depending upon the analysis desired and the availability of data. Historically speaking, most predictions thus arrived at have been short term and country‐specific in nature. In this work, multimodel machine learning technique is called EAMA for forecasting Covid‐19 related parameters in the long‐term both within India and on a global scale have been proposed. This proposed EAMA hybrid model is well‐suited to predictions based on past and present data. For this study, two datasets from the Ministry of Health & Family Welfare of India and Worldometers, respectively, have been exploited. Using these two datasets, long‐term data predictions for both India and the world have been outlined, and observed that predicted data being very similar to real‐time values. The experiment also conducted for statewise predictions of India and the countrywise predictions across the world and it has been included in the Appendix.

Zoonotic diseases—human diseases of animal origin—represent one of the world's greatest health challenges, both today and in the past. Since the Neolithic, zoonotic diseases have been one of the major factors shaping and influencing human adaptation. Archaeology is ideally situated to provide the long view on human–animal–pathogen relationships through combining cultural, environmental and biological datasets, yet long‐term studies of linked human and animal records have often been overlooked and undertheorized. The seven papers in this special issue “Zoonotic diseases: New directions in human–animal pathology” cover a range of diseases caused by bacterial, viral, and parasitic pathogens, from case studies drawn from across Europe, Asia, Africa and the Americas. They speak to the diversity of human–animal–environment interactions that shaped disease emergence and transmission. They also review methodological advancements relating to disease identification and interpretation and discuss interdisciplinary approaches to effectively investigate these complex processes in the past. This introduction highlights their key themes and outcomes and identifies research priorities moving forward.

Copyright © 2020 The Authors. The rapid spread of COVID-19 has led to a global shortfall in essential items, turning many countries into resource-constrained environments. In response, an unprecedented number of do-it-yourself hobbyists (i.e. makers) have started to use digital fabrication tools to produce critical items. These bottom-up communities are mobilising as part of a global movement to produce innovative solutions to much-needed items, such as face masks, face shields and ventilators. As these individuals tackle widespread resource constraints, the conceptual lens of frugal innovation becomes highly relevant to study how these solutions developed. Frugal innovation is a type of resource-constrained innovation that refers to the practice of doing more with less, for more people. In this study, we present two instrumental case studies of maker projects that use digital fabrication to tackle COVID-19. The first case study is from Italy (a High Income Country) and the second is from India (a Lower Middle Income Country). We analyse the frugality of these cases and highlight their similar approaches. In doing so, we suggest that current theories of frugal innovation can be expanded to new geographical and technological contexts. We put forward that frugal innovation is an important strategy in crisis response beyond emerging markets and that digital fabrication can be considered as an important frugal innovation enabler, both in its ability to produce frugal solutions and to support distributed networks of innovation actors. This study advances knowledge on how frugal innovation unfolds in the Maker movement. It is among one of the first studies to connect the domains of makers and frugal innovation, and the paper concludes by identifying several promising areas for further research. EPSRC DTP Doctoral Fellowship Award grant number NAAG/070.

Summary COVID‐19 is a quickly spreading over 10 million persons globally. The overall number of infected patients worldwide is estimated to be around 133,381,413 people. Infection rate is being increased on daily basis. It has also caused a devastating effect on the world economy and public health. Early stage detection of this disease is mandatory to reduce the mortality rate. Artificial intelligence performs a vital role for COVID‐19 detection at an initial stage using chest radiographs. The proposed methods comprise of the two phases. Deep features (DFs) are derived from its last fully connected layers of pre‐trained models like AlexNet and MobileNet in phase‐I. Later these feature vectors are fused serially. Best features are selected through feature selection method of PCA and passed to the SVM and KNN for classification. In phase‐II, quantum transfer learning model is utilized, in which a pre‐trained ResNet‐18 model is applied for DF collection and then these features are supplied as an input to the 4‐qubit quantum circuit for model training with the tuned hyperparameters. The proposed technique is evaluated on two publicly available x‐ray imaging datasets. The proposed methodology achieved an accuracy index of 99.0% with three classes including corona virus‐positive images, normal images, and pneumonia radiographs. In comparison to other recently published work, the experimental findings show that the proposed approach outperforms it.

The goal of this study is to examine knowledge sharing as a boundary condition under which employee innovation can be enhanced in response to the job stress induced by the COVID-19 pandemic. We argue that when stressed employees share knowledge, they can expand their knowledge base and thereby enhance their innovative potential. Consistent with our hypothesis, multiple regression analysis results based on a sample of 61 R&D employees of UK and US technology-based firms show that knowledge sharing moderated the relationship between COVID-19-induced job stress and employee innovation, such that the relationship was negative when knowledge sharing was lower but became positive when knowledge sharing was higher. These findings highlight the importance of investing in knowledge-based resources to promote innovation behavior at work during a pandemic.

One of the key issues in the field of technology analysis and innovation management is how new technologies origin and evolve in the presence of environmental threats. We confront this problem focusing on emerging innovative solutions to cope with unexpected and harmful problems posed by crises and needing a rapid, effective response. We specifically analyze the patterns of critical innovations to cope with new coronavirus disease (COVID‐19) that is generating public health and economic issues worldwide. Accordingly, in the context of the theory of technological exaptation, we adopted a narrative approach examining vital innovations that ended up treating COVID‐19 even though they were originated to treat other diseases (more or less distant from the COVID‐19 domain), as the antiviral drug Remdesivir and the antirheumatoid arthritis drug Tocilizumab. Results reveal that technological exaptation, especially if characterized by a longer exaptive distance, is a potential driving force of innovation to cope with COVID‐19 in the short‐term and other similar issues. On this basis, we provide propositions for a more general crisis model of innovation. This study adds a new perspective that may be helpful to explain the evolution of innovation in the presence of crises, considering technological exaptation in a context of environmental threats.

COVID-19 has been the most important pandemic that hit the world in the last century An overwhelming number of initiatives aimed to save lives and protect humanity came out especially at the beginning of the health crisis, following an outside the box approach to address relevant R&D problems Digital makers, not surprisingly, have been part of the game Being masters in using digital technologies for customized products, and being strategically fitted to cope with the imperative to be fast, many of the players on the front line to fight the pandemic have looked at them as the ‘providers next to the door’ able to offer fast solutions to critical problems Based on these premises and adopting frugal innovation as theoretical base, the aim of the study is to analyze how digital makers have taken part in R&D processes to find solutions to the problems created by an exogenous and unexpected sanitary problem Through an in-depth case study, we provide insights on if and how frugal approach to innovation is adapt to cope with the extraordinary pressure coming from event like the COVID-19 The results of the study highlight the power of connecting frugal innovation principles, agile mindset of makers and open innovation strategies in providing effective solutions on a local scale and eventually exploit their potential global impact Furthermore, we highlight implications and pathways for future researches © 2021 RADMA and John Wiley & Sons Ltd

The COVID‐19 pandemic has emerged as a highly transmissible disease which has caused a disastrous impact worldwide by adversely affecting the global economy, health, and human lives. This sudden explosion and uncontrolled worldwide spread of COVID‐19 has revealed the limitations of existing healthcare systems regarding handling public health emergencies. As governments seek to effectively re‐establish their economies, open workplaces, ensure safe travels and progressively return to normal life, there is an urgent need for technologies that may alleviate the severity of the losses. This article explores a promising solution for secure Digital Health Certificate, called NovidChain, a Blockchain‐based privacy‐preserving platform for COVID‐19 test/vaccine certificates issuing and verifying. More precisely, NovidChain incorporates several emergent concepts: (i) Blockchain technology to ensure data integrity and immutability, (ii) self‐sovereign identity to allow users to have complete control over their data, (iii) encryption of Personally Identifiable Information to enhance privacy, (iv) W3C verifiable credentials standard to facilitate instant verification of COVID‐19 proof, and (v) selective disclosure concept to permit user to share selected pieces of information with trusted parties. Therefore, NovidChain is designed to meet a high level of protection of personal data, in compliant with the GDPR and KYC requirements, and guarantees the user's self‐sovereignty, while ensuring both the safety of populations and the user's right to privacy. To prove the security and efficiency of the proposed NovidChain platform, this article also provides a detailed technical description, a proof‐of‐concept implementation, different experiments, and a comparative evaluation. The evaluation shows that NovidChain provides better financial cost and scalability results compared to other solutions. More precisely, we note a high difference in time between operations (i.e., between 46% and 56%). Furthermore, the evaluation confirms that NovidChain ensures security properties, particularly data integrity, forge, binding, uniqueness, peer‐indistinguishability, and revocation.

Background Meaningful and generalizable research depends on patients' willingness to participate. Studies often fail to reach satisfactory representativeness. Objective This paper aims to investigate reasons for not participating in research among young adult patients with psychiatric illness. Method A quantitative cross‐sectional study was performed based on questionnaires reported on by 51 psychiatric patients (14 males, 35 females and two unspecified) who had previously declined participation in an ongoing research project. Thereafter, a qualitative interview with subsequent content analysis was conducted with ten additional patients (five males, five females). Results The questionnaires indicate being ‘too tired/too sick to participate’ as the most common barrier. Lack of time and fear of needles were other common barriers. Lack of trust or belief in the value of research was less inhibitive. In the interviews, disabling psychiatric symptoms were confirmed as the main reason for not participating. Several potential ways to increase participation were identified, such as simplification of procedures and information as well as providing rewards and feedback, and building relationships before asking. Conclusion This study is unusual as it focuses on the group of young people attending psychiatry outpatient clinics we know very little about – those who do not partake in research. Our results indicate that fatigue and sickness reduce research participation and identify factors that may facilitate enrolment of this important group.

Network meta-analysis (NMA) can be used to compare multiple competing treatments for the same disease. In practice, usually a range of outcomes is of interest. As the number of outcomes increases, summarizing results from multiple NMAs becomes a nontrivial task, especially for larger networks. Moreover, NMAs provide results in terms of relative effect measures that can be difficult to interpret and apply in every-day clinical practice, such as the odds ratios. In this article, we aim to facilitate the clinical decision-making process by proposing a new graphical tool, the Kilim plot, for presenting results from NMA on multiple outcomes. Our plot compactly summarizes results on all treatments and all outcomes; it provides information regarding the strength of the statistical evidence of treatment effects, while it illustrates absolute, rather than relative, effects of interventions. Moreover, it can be easily modified to include considerations regarding clinically important effects. To showcase our method, we use data from a network of studies in antidepressants. All analyses are performed in R and we provide the source code needed to produce the Kilim plot, as well as an interactive web application.

It is often challenging to present the available evidence in a timely and comprehensible manner. We aimed to visualize the evolution of evidence about antidepressants for depression by conducting cumulative network meta-analyses (NMAs) and to examine whether it could have helped the selection of optimal drugs. We built a Shiny web application that performs and presents cumulative NMAs based on R netmeta. We used a comprehensive dataset of double-blind randomized controlled trials of 21 antidepressants in the acute treatment of major depression. The primary outcomes were efficacy (treatment response) and acceptability (all-cause discontinuation), and treatment effects were summarized via odds ratios. We evaluated the confidence in evidence using the CINeMA (Confidence in Network Meta-Analysis) framework for a series of consecutive NMAs. Users can change several conditions for the analysis, such as the period of synthesis, among the others. We present the league tables and two-dimensional plots that combine efficacy, acceptability and level of confidence in the evidence together, for NMAs conducted in 1990, 1995, 2000, 2005, 2010, and 2016. They reveal that through the past four decades, newly approved drugs often showed initially exaggerated results, which tended to diminish and stabilize after approximately a decade. Over the years, the drugs with relative superiority changed dramatically; but as the evidence network grew larger and better connected, the overall confidence improved. The Shiny app visualizes how evidence evolved over years, emphasizing the need for a careful interpretation of relative effects between drugs, especially for the potentially amplified performance of newly approved drugs. HIGHLIGHTS: Network meta-analysis is considered to be a proper way of demonstrating the available evidence, since it allows comparisons between multiple interventions, and has been proved to be statistically powerful. It is challenging to present the voluminous results of NMA in an efficient and comprehendible manner. Evidence evolution based on the relatively new method NMA has not been investigated yet. The results of NMA should not only include the effects but also the confidence in the evidence, which can help interpret the findings appropriately. Effective use of rapidly developing statistical analysis and presentation tools such as Shiny package in R, may facilitate and simplify the visualization of NMA output. We should stay conservative towards new drugs, as their performance was often shown to be exaggerated initially, and it took time to become stable.

The article deals with the analysis of the fractional COVID‐19 epidemic model (FCEM) with a convex incidence rate. Keeping in view the fading memory and crossover behavior found in many biological phenomena, we study the coronavirus disease by using the noninteger Caputo derivative (CD). Under the Caputo operator (CO), existence and uniqueness for the solutions of the FCEM have been analyzed using fixed point theorems. We study all the basic properties and results including local and global stability. We show the global stability of disease‐free equilibrium using the method of Castillo‐Chavez, while for disease endemic, we use the method of geometrical approach. Sensitivity analysis is carried out to highlight the most sensitive parameters corresponding to basic reproduction number. Simulations are performed via first‐order convergent numerical technique to determine how changes in parameters affect the dynamical behavior of the system.

Purpose: The implementation of photon-counting detectors is widely expected to be the next breakthrough in X-ray computed tomography (CT) instrumentation. A small number of prototype scanners equipped with direct-conversion detectors based on room-temperature semiconductors, such as CdTe and CdZnTe (CZT), are currently installed at medical centers. Here, we investigate the feasibility of using silicon photomultiplier (SiPM)-based scintillation detectors in photon-counting computed tomography (PCCT) scanners, as a potential alternative to CdTe and CZT detectors. Methods: We introduce a model that allows us to compute the expected energy resolution as well as the expected pulse shape and associated rate capability of SiPM-based PCCT detectors. The model takes into account SiPM saturation and optical crosstalk, because these phenomena may substantially affect the performance of SiPM-based PCCT detectors with sub-mm pixels. We present model validation experiments using a single-pixel detector consisting of a 0.9 × 0.9 × 1.0 mm3 LuAP:Ce scintillation crystal coupled to a 1 × 1 mm2 SiPM. We subsequently use the validated model to compute the expected performance of the fast scintillators LYSO:Ce, LuAP:Ce, and LaBr3:Ce, coupled to currently available SiPMs, as well as to a more advanced SiPM prototype with improved dynamic range, for sub-mm pixel sizes. Results: The model was found to be in good agreement with the validation experiments, both with respect to energy resolution and pulse shape. It shows how saturation progressively degrades the energy resolution of detectors equipped with currently available SiPMs as the pixel size decreases. Moreover, the expected pulse duration is relatively long (~200 ns) with these SiPMs. However, when LuAP:Ce and LaBr3:Ce are coupled to the more advanced SiPM prototype, the pulse duration improves to less than 60 ns, which is in the same order of magnitude as pulses from CdTe and CZT detectors. It follows that sufficient rate capability can be achieved with pixel sizes of 400 μm or smaller. Moreover, LaBr3:Ce detectors can provide an energy resolution of 11.5%-13.5% at 60 keV, comparable to CdTe and CZT detectors. Conclusions: This work provides first evidence that it may be feasible to develop SiPM-based scintillation detectors for PCCT that can compete with CdTe and CZT detectors in terms of energy resolution and rate capability.

Effect direction (evidence to indicate improvement, deterioration, or no change in an outcome) can be used as a standardized metric which enables the synthesis of diverse effect measures in systematic reviews. The effect direction (ED) plot was developed to support the synthesis and visualization of effect direction data. Methods for the ED plot require updating in light of new Cochrane guidance on alternative synthesis methods. To update the ED plot, statistical significance was removed from the algorithm for within-study synthesis and use of a sign test was considered to examine whether patterns of ED across studies could be due to chance alone. The revised methods were applied to an existing Cochrane review of the health impacts of housing improvements. The revised ED plot provides a method of data visualization in synthesis without meta-analysis that incorporates information about study characteristics and study quality, using ED as a common metric, without relying on statistical significance to combine outcomes of single studies. The results of sign tests, when appropriate, suggest caution in over-interpreting apparent patterns in effect direction, especially when the number of included studies is small. The revised ED plot meets the need for alternative methods of synthesis and data visualization when meta-analysis is not possible, enabling a transparent link between the data and conclusions of a systematic review. ED plots may be particularly useful in reviews that incorporate nonrandomized studies, complex systems approaches, and diverse sources of evidence, due to the variety of study designs and outcomes in such reviews.

The COVID-19 pandemic has caused unprecedented public health and economic crises. As a response to face the current emergency, science and innovation communities are realizing a fundamental contribution to tackle the crisis. During the past few months, we have witnessed an impressive number of initiatives to encourage networking opportunities, to foster interactions between the different stakeholders involved (health care, industry, governments, academics, ordinary people), and to develop innovative solutions and collaborative infrastructures in support of the health sector. Adopting an open and collaborative approach and joining forces is essential in the fight against the COVID-19 crisis. Also, the involvement of crowds as innovation partners can be of great support. Therefore, our work aims to review and classify those initiatives, based on the crowdsourcing model, that have been put into place to face the emergency generated by the novel coronavirus pandemic. We illustrate the 16 crowdsourcing initiatives devoted to the SARS-CoV-2 outbreak that we identified, detailing their development and implementation. Then, we propose a classification of them, along two dimensions: type of crowdsourcing configuration and kind of tasks, being able to find a relationship between these two aspects. Evidence from the analyzed projects suggests that across disparate domains, crowdsourcing can be an effective strategy in the response to the COVID-19 pandemic. To conclude, we suggest some important implications for innovation best practices and lessons that can be learned for the future: crowdsourcing, harnessing the power of crowds and online communities, can help tackle the COVID-19 pandemic, by providing original, actionable, quick, and low-cost solutions to the challenges of the current health and economic crisis.