Historical events and technological innovation in the mid-part of Industry 2.0 created an inflection point resulting in the emergence of prosthetists who concentrated solely on hand crafting and fitting artificial limbs as a professional specialty. The third industrial revolution, Industry 3.0, began transforming prosthetic devices themselves. Static or body powered devices began to incorporate digital technology and myoelectric control options and hand carved wood sockets transitioned to laminated designs. Industry 4.0 continued digital advancements and augmenting them with data bases which to which machine learning (M/L) could be applied. This made it possible to use modeling software to better design various elements of prosthetic componentry in conjunction with new materials, additive manufacturing processes and mass customization capabilities. Digitization also began supporting clinical practices, allowing the development of clinical evaluation tools which were becoming a necessity as those paying for devices began requiring objective evidence that the prosthetic technology being paid for was clinically and functionally appropriate and cost effective. Two additional disruptive dynamics emerged. The first was the use of social media tools, allowing amputees to connect directly with engineers and tech developers and become participants in the prosthetic design process. The second was innovation in medical treatments, from diabetes treatments having the potential to reduce the number of lower limb amputations to Osseointegration techniques, which allow for the direct attachment of a prosthesis to a bone anchored implant. Both have the potential to impact prosthetic clinical and business models. Questions remains as to how current prosthetic clinical practitioners will respond and adapt as Industry 4.0 as it continues to shape the sector.Technological advances from Industry 1.0 to 4.0, have exercised an increasing influence on prosthetic technology and practices. This paper explores the historical development of the sector within the greater context of industrial revolution. Over the course of the first and up the midpoint of the second industrial revolutions, Industry 1.0 and 2.0, the production and provision of prosthetic devices was an  The term Industry 4.0 is frequently used enthusiastically to describe a new wave of exponential innovation that will revolutionize the world as we know it and, with it, the field of orthopedics, including prosthetic devices , 2. The It has become common to describe technological evolution within a framework of industrial revolutions, which are used to denote eras of significant change in how goods are designed and produced or how technological developments change products and processes. Initially it was used to describe the transition from an agrarian society to an industrial one beginning in the mid 1700's. It is now used to describe four eras: The Industrial Revolution (Industry 1.0), The Technological Revolution Industry 2.0), The Digital Revolution Industry 3.0) and the Physical, Digital, and Biological Convergence (Industry 4.0) , Flexible Manufacturing Systems (FMS) and Advanced Digital Manufacturing (ADM). This set the stage for disruption of prosthetic production under Industry 4.0.Beginning in the 1980's digital design and manufacturing tools developed for other sectors began to make their way into the prosthetics sector. The application of CAD/CAM to the production of prosthetic devices was a natural offshoot of the success of CAD/CAM in other fields, adding efficiencies and reproducible accuracy. Early champions identified multiple benefits coming from the adoption of this technology in prosthetics . SeminalDigitally supported advances were not limited to production processes. The far more visible and impactful digital transformation occurred in prosthetic componentry, setting new standards for prosthetic device function and end-user outcomes. Digital solutions first made their mark in upper limb prosthetics early in this period but it was in lower limb prosthetics that digital technology had the most perceptible impact on amputees, with the introduction of microprocessor controlled (MPC) joints. The Intelligent Knee  became the first MPC prosthetic knee to enter the marketplace quickly followed by the C-leg  and then, MCP ankles and feet. These components profoundly changed the amputee experience by addressing functional needs and safety that previous prosthetic designs and technology were unable to –25.Digital applications are the hallmark of Industry 3.0, but it was the adoption of synthetic polymers developed during Industry 2.0 most visually changed prosthetic technology in the early days of Industry 3.0. At that time prosthetic devices were still carved from wood, forged from metal and completed with customized leatherwork. New materials quickly transitioned the sector away from those materials to acrylic and polyester laminates. Though not “digital” in nature, the adoption of these new materials supported a paradigm shift that allowed novel prosthetic designs to be developed using structured processes incorporating interdisciplinary criteria, such as biomechanics and anatomy, into the design process . The movFollowing World War 2, university research programs supporting improvements in prosthetic design were initiated. Universities began to influence the sector with developments such as the Supracondylar Socket, Patellar Tendon Bearing Socket, Four-Bar prosthetic knee joint mechanism, SACH (Solid Ankle Cushion Heel) Foot and Seattle Foot, each of which capitalized on materials sciences advances that were now coupled with structured engineering design practices and were carried out within an academic environment.Collaborations between private industry and academic or public institutions also contributed, the classic example being the myoelectric arm combining the use of new materials with digital technology and developed by Ottobock Gmbh in collaboration with institutionally based research programs, such at the ones at I.N.A.I.L in Italy . A seconThe objective study of prosthetic gait today cannot be imagined without digital technology. The integration of cameras capturing 3D movement with force sensors launched kinematic (motion) studies and kinetic (force) studies as a formal area of research. Leaders in applying this to pathological gait and prosthetic applications are Inman (UCLA-SF) and Perry , the latter of whom expanded the tool kit by adding fine wire electromyography. Professor Paul aided, by Jarrett and Andrews, was instrumental in digitally integrating these tools, setting the stage for modern gait analysis systems , 29. TheAs the digital revolution gained momentum, the range of digital tools expanded to include scanners to capture residual limb shapes for modification in CAD/CAM systems, step count monitors allowing the tracking of community-based activity of prosthesis users , and tooThe development of these tools provided new perspectives on the prosthetic device provision process. Their objective, valid and reliable evidence-based outcome measures presented an alternative to the subjective “clinical expert” opinion that, up to that point, had been the standard for determining if prosthesis fit or function was satisfactory, or not. Despite their availability, uptake of the new digital tools was slow on the part of prosthetic practitioners in large part because they did not provide direct benefit to the clinical practitioner by improving efficiency, increasing productivity or boosting the bottom line. Compounding this, many of the new tools had high entry costs which proved to be a tangible barrier to adoption .In the academic setting, the development of clinical outcome measures specific to prosthetics gained momentum in the mid 1990's. In particular, Gailey (University of Miami) and Hafner (University of Washington) were carried out critical, objective research on amputee gait that led to the development of a wide range objective clinical evaluation measures including, but not limited to, the AMPRO, AMPnoPRO (Gailey) and Plus-M (Hafner) , 35.The need for such measures had already been identified in literature by Ramstrand and Brotkorb  but untiThe entry costs for digital tools in this sector have significantly reduced over time but, in developing technology for this sector, this stage of prosthetic history illustrates the importance of balancing the full spectrum of economic costs vs. benefits; a critical factor in prosthetics due to the highly cost sensitive nature of the fee-for-device business model.Clinical outcome measures and evidence-based practices were not seriously considered in the clinical setting until the publication of the aforementioned Levinson Report  by the UThis report set in motion the translation of academic research on clinical outcome measures and digital evaluation tools practices into the clinical setting. Momentum was built by the active support for the development of evidence-based practices and tools through funding from organizations as diverse as: American Academy of Orthotists and Prosthetists (AAOP). American Orthotics and Prosthetics Association, the U.S. National Institutes of Health (NIH), U.S. Department of Defense (DoD) and the Orthotics & Prosthetics Education and Research Foundation (OPERF). This established an ongoing collaborative, interdisciplinary effort that is creating evidence-based knowledge on clinical and technical issues that relate to amputees and prosthetic care, including the use of digital tools.The term Industry 4.0 describes a convergence of Physical, Digital and Biological Systems that support the creation of “smart” technology or cyber-physical systems. The resulting technology can be networked and allows for the collection and storing of large amounts of data in data bases which have value in themselves as drivers of innovation.Smart technology is often described as disruptive, spanning nine enabling technologies: Advanced Manufacturing, Additive Manufacturing, Data Analytics, Augmented Reality, Simulation, Horizontal/Vertical Integration, Cyber Security, Cloud Computing and the Industrial Internet.At first glance this list has little relevance to the day to day production and provision of prosthetic devices but, as was the case in Industry 2.0 and 3.0, the prosthetics sector will be influenced and shaped by Industry 4.0. Technologies from this list that have already begun to manifest themselves in the prosthetics sector include Additive Manufacturing (3D printing), Smart Sensors, the Internet of Things (IoT), Blockchain, Software as a Service (SaaS), Machine Learning (M/L) and Big Data . In the As in the previous industrial revolutions, advances in materials sciences are playing a prominent role in the evolution of prosthetic devices under Industry 4.0. In previous eras materials advances led to improved cosmesis, function and fit but the general form and design of prosthetic devices remained the same. Under Industry 4.0 data driven engineering approaches are being applied to materials development. This is allowing prosthetic designs metamorphosize and, in particular, there is now the ability to address shortcomings that have been identified with the classic prosthetic socket .Changes in volume of the residual limb have been a long-identified problem with classic, rigid socket designs. Past approaches to managing residual limb volume fluctuation ranged from early adjustable sockets made of leather and lacers , 42 to aAt the complex end of the spectrum tools such as 3D Printing will be supported by new processes for measuring residual limbs using smart phones, scanners and other imaging technology. Highly sophisticated and complex methods of objectively capturing surface anatomy, images of the underlying anatomical structures and potentially, pressure gradients  and tissSmart garments combine novel fibers and textiles with sensors and data streaming capabilities. They can be used to monitor and diagnose medical conditions or, by the very nature of their properties, provide benefits to the wearer including equal or superior clinical outcomes, lower costs or better customer experience. Digital tools integrated into garments will become commonplace throughout the entire health care system and, in addition to enhancing care, will allow vast data bases to be created and mined to support decision making processes.tm, has been validated as a tool for collecting physiological measures for a range of activities  and generative design practices will be applied . This wiThe prosthesis of the future will be one that is custom designed and produced for individual end-users using objective design tools and automated industrial production methods and will be fitted and maintained using smart tools that provide objective, close to real time, data. This will allow the prosthetist to complete the evolution from being a crafter and fitter of devices to become a clinical technology manager, in partnership with amputees.In this transformation to becoming technology mangers, automation and Software-as-a-Service (SaaS) can assist prosthetists with improving quality and outcomes, whilst at the same time addressing productivity and labor shortages, another challenge currently faced by the sector and which will require the adoption of new approaches and technology.The World Health Organization (WHO) identified a lack of skilled personnel at the international level. The National Committee on Orthotic and Prosthetic Education (NCOPE) has identified a labor shortage in the US , a prostIn sectors with labor shortages, including health care service delivery, automation processes and the use of AI can help ameliorate workload and productivity challenges and at the same time improve clinical outcomes . AutomatOne of two perspectives on automation typically surface when discussing Industry 4.0. One provocatively presents the automated “robot” as a replacement for the worker. The other presents automation as a tool to help improve productivity and quality . The forEfforts to move the prosthetics business model from fee-for-device to fee-for-clinical services have met with limited success globally. The prosthetics practitioner is increasingly challenged to find efficiencies within their current business model. This is where automation tools will be able to play a positive role, by allowing prosthetic practitioners to restructure their prosthetic design and production activities improve productivity. This is no a scenario in which robots take over. This shift will happen in parallel with software advances that improve the efficiency of clinic practices by re-shaping current administrative and business practices. Much of the focus in the prosthetics sector has been on hardware related innovations, but software innovation will have an equal impact. Two are worth exploring within the context of this paper, Apps and Software-as-a-Service (SaaS).App is short for Application, a small piece of software designed to carry out a specific task using smart phones, tablets and other digital devices. Apps are already available in the prosthetics sector supporting tasks such as taking outcome measures, aligning, tuning or monitoring a prosthetic device, taking scans of body parts, interfacing with electronic medical records (EMRs) and providing a portal for communicating with payors . Apps arSaaS consists of subscription-based software platforms that support a range of business, production and clinical activities. SaaS reduces the initial cost of purchasing software and the associated costs of maintaining software in house. It can assist a clinical practice to become leaner, aiding with billing and reporting to payors, communicating with clients, organizing clinical outcome measures and tracking quality assurance data. It can also support liability management processes and can help maintain business continuity in crisis situations. SaaS will become an essential component of the prosthetic business model of the future, working synergistically with the manufacturing software and hardware. Finally, SaaS's networked nature will allow for prosthetic clinics to network with others that use the same platform(s) in order to collaborate to create large data bases that can be shared and mined, helping network members to maintain a collective competitive edge.If harnessed strategically, these tools can allow prosthetists to reduce the time they devote to either producing prosthetic devices or wrestling with administrative tasks, allowing them to reallocate that time to focusing on their relationships with their clients, the amputees.via the new tools being created and by using modeling, machine learning and AI to harness data bases. It is difficult to predicted what the impact will be as these developments are still in the early to mid stages of the innovation pipeline. But two examples with the potential to disrupt current prosthetic practices have matured and are translating to the clinical setting in developed nations.Industry 4.0 will also be accelerating advances in medical treatment options The first is in the treatment of diabetes, which creates a high societal burden and is a common cause of amputation in developed nations , 66. A wThe second, specific to prosthetics, is osseointegration (OI) or bone anchored prostheses which sit directly on the intersection of the Physical, Digital and Biological as a medical-technological hybrid solution for existing amputees. OI involves attaching prosthetic leg componentry direct to a bone anchored implant, much like dental implants work. This eliminates the prosthetic socket completely and with it, many of the problems associated with the fit and use of sockets. OI provides the additional benefit of providing a secure interface between prosthesis and skeleton, which has been shown to improve osseo-perception and walking ability . It doesIndustry 4.0 is often presented using device-based, hardware and software examples. More difficult to quantify and express are the psychosocial changes occurring in this era, arising from the enthusiastic uptake of the concept of democratization of technology . The incvice versa. Amputees can now communicate their desires and selves directly to engineers and industrial designers, circumventing the traditional “clinical expert” filter who in the past formed a barrier between end-user and engineer  that were designed, produced and supplied directly to the amputees, typically by engineering student volunteer groups. The ensuing demand and media attention made it clear that there was an unmet market need that was actively seeking a solution. This first iteration toward that solution did not upend the traditional prosthetic device market, but work in this space continues. A powerful characteristic of additive manufacturing is to allow fast, documentable design iteration and it can be anticipated that efforts by technology developers to create lower cost, customizable designs at the local level will eventually be successful and will have global impact in addressing this unmet market need.The creation of lower cost, high quality, durable medical devices and health products supports more equitable health care options for all persons at the global level. In low resource settings, developments at the low-cost end of the innovation spectrum have the potential to not only reduce the cost of producing a device for those who cannot afford one, but also by making it possible to move the point of care to the local level. This is of critical importance as the expense of travel to a prosthetic clinic costs more than the device itself, creating an additional barrier. The cost of 3D printing technology continues to decree while the quality of prints increases, in parallel with smart phones become ubiquitous globally and provide access to telemedicine. The intersection of these trends will allow more sophisticated, mobile and affordable care to be delivered close to where it is needed. This vision is consistent with other health care innovations for low resource settings which are now harnessing technology in this way .via a hearing aid clinic  Subcommittee on Rehabilitation Engineering and Prosthetics/Orthotics.The author confirms being the sole contributor of this work and has approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


As a body movement art, dance has its special form of expression. In terms of dance vocabulary, it can be roughly divided into two parts: external body movement and internal modality. In the process of body movement, it conveys information through silent language and the audience directly feels the information given by the dance image through vision. This is the special way of expressing emotion and meaning in dance art. This paper combines artificial intelligence technology and BP neural network (BPNN) algorithm to intelligently control dance teaching and solve complex nonlinear control problems. This paper studies dance teaching based on artificial intelligence technology. In this paper, BPNN algorithm and PCA-BPNN algorithm are used to test the dance teaching training of dance language, dance music, and stage art. The average accuracy of the BPNN evaluation model is 85.35% when the time reaches 80, while the average accuracy of the PCA-BPNN evaluation model is 65.64%. This shows that the accuracy of the BPNN evaluation model is higher than that of the PCA-BPNN evaluation model. Under the artificial intelligence technology, the dance using BPNN algorithm brings more intense sensory stimulation to the viewer because of the accompaniment of music, so as to achieve the infection and enjoyment of beauty and achieve the harmonious unity of sports and art. With the action, image, and expression of the performer's human body, dance is a movement that conveys the character's emotion and infers the story's content and plot. The most direct, vivid, and contagious way for people to express their thoughts and feelings is through dance, it can be said. The audience is directly made aware of the information provided by the dance image through vision as a result of the body's process of silent language communication. This is the unique manner in which dance art expresses emotion and meaning. Education in dance aesthetics is a crucial part of the educational process. It plays a significant part in enhancing one's physical and mental well-being, particularly psychological well-being, which primarily includes intelligence, non-intellectual factors, creativity, mental health, and esthetic psychological ability , 2. The This paper implements intelligent control on dance teaching using BPNN algorithm  and artiArtificial intelligence technology can effectively obtain learners' dance training actions and give personalized, real-time and in-depth data feedback through the application of BPNN algorithm in dance-assisted teaching, which is of great benefit to promoting students' autonomous learning, assisting teachers' assisted teaching and improving dance teaching efficiency. The cognitive intelligence of BPNN algorithm is further developed on the basis of perceptual intelligence. It has certain machine learning ability , which cThis paper constructs a BPNN model, which is composed of three layers of neurons: input layer, hidden layer, and output layer. The neurons between different layers are fully interconnected, and there is no interconnection between neurons in the same layer. The input layer is the standardized evaluation index value, and the output layer has only one neuron.This paper analyzes the main problems and solutions of dance teaching. Dance teaching achieves richer training effects through more diversified teaching modes, meets the different needs of different learners, creates more dance learning space and learning opportunities, and further improves the effect of dance teaching through the good integration of teaching methods, advanced technology, and learning space.The main innovations of this paper are as follows:The overall structure of this paper consists of five parts. Section 1 introduces the background and significance of dance teaching and then introduces the main work of this paper. Section 2 mainly introduces the literature review of dance teaching. Section 3 studies the application of artificial intelligence technology in dance teaching. In Section 4, the simulation experiment is carried out, and the influence analysis of dance teaching is experimented and discussed. Section 5 is the summary of the full text.With the progress of the times, the popularity of dance and the development of mass dance activities have been widely used in China. Dance teaching materials enable professional teachers to more accurately and completely express the teaching content for future teaching reference and research, help students obtain creative inspiration, improve their overall ability, and reflect the advantages of multimedia technology teaching. The following is a literature review on the research made by researchers in dance teaching.Liu et al. proposed that due to the lack of informatization in the traditional dance teaching space, the dance curriculum and information technology have not been effectively integrated. There is still a problem that the teaching chain at the equipment end of the venue has not been opened up, which leads to the blind spot in the judgment of students on the standard of their own practice movements, so that it is difficult for learners to independently form and improve dance skills . Wang etThe aforementioned literature leads to the conclusion that, in contrast to other disciplines, dance instruction places a premium on teacher-led demonstrations, making it challenging for students to learn dance technique solely through words and pictures. So I conducted a thorough study on artificial intelligence-based dance instruction. Artificial intelligence's quick advancement creates favorable conditions for the diversification of teaching strategies. When the class size is large, it is beneficial for teachers to make full use of artificial intelligence to organize the teaching so that students are taught according to their aptitude. For various students, offer targeted teaching methods. In order to learn the fundamentals of applied mathematics, students must first solve the problems that they come across. Teachers can develop an effective and clear understanding of students' mastery of dance skills and theoretical knowledge learning through the statistics and analysis of big data in dance teaching based on artificial intelligence technology. This allows teachers to not only identify each student's “short board,” but also clarify their specific learning challenges at the time. In order to carry out individualized learning and teach students according to their aptitude, it is necessary to record, track, and analyze the learning behavior data of learners. This creates a “learner portrait” that teachers and learners can use to better understand the learning characteristics and rules of each individual learner. Through artificial intelligence technology, learners are presented with various kinds of learning performance data such as individuals and others, individuals and groups, etc., which helps students to understand and reflect on their own learning state, thus promoting the development of their learning thinking and their active construction of knowledge, helping to improve students' metacognitive development level and make them become active learners who can make better use of the data for dance training.Artificial intelligence technology first appeared on the historical stage with the invention of computers, and people started to study it seriously after that. The goal of artificial intelligence research is to free humans from complex work that requires entirely different justifications at various points in time  and alloThe model consists of three layers of neurons: input layer, hidden layer, and output layer. The neurons in different layers are fully interconnected, but there is no interconnection between neurons in the same layer. The input layer is the standardized evaluation index values, and the output layer has only one neuron. The input layer accepts the input vector and normalizes it: the response function of the mode layer isSj of each unit in the hidden layer is calculated by a randomly selected set of input samples pk=, connection weight Wij, and threshold θj, and then, the output of each unit in the hidden layer is calculated by Sj.f means activation function, which is used to show the activity of neurons represented by each unit. Because it maps a larger input range to a smaller interval, it is also called squeezing function.The input Lt of each unit in the output layer is calculated by using the output bj connection weight Vjt and the threshold γt of the hidden layer, and then the output Ct0 of each unit in the output layer is calculated by the activation function.The input Tk=. The actual output Ct of the network, calculate the generalized error dt0k of each unit in the output layer.We use the network target vector Vjt, the generalization error dt of the output layer and the output bj of the hidden layer, the unit generalization error ej0k of the hidden layer is calculated.Using the connection weight It primarily addresses the data mining problems of classification and regression. It simulates human thought processes based on the parallel processing mechanism, realizing some aspects of human thought processes like learning, logical inference, associative memory, and self-organization. Actually, understanding labor is not that difficult. The definition of labor in the scientific community can essentially be unified, that is, people working within an organizational framework work and operate in accordance with predetermined procedures in order to fully utilize their individual strengths and accomplish the desired results or functions . Simply a and b are taken as examples to illustrate the principle of probabilistic neural network. Let the vector to be judged be X. According to the minimum risk criterion, the following are the classification rules:Ha and Hb are the prior probabilities that X belongs to a and b, respectively; La and Lb are the cost factors for misclassifying a and b states, respectively; and fa(X) and fb(X) are conditional probability functions of X belonging to a and b, respectively. fa(X) can be obtained from training samples.q is the dimension of the vector X to be classified and the training vector; Na is the number of training samples of state mode a; Xai is the ith training vector of the state mode a; and σ is the smoothing parameter.An application that uses a computer to build a simulation environment so that users can fully immerse themselves in it is known as a computer simulation system . The virBPNN has made remarkable achievements in many fields such as signal processing, pattern recognition, target tracking, robot control, expert system, combinatorial optimization, network management, and so on. Especially in solving the problems of nonlinear systems, it has unique advantages.Dance instruction has experienced a thorough transformation of the teaching approach and methods as a result of today's educational reform and innovation. Traditional teaching techniques like teacher narration, demonstration, and action correction in this situation exhibit increasingly glaring drawbacks in terms of teaching effectiveness and quality. The origin of all other art forms is dance. The essence of dance art cannot be reduced to basic bodily motions, material motions, or physical phenomena. Dance is only carried and performed on the body. Man is spirit at his core. The soul of art is alive with the spirit of beauty. The most important technique in dance instruction is systematic, rigorous training. This will ensure that students have the fundamental dance skills they need, that their bodies are coordinated and under tight control, that their performances have enough explosive power, and that there is a seamless connection between their dance movements and facial expressions. The idea of natural ability and dance knowledge development occurring simultaneously. Through all of the learning activities, dance creativity and practical application skills are being developed. On the basis of focusing on the transfer of knowledge, we should vigorously strengthen the cultivation of the capacity to learn, pose, analyze, and solve problems, as well as to internalize knowledge and skills into quality. It will significantly harm the specific presentation effect of dance works and even endanger the dancer's health if learners' dance movements deviate and cannot be quickly corrected. Dance instruction involves using dance vocabulary and incorporates other tools like stage art and music and clearly states the work's theme. Describe the characters' inner feelings. Give an explanation of the characters' personalities. Draw something strikingly creative. This necessitates that dance art performance performers have a strong foundation in dance performance able to adjust to the complex and fluctuating requirements of challenging dance skills.In such a teaching style, students find it challenging to reflect the dominant position and also lack the resources necessary for independent study in the sciences. They are also unable to integrate dance into other disciplines through appropriate practice due to the fact that teachers are unable to accurately understand the unique learning circumstances of their students before and after class and cannot conduct a practical analysis of their learning situation. Additionally, the assessment and evaluation process is very rigid, which has an impact on the evaluation's diversity and objectivity. As a result, it is difficult to identify the primary issues preventing students from learning to dance and to offer solutions. As a result, dance instruction cannot be supported and encouraged effectively. These so-called foundations are very different from what students actually need. In the few class periods available, students cannot finish these foundations. Additionally, we received our training from primary and secondary school music teachers rather than professional actors. The primary focus of the instruction is the study of Chinese national folk dance, which is done to help the students love their country and dance tradition, improve their performance awareness of their national dance, which includes “singing and dancing, self-entertainment, active participation, and improvisation,” and more fully assimilate the world's best dance traditions. Based on such an all-encompassing educational approach, dance education moves beyond the conventional single physiological training concept of movement skill training and adopts a three-dimensional dance concept of physiology, psychology, and teachers. It has the overall result of encouraging the balanced development of teachers' skills and of students' bodies and minds. Therefore, we think that the three levels of “dance performance, dance creation, and practical application ability” should be the foundation of the dance curriculum in order to create a teaching curriculum structure that satisfies the demands of the growth of music education in primary and secondary schools. The basic movement and skill training of learners has not been the best in traditional dance education, though. This group's proficiency with fundamental dance movements exhibits ever-increasing differences, particularly with the growing number of dance learners. Dance instruction produces richer training effects through a wider variety of teaching modalities, satisfies the diverse learning needs of different students, expands the learning environment and opportunities for dance instruction, and further enhances the impact of dance instruction through effective fusion of instructional strategies, cutting-edge technology, and learning environment.A total of 120 students from 40 dance classes, 40 aerobics classes and 40 rhythmic gymnastics classes in the Department of Sports and Art were given out 120 questionnaires, and 117 questionnaires were collected, with a recovery rate of 97.5%. The survey adopted a unified instruction, and the respondents filled it out at the same time. The experimental results are shown in After the completion of the questionnaire design, the validity of the questionnaire was tested by expert judgment method, and six related research experts were hired to test the validity of the questionnaire, including four professors and two associate professors. According to the evaluation results, Dance language, dance music and stage art are used for dance teaching training test. The training convergence diagrams of BPNN evaluation model and PCA-BPNN evaluation model are shown in Figures As can be seen from Figures The basic elements of music are composed of three parts: rhythm, which controls the movement of music in time; Melody, which means the linear arrangement of musical notes; Harmony, which deals with the relationship between the simultaneous sound of different tones. There are other important ingredients. This experiment ranks the basic elements of music that affect dance learning, and the results are shown in It can be seen from A dance robot was selected as the experimental object, and the program was written by MATLAB to read the music signal in the dance. The results showed that the pitch frequency range of the music used in the experiment was 103 Hz∼405 Hz, the frequency range of the music signal was 25 Hz∼25 kHz, the peak value range was 2.6 ms∼9.7 ms, and the maximum absolute error of MATLAB was 0.5. Set the sampling frequency range to 12 kHZ, 24 kHZ, and 46 kHZ, and the window length range to 31 ms∼51 ms. The music signal after frame processing is shown in As can be seen from People's perception of music is formed through these basic elements. The reason why different people have their own understanding of the same music is that they have different feelings for these basic elements. In the process of dance learning, the influence degree of each element of music is different, and there are differences between people. The results are shown in As can be seen from Dance language, dance music, and stage art are used for dance teaching training test, and the accuracy of BPNN evaluation model and PCA-BPNN evaluation model is tested. The experimental results are shown in Figures It can be seen from Figures Dance teaching should take the high unity of dance knowledge and skills and practical application as the ultimate goal. Therefore, it is necessary to increase the learning contents based on students' personal experience, such as practical practice, free operation, and social practice. Interactive teaching and learning tools must be used in classroom teaching, and the teaching mode of “I jump, you see, I say, you do, stick, mirror and floor” cannot be used. This paper studies dance teaching based on artificial intelligence technology. In this paper, BPNN algorithm and PCA-BPNN algorithm are used to test the dance teaching training of dance language, dance music, and stage art. The average accuracy of BPNN evaluation model is 85.35% when the time reaches 80, while the average accuracy of PCA-BPNN evaluation model is 65.64%, This shows that the accuracy of BPNN evaluation model is higher than that of PCA-BPNN evaluation model. In the classroom teaching under artificial intelligence technology, we should not only emphasize the practical practice of movement skills, but also pay attention to cultivating students' practical awareness of the application of dance knowledge and skills, put the focus on the practical situation, and create rich, diverse, vivid, and interesting practical activities, so that students can realize that dance learning is not only mastering ready-made standardized movement skills in the independent activities they are interested in. More importantly, enhance the operational ability of applying knowledge and skills to practice. Therefore, it can be concluded that the application of artificial intelligence in the field of dance teaching not only increases the interest of dance teaching, but also enriches the content of dance teaching, which provides favorable conditions for reducing the teaching pressure of teachers, improving the efficiency of dance teaching and promoting the all-round development of students.


To the best of our knowledge, there are no standard accountability measures for diversity efforts at the departmental level. Therefore, the purpose of this study is to evaluate a multiprong report card as a structure for evaluation, tracking, and reporting as well as to examine any relationships between expenditures and outcomes.We instituted an intervention that offered leadership a report card of metrics related to diversity efforts. Included are diversity expenditures, benchmark demographic and departmental data, applications to support faculty salaries, participation in clerkship programs focused on attracting diverse candidates, and requests for candidate lists. The purpose of this analysis is to demonstrate the impact of the intervention.p<0.001). An association was also found between total expenditures and URM representation in a department . Other outcomes include the following: (1) women, URM, and minority faculty have all increased in representation since tracking began; (2) diversity expenditures and faculty opportunity fund and presidential professorship applications have increased over time; and (3) a steady decline in departments with zero URM representation after the tracking of diversity expenditures in both clinical and basic science departments.A significant relationship was found between faculty funding applications and under-represented minority (URM) representation in a department (0.19; confidence interval [95% CI] 0.17–0.21; Our findings suggest that standardized metrics for inclusion and diversity initiatives promote accountability and buy-in from executive leadership. Departmental detail enables tracking of progress longitudinally. Future work will continue to evaluate the downstream effects of diversity expenditures. There exist many diversity initiatives, but a thorough literature search has not shown a standardized method of evaluating these efforts. As a component of institutional stewardship, we must be able to analyze where a program has been, where it is headed in the future, the type of impact, and ultimately help determine if goals are being met.1Accountability is a means by which institutional culture can change.2 These include recruitment, retention,3 exit interviews, advancement, and workforce representation. One area of focus that deserves more attention is metrics on expenditures, and other concrete indicators of departmental investment related to inclusion and diversity efforts. Research around these programs5 has not focused on analyzing relationships between initiatives and outcomes, with the exception of bias training.This level of accountability specifically around inclusion and diversity is largely absent from academic medicine literature. Academic institutions and hospital systems have outlined various metrics to increase inclusion and diversity.7 the connection to spending is not directly addressed.5 To the best of our knowledge, little is known about the intersection of department expenditures and diversity. Further understanding of how expenditures play a role in departments provides a unique opportunity. Therefore, the purpose of this study was to evaluate the effectiveness of these metrics in assessing the relationship between diversity expenditures and increased faculty diversity.While there are some analyses making the business case for diversity and discussing the financial benefits to corporations and institutions of these efforts,7 To accomplish this goal, we developed this report card. The tracking of diversity-related efforts is a component of the annual department chair review process.Our intervention aims to systemize and track departmental-level efforts in diversity to ensure accountability across the institution. Previous literature that has examined evaluating inclusion and diversity programs in business organizations indicates the importance of metrics to improve those programs and to facilitate buy-in at the executive level.Before the creation of the report card, there was not a fine-grained longitudinal examination of these efforts included in the annual department chair reviews. The report card does not distinguish between programs that existed previously and those that are newly created. It is provided to the Dean of the Perelman School of Medicine before the annual department chair reviews. This includes comparisons of all clinical departments and all basic science departments. This information is shared with the department chairs rather than directly with any group of under-represented minority (URM) faculty, and the expenditures report is filled out by the department chair in concert with their business office. The primary focus of these report cards is to have longitudinal data and accountability for the department leadership.The University of Pennsylvania instituted reporting of expenditures related to inclusion and diversity in fiscal year (FY) 2011 in conjunction with Penn's Diversity Action Plan and Budget Initiative. The first Institutional Diversity Expenditures Budget to include school-level expenditures was developed in FY 2013. The same year, the report was revised to contain department-level data. The original purpose of the diversity expenditures report was to track the school-level efforts in different diversity activities across time. Department-level data were added shortly after. There were no predetermined outcomes being tracked.The Penn Compact 2020, launched in 2013, focused on the following principals: Increase Access, Integrate Knowledge, and Engage Locally, Nationally, and Globally, at the same time that school-level diversity expenditures began to be reported. The reporting template was redesigned in FY 2014, and the production of the report became a joint effort between Faculty Affairs and Professional Development (FAPD) and the Office of Inclusion, Diversity, and Equity (OIDE) in the Perelman School of Medicine. Beginning in FY 2017, OIDE developed a system of metrics including the diversity expenditures to be used in annual department reviews.Departmental-level data included in the diversity expenditures report are broken down into the following categories: (1) faculty retention, (2) diversity search advisors, (3) faculty searches, (4) faculty leadership and mentorship, (5) conferences, (6) community and global outreach, (7) and pipeline programs . These m8 and diversity of the residency program. These data allow each department to evaluate itself by demographics on a national level. URM includes Black, Hispanic/Latinx, Native American, and Pacific Islander. Minority includes these categories and additionally includes Asian faculty.Faculty demographics for the department and in comparison, to the rest of the medical school, are provided to the chair and the Dean as part of the report card. These data also include the average demographics of the specialty by rank from the American Association of Medical College's (AAMC) U.S. Medical School Faculty Report, most recently published with 2019 data,In addition to diversity expenditures, the evaluation of departments includes applications for two university funds for diverse candidates: (1) faculty opportunity fund (FOF) and (2) presidential professorships (PPs) . The FOFBoth sources of funding are tools to support department recruitment of diverse candidates, by defraying a portion of the salary cost. As a result, we used the number of FOF and PP applications as the metric. The submission of applications indicates a level of commitment and engagement on the part of the department; therefore, we used this as the metric rather than the number of approved applications. OIDE and FAPD actively reach out and provide support to departments regarding applications for these two university-funded programs. This information has been included in the report card since its inception.Departments have the option to request lists of diverse faculty candidates from OIDE, a program that is itself novel among academic medical centers. Departments supply OIDE with the track, rank, specialty, and any other critical information about the position, and a list of candidates is supplied from peer institutions and institutions that graduate high numbers of URM physicians.OIDE examines public pages of faculty at peer institutions and institutions, which produce higher numbers of URM doctors, and creates a list based on departmental criteria. This information is not provided to OIDE by the external institution. This service has been available to departments since FY17. The number of requests made is included in the annual reporting. Requests are made by rank, track, and specialty. The requests have been part of the report card since FY18.The Perelman School of Medicine URM Visiting Clerkship Program is a program developed by the Alliance for Minority Physicians partnered with the University of Pennsylvania Health System and The Children's Hospital of Pennsylvania (CHOP) in FY11. This program offers fourth-year medical students, nationally, the opportunity to explore rotations at CHOP and many of the Penn-affiliated hospitals. Students have access to mentorship with residents and faculty, funding for their clerkship, and the chance to meet with program directors. This program is a component of the overall evaluation, and participation in this program has been noted as a metric in the report card since FY17.When preparing documentation for external review of the department, the average demographics of the specialty by rank, demographics of the department by rank, and diversity of the residency program are provided to the committees, in addition to the expenditure and funding application data. External department reviews take place every 5–7 years, by a committee selected by the Dean.9The schools are periodically reviewed by a committee appointed by the President and Provost. This meets the Middle States requirements for continuing accreditation, which states, “The institution's planning processes, resources, and structures are aligned with each other and are sufficient to fulfill its mission and goals, to continuously assess and improve its programs and services, and to respond effectively to opportunities and challenges.”Our analysis had three main objectives: first, we determined distributions of each characteristic of our report card and outcomes of interest. Second, we compared trends for these measures across years for all available data. Third, we evaluated associations between our report card components and the number of URM faculty. We tabulated and compared the distributions  of each element for basic science and clinical departments, using the nonparametric Wilcoxon rank-sum tests. In our adjusted analyses, our dependent variable was URM faculty count.p-values and confidence intervals (95% CIs) are reported for all statistical tests, with p<0.05 considered statistically significant.Our key independent variables included the following: (1) diversity expenditures; (2) FOFs and/or PPs (FOF/PP); (3) department type ; and time trend (FY16 through FY19). We estimated the relationship between our independent and dependent variables using negative binomial regression models to account for overdispersion in the URM faculty count outcome and generalized estimating equations with an exchangeable correlation structure to account for covariate clustering by year. Two-tailed Although the basic science departments are smaller than clinical departments, the representation of diverse candidates was noted to be not statistically significant between these two groups . Howeverp<0.001).The number of departments with no URM faculty has decreased since tracking began in FY11 , with adp<0.001) after adjusting for year (FY17 and FY18) and department type. Clinical departments have shown a sustained interest in resources such as the faculty candidate database and FOF and PP, while the basic science departments noticeably increased their requests in FY19 (In addition, there was a significant association between total expenditures and the number of URMs in a department (0.002; 95% CI 0.002–0.003;  in FY19 .10 found that clinical departments observed significantly more change in URM faculty compared with basic science departments.Despite basic science departments' low engagement with the faculty candidate database, they have shown an increase in applications . It shou4 However, the primary metric used to evaluate them in academic centers, if any, is student and faculty demographics,1 which is not tied directly to accountability for department leadership. Demographic change is an important objective, but it does not measure all efforts equally, and it may discourage programmatic initiatives that are not demonstrating an immediate yield.Inclusion and diversity initiatives are becoming more common in medical schools across the country.It can also be challenging to tie a specific initiative directly to increased diversity, as demographics are a lagging indicator. Using a method of evaluation that accounts for effort means that schools can fairly assess a department's commitment to change. The data are also supplied to the external committees during the regular process of department reviews. This high-level review is not in place in any other academic medical institution, to the best of our knowledge.We acknowledge some limitations to our study. Our findings may not be nationally generalizable, given that these are data obtained from one institution. However, the data included all departments in the medical school, over a period of 4 years, which allows us to see larger trends without overemphasis on any given specialty. Another limitation is that only 4 years of data were available for analysis, which is challenging given that results related to pipeline and outreach efforts can take years to manifest. Because pipeline programs and outreach can start quite early, effects may not be truly visible for quite some time.Rather than attempting to measure the effectiveness of these programs, we examine the commitment of the department to these programs. However, using the same format for reporting revealed consistent results and a strong framework to use for further evaluation. Finally, to our knowledge, this analysis is the first of its kind at an academic center, and provides a holistic evaluation of inclusion and diversity efforts.The report card does not include funding used for patient care, but the expenditures report does include pipeline programs and outreach, as well as an “other” category left to the department's discretion, which may include educational spending.Metrics were developed to track expenditures related to inclusion and diversity efforts over time. These metrics have been successfully used in the review of departments, both internally and externally. Using the same metrics across all departments allows us to identify leaders and offer assistance where it is most needed. Developing metrics that expanded upon the original expenditure tracking program creates a broader view of the efforts made by departments. The addition of these data in the annual chair evaluations has created an opportunity for an ongoing conversation with departmental leadership about the importance of all their inclusion and diversity programs, and the recruitment and retention of diverse faculty.The school has a way to hold the departments accountable, which is transparent to the department chairs. It is important to include metrics that are tied to efforts, rather than only to outcomes. By doing so, we recognize the work being done in departments that may not yet have reached their demographic goals but are investing in long-term diversity.Our study reveals promising results utilizing metrics to assess the effectiveness of diversity and inclusion efforts, from a financial standpoint. With a standardized approach, we can better track our progress and highlight areas that need improvement. The strategic plan includes continuing to collect these data every year, and analyze trends in the relationship between expenditures and increased diversity as well as continuing to evaluate the categories of reporting, and to ensure that departments are using the classifications consistently so that comparison is possible.In addition, pipeline programs will have the opportunity to bear fruit, and use this blueprint to examine costs and outcomes related to their program development. Fundamentally, we have found that measuring actions rather than outcomes indicates a connection between those efforts and increased URM faculty. We have the opportunity to encourage further efforts, to acknowledge the current efforts in departments, and to demonstrate the importance of institutional buy-in and support.


The COVID-19 pandemic cast a dramatic spotlight on the use of data as a fundamental component of good decision-making. Evaluating and comparing alternative policies required information on concurrent infection rates and insightful analysis to project them into the future. Statisticians in Israel were involved in these processes early in the pandemic in some silos as an ad-hoc unorganized effort. Informal discussions within the statistical community culminated in a roundtable, organized by three past presidents of the Israel Statistical Association, and hosted by the Samuel Neaman Institute in April 2021. The meeting was designed to provide a forum for exchange of views on the profession’s role during the COVID-19 pandemic, and more generally, on its influence in promoting evidence-based public policy. This paper builds on the insights and discussions that emerged during the roundtable meeting and presents a general framework, with recommendations, for involving statisticians and statistics in decision-making. The COVID-19 pandemic posed significant challenges to the health, economic and social systems around the globe. Faced with a crisis at a scale not experienced in the last century, policy makers were required to make difficult decisions on a daily basis. Ideally, such policy decisions should be based on properly analyzed data and subject knowledge. Specifically, decision makers should seek actionable information coming from robust and rigorous analysis of accurate and relevant data. In reality, specific policy choices varied widely, from the airtight seal in New Zealand to the lockdowns in Europe and the laisse faire attitude of Brazil. Countries veered from one policy to another. For example, Britain adopted tight lockdowns only after a limited social distancing campaign was accompanied by heavy infection and burden of disease. In Israel, infections waned following a tight lockdown, but returned to much higher levels several months after most restrictions were removed.As a pandemic unfolds, new dilemmas arise. Depending on the state of emergency, tools being used and depth of analysis will vary. But in any case, a volatile reality as manifested in crisis times requires a well-founded and robust approach for data-driven decision making. The ability to rapidly obtain high quality data and to analyze it, in a timely manner, cannot be taken for granted. In fact, in many countries there is uncertainty about the most basic issue—how many people died as a result of COVID-19 infection. In Russia, for example, “data-based” estimates have suggested that the true death count may be twice as high as the official count .statistical thinking leading to sound statistical analysis and uncertainty quantification. Such skills are at the foundation of the statistical profession and therefore should be represented and executed by statisticians [Successfully addressing challenges, like those mentioned above, requires among other things, sticians . MoreoveFirst and foremost, statistical thinking has a role in the top floor where strategic plans are made. We would like to stress that statisticians should be involved in designing the strategic view of quality control, data collection and data management. The implementation of a specific strategy is at the tactical tier, and there too statisticians should be active as written in the sequel. Indeed, well explained statistical thinking can guide strategic discussions and point out promising directions, for example, recommending more efficient designs for data collection to support data-driven decision-making. At an early stage, statisticians should be involved in efforts to ensure quality control of data collection and data management. Tools from statistical process control can play a valuable role in such efforts. This is a crucial stage where an appropriate implementation according to best practices of data science will affect all future analyses. Clearly, statisticians can assume a leading role at the tactical and operational floors, where data collection and analyses are executed, and insights are generated. It is a stage where statisticians should be involved in analyzing the data. Insights need to be clearly and accurately presented to decision makers at the top floor.In the industrial context, Deming  recommenThe structure of the rest of the paper is as follows. We begin by presenting the objectives of the roundtable. Subsequent sections describe the evolution of statistical activities in the early stages of the pandemic, discuss methods and tools for statistical intervention and describe the role of a statistician in the context of a pandemic. The next section details how the role of statisticians transformed as the pandemic evolved. Our Conclusions section summarizes the main points raised in the paper, followed by several recommendations.What are the main challenges in data collection flow and in leveraging statistical expertise in the COVID-19 Pandemic?What are the expected benefits of using statistical analysis of COVID data?What are the main barriers in developing and using statistical analytics in the pandemic?How can we overcome these barriers?What provisions should be taken to facilitate access to relevant data?What is the proposed role of academia in general, and statistical expertise in particular, in developing data analytics knowledge and capabilities?What should be considered success stories in using statistical analytics of Corona related data to guide policy decisions?Acknowledging the potential benefit of sound statistical thinking in the decision-making process, a roundtable was organized by three past presidents of the Israel Statistical Association. Twenty colleagues from statistics and related areas participated in the event, which was hosted at the Technion by the Samuel Neaman Institute on 13 April 2021. The meeting was designed to provide a forum for discussion and exchange of ideas on the role that the profession should play in promoting evidence-based public policy and the way to deliver the information to decision makers. As a stimulus to discussion, the participants were invited to address questions such as:https://www.neaman.org.il/EN/Statistical-analysis-of-Corona-data-A-Roundtable.A report (in Hebrew) summarizing the round table is available at The evolution of statistical activity as the pandemic unfolded.The methods and tools for statistical intervention in decision-making—how to take the results of a statistical analysis and apply them to affect policy.The role of statisticians and of statistics as a profession in a pandemic and more generally, how the discipline can aid in improving public policy.The topics raised in the roundtable can be grouped into the following three categories:This paper builds on the roundtable discussions and comments, providing several recommendations of general relevance to crisis management and public policy. The paper is organized according to the three categories mentioned above and aims to highlight the best way to leverage statisticians and statistics within the complex reality of emergency decision making. We take a holistic point of view, acknowledging that for statistics to have a significant impact, it must be actively represented in each of the decision-making domains.In March 2020, the COVID-19 pandemic rapidly escalated from a minor news item about a virus outbreak in China to a full-blown national emergency. The Israeli government instituted a strict lockdown in an attempt to reduce spread of the virus. At this stage, the Israeli medical system needed diagnostic tools, hospitalization procedures and treatment protocols, as the magnitude of the pandemic and its requirements from the medical system were not well understood . Policy The dynamic nature of the pandemic brought with it a changing landscape of challenges; the role of statisticians and biostatisticians evolved accordingly. In the hope of mitigating the dramatic impact of COVID-19 on society and the health and economic systems, it was important to address questions such as: How many individuals are infected with SARS-Cov2 virus? How many new infections are likely to occur in the week(s) ahead? What was the economic impact of a lockdown vs. no-lockdown decision? What was the effect of a lockdown on mortality from COVID-19? Do we need to implement new triage procedures that will be effective in preventing transmission of SARS-CoV-2 to patients and healthcare workers ? Health,During the first phase of the pandemic, statisticians were involved in developing infrastructure, including data collection procedures and analysis approaches. There were some instances of direct involvement of statisticians in decision-making. Often, though, analyses by other scientists, sometimes with limited training and experience in statistics, were the basis for decisions. Examples of such constructive links between the statistical community and policy makers were provided by two important resources for the Ministry of Health: the statistical unit at the Gertner Institute for Epidemiology and Health Policy Research and the National Institute for Health Policy Research (NIHP).These institutes and divisions are directed by statisticians or statistically savvy epidemiologists and include statisticians on their professional staff. Early in the pandemic, around March 2020, the Gertner Institute formed and coordinated a group of volunteers that included statisticians, data scientists and mathematicians. The group modeled the unfolding of the pandemic within the population using various mathematical and statistical tools and provided the Ministry of Health with estimates of the daily effective reproduction number. The group members focused daily on a variety of aspects of the pandemic such as: informing triage procedures and implementation using survival analysis; assessing treatment efficacy and social interventions using causal analysis; making fast, real-time decisions, such as recommending the number of ventilation machines required, combining applied probability and operations research approaches; supporting daily activities such as monitoring models and forecasts, and results of interventions by building informative, dynamic, and interactive dashboards; providing uncertainty assessment for all resulting numbers and recommendations.Statistical input was important in guiding data collection. One example relates to the data from the labs carrying out PCR testing. Statisticians from the Gertner Institute stressed the need to report the age of tested subjects, which proved extremely useful for assessing the nature of the disease. A second concerns the data needed to support triage in ICU’s. Statisticians working with this problem highlighted the need for data on sojourn times, i.e. the length of time spent in different processing and disease stages, which are essential for modeling and prediction.Much of the necessary data was collected from the health care providers in Israel, with supervision from their epidemiological research divisions. One of the efforts was to design and build a repository for storing COVID-19 data. Many challenges arose, such as the low reliability of diagnostic tools and the lack of unique criteria for defining variables (even the definition of “SARS-CoV-2 positive” was not unique and comparable). Indeed, there are numerous organizational units that collect and store data and are responsible for making them accessible. These groups include HMOs and hospitals, which use different data entry and storage platforms. In Israel, the collection and fusion of data from different sources is unique and has great potential. Efforts in this area are facilitated by the Israeli Ministry of Health. However, better standards would further improve data quality and rapid availability. Data often have inconsistent formats, making fusion difficult.The NIHP acted quickly and in April 2020 issued a call for ‘COVID-19-specific’ research projects. After a rapid yet efficient review process about 20 projects were funded, the majority of which are based on collaboration with statisticians and/or epidemiologists. The NIHP also held a series of meetings  where policy makers, leaders of the health services and researchers, including statisticians and data scientists, discussed various aspects of the pandemic such as challenges associated with obtaining suitable data for decision making and research and the characteristics of the dominant models used for predicting the development of COVID-19 in Israel. The head of the NIHP served as the statistician in a multidisciplinary academic group working on children and coronavirus which had substantial policy inputs [The Israel Central Bureau of Statistics (CBS), together with the Ministry of Health (MoH) and the Gertner Institute, designed serological surveys in the heavily infected city of Bnei Braq to assess the extent and patterns of the infection. The CBS, MoH and the Gertner Institute applied scientific sampling methods to obtain representative samples, and also analysis methods that adjusted for the biasing effects of nonresponse, which are unavoidable in such surveys. The CBS was also at the forefront in providing timely data on the economic and social impact of COVID-19, launching four dedicated surveys of households, and 11 surveys of businesses in Israel. Timelines were accelerated to guarantee rapid availability of data to other government offices. Several standard reports were issued at double the usual frequency  to enhance timeliness. Difficulty in obtaining data required application of more sophisticated statistical methods, including imputation and weighting, and the use of data from other sources .Statisticians were also involved in analyzing epidemiological data, clinical data, and data from basic research . One stuStatisticians contributed to efforts to find a modelling approach that would support extrapolation of information on epidemiological parameters such as mortality, fatality rates, incidence rates and predictors of disease evolution , 16. TheThe data science approach is designed to support data-driven decision making. For decision making, whether in government, industry or academia, a data-driven approach should be taken in a context and therefore starts with a well-defined subject matter in mind. Appropriate and informative data must be collected or relevant data sources identified . SeveralThe data science approach is represented by a life cycle perspective , see FigFigure R0) was crucial from the beginning of the pandemic, while understanding the effect of vaccines and planning successful vaccination strategies accordingly was relevant later on. Answering such scientific questions requires reliable data collection, prompt data availability and sound statistical analysis. Statisticians interested in providing insight through data analysis often found that the necessary data were not accessible, even in the context of protected data rooms.For example, estimating the reproduction number , with an excellent track record in official statistics, was involved in questions of survey design. The serological survey in Bnei Braq, with many interesting and useful outcomes, was a noteworthy example. However, plans by the CBS to carry out national surveys to assess the spread of the pandemic, jointly with the Ministry of Health, never came to fruition. The CBS was also not involved in many other issues where its experts could have contributed.Communication, both with decision makers and with the public, may have played a role. Statisticians excel in describing uncertainty, but that is often difficult to fully appreciate, especially by those who want a single sharp answer. The “limelight” factor may also have played a role, with members of the statistics community largely shying away from intense public exposure. Other professions also complained that their voice should have received more attention, but often with spokespersons who were very active in the media .Statisticians often emphasize the need for thorough analysis, carefully examining all assumptions. There are many advantages to this approach. However, it may have made conventional statistical work too slow to meet the need for rapid analysis and response in emergency situations like a pandemic. That is unfortunate, as sharp statistical thinking and clear guidance are important in crisis times that require decisions in the face of huge uncertainty. Trust in data and its interpretation is a critical element in providing a clear and logical rationale underlying decisions in an emergency event like COVID-19. Without trust, compliance with instructions to the public is low, affecting the success of any policy.On a deeper scientific level, the assessment of evidence should be considered a key competency of statistical work . In a moThe situation in Israel during the first year of the pandemic was not altogether different from that in other countries. In Italy, statisticians were on the front lines of communication and became regular members of television news panels reporting on COVID-19. At the same time, the lack of data was lamented, and 1400 statisticians and researchers signed a petition to make COVID-19 data accessible. A special meeting of the Italian statistical society was held to deal with COVID-19 issues. Fricker and Rigdon  and FricIn a recent and closely related paper, Ellenberg and Morris  provide The lessons above pose challenges and call for action. We urge the statistical community in Israel and the nation’s policy makers to face them head on by taking steps to leverage the unique skills and experience of statisticians to influence policy in the future. In particular, there is a need for statisticians to be actively involved in all floors of the decision-making pyramid, starting with its most important top strategic floor Fig. . FurtherThree lessons from the COVID-19 experience seem particularly cogent. (1) The statistical community in Israel was not part of the inner circles of advice and influence during much of the pandemic. (2) To influence leaders in an emergency, experts need to build confidence and establish working relationships during periods of normalcy. (3) Statisticians should adopt a “push” strategy rather than waiting to be asked for help.The fourth wave of COVID-19 infection in Israel began at the end of June 2021. During that wave, a dramatic change occurred in the role filled by statisticians in support of evidence-based decision-making by the Israeli Ministry of Health. The group of statisticians, data scientists and mathematicians coordinated by the Gertner Institute, continued to analyze data and at this stage focused on the Israeli vaccination campaign. The fact that this work was done in collaboration with high official members of the Ministry lent it major importance in setting policy during Wave 4.The first issue was to compare the protection of individuals who recovered from COVID-19 to others, both unvaccinated and vaccinated. The statistical analysis revealed that recovered individuals are protected in a similar fashion to individuals recently vaccinated with two doses . An updaThe most influential project was to identify the waning immunity of the BNT162b2 vaccine during the fourth wave. Careful comparison of the rate of infection as a function of vaccination time demonstrated that immunity against the Delta variant, which dominated the fourth wave, wanes in all age groups 6 months after the second vaccine dose . The staPartnering with the CBS has also accelerated and expanded to conditions beyond COVID-19. The Ministry of Health hopes to run, with the methodological support of the CBS, a national non-probability survey to estimate the prevalence of viral disease in the Israeli population.Given complex situations such as the one we observed during the COVID-19 pandemic, data-driven policies are essential. This requires access to data that is actionable. In addition, data-based policy recommendations that can support decision makers such as government ministers, hospital managers, education system administrators and economic system leaders require the use of validated models that can be used for analyzing the evolution of the pandemic, the effects of interventions and for predictions.The challenge of reaching well informed decision-making stretches far beyond having the “right”, high quality data or good models. A successful data-driven approach to decision-making requires efficient multidisciplinary collaboration which in turn, depends crucially on having trust and common language between disparate domains. This is true not just for supporting the Ministry of Health, but also for other government branches such as the Ministry of Finance, the Ministry of Economy and Industry, the Ministry of Labor, Social Affairs, and Social Services, and the Ministry of Justice. All seek or should seek data-driven decision making and all are relevant in crisis times as the one we experienced recently. This is possible only when there are already information systems in place for collecting, storing and presenting data. It requires pipelines for connecting data from diverse sources for joint analysis. Data collected from different sources need to be structured and definitions must be consistent and agreed upon, and relate to the same or at least similar time periods. Building a common language takes time and should start immediately.Thus, good channels of communication are essential for reliable data and insightful analyses to reach the right eyes and ears and have an essential influence on action. Moreover, adopting a data science life cycle point of view has great potential in establishing efficient multidisciplinary working procedures that naturally lead to well informed decision-making. Therefore, practicing daily collaboration of multidisciplinary teams in “normal” times will have a huge benefit in crisis time, resulting in seamless execution of data-driven decision-making thanks to the trust and common language built along the way. The academic statistical community, the Central Bureau of Statistics, the Gertner Institute and the National Institute for Health Policy Research are already geared to such multidisciplinary working procedures, but other ministry and public organizations need to join in.Lobby for active participation of statisticians in decision-making forums.Establish a pool of statisticians from academia and industry, as well as of other experts, that will work routinely in normal times in multidisciplinary teams together with policy decision makers. This requires the establishment of a policy committee that can serve as a contact point between decision makers who seek evidence-based policy and statisticians who are interested and ready to share their knowledge and expertise.Improve the organization and allocation of statistical tasks in the face of a health emergency. This could also be handled by the policy committee.Government ministries and other public agencies can fund scholarships for students to further support such an initiative, for example within the framework of the successful Science and Policy Fellowship Program Mimshak .Establish national standards for health records, including treatment of free text entries.Educate stakeholders to appreciate the importance of standards and statistical thinking and analysis.Hold professional meetings to discuss challenges and debate possible solutions.Enhance presence in the media, where statistical expertise must be brought to the public in language that is understandable to a broad audience. “Push” rather than wait to be called.Nature “model manifesto” [Discuss the nifesto”  and the nifesto” .Train the next generation of statisticians and data scientists to have the needed skills  to aid in decision-making during crisis.These recommendations are presented with a forward-looking perspective. The role of statisticians is crucial in the evaluation of the impact of COVID-19 on the population and in preventing a resurgence of new COVID-19 waves. Many statistical issues are critical in the analysis of public health data; consequently, statisticians should have access to such data. Healthcare systems, in general, should be prepared to absorb a sudden deterioration of patients in areas such as chronic pathologies, which could result from cure avoidance or postponement of screening examinations. For example, careful evaluation and comparison of historical data with actual data, based on statistical predictive models is needed. Statisticians should be essential partners in implementing data-driven public health due to their knowledge of sampling strategies; this is an essential starting point in building statistical expertise and is needed in designing surveillance systems, which depend heavily on sampling strategies to provide early alert to the onset of infectious diseases and to map indices of disease spread , 40.In conclusion, effective use of statistics emerges as crucial in times of crisis and in particular, for evaluating the impact of COVID-19 on the population and limiting the impact of new COVID-19 waves. We fully agree with Ellenberg and Morris  and thei