Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective.

In Industry 4.0, smart manufacturing is facing its next stage, cybermanufacturing, founded upon advanced communication, computation, and control infrastructure. Cybermanufacturing will unleash the potential of multi-modal manufacturing data, and provide a new perspective called computation service, as a part of service-oriented architecture (SOA), where on-demand computation requests throughout manufacturing operations are seamlessly satisfied by data analytics and machine learning. However, the complexity of information technology infrastructure leads to fundamental challenges in modeling and analysis under cybermanufacturing, ranging from information-poor datasets to a lack of reproducibility of analytical studies. Nevertheless, existing reviews have focused on the overall architecture of cybermanufacturing/SOA or its technical components (e.g., communication protocol), rather than the potential bottleneck of computation service with respect to modeling and analysis. In this paper, we review the fundamental challenges with respect to modeling and analysis in cybermanufacturing. Then, we introduce the existing efforts in computation pipeline recommendation, which aims at identifying an optimal sequence of method options for data analytics/machine learning without time-consuming trial-and-error. We envision computation pipeline recommendation as a promising research field to address the fundamental challenges in cybermanufacturing. We also expect that computation pipeline recommendation can be a driving force to flexible and resilient manufacturing operations in the post-COVID-19 industry.

The reproducibility of COVID-19 data analysis: paradoxes, pitfalls, and future challenges.

In the midst of the COVID-19 experience, we learned an important scientific lesson: knowledge acquisition and information quality in medicine depends more on "data quality" rather than "data quantity." The large number of COVID-19 reports, published in a very short time, demonstrated that the most advanced statistical and computational tools cannot properly overcome the poor quality of acquired data. The main evidence for this observation comes from the poor reproducibility of results. Indeed, understanding the data generation process is fundamental when investigating scientific questions such as prevalence, immunity, transmissibility, and susceptibility. Most of COVID-19 studies are case reports based on "non probability" sampling and do not adhere to the general principles of controlled experimental designs. Such collected data suffers from many limitations when used to derive clinical conclusions. These include confounding factors, measurement errors and bias selection effects. Each of these elements represents a source of uncertainty, which is often ignored or assumed to provide an unbiased random contribution. Inference retrieved from large data in medicine is also affected by data protection policies that, while protecting patients' privacy, are likely to reduce consistently usefulness of big data in achieving fundamental goals such as effective and efficient data-integration. This limits the degree of generalizability of scientific studies and leads to paradoxical and conflicting conclusions. We provide such examples from assessing the role of risks factors. In conclusion, new paradigms and new designs schemes are needed in order to reach inferential conclusions that are meaningful and informative when dealing with data collected during emergencies like COVID-19.

The role of statisticians in the response to COVID-19 in Israel: a holistic point of view.

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.

Integrated Analysis of Behavioural and Health COVID-19 Data Combining Bayesian Networks and Structural Equation Models.

The response to the COVID-19 pandemic has been highly variable. Governments have applied different mitigation policies with varying effect on social and economic measures, over time. This article presents a methodology for examining the effect of mobility restriction measures and the association between health and population activity data. As case studies, we refer to the pre-vaccination experience in Italy and Israel. Facing the pandemic, Israel and Italy implemented different policy measures and experienced different population behavioral patterns. Data from these countries are used to demonstrate the proposed methodology. The analysis we introduce in this paper is a staged approach using Bayesian Networks and Structural Equations Models. The goal is to assess the impact of pandemic management and mitigation policies on pandemic spread and population activity. The proposed methodology models data from health registries and Google mobility data and then shows how decision makers can conduct scenario analyses to help design adequate pandemic management policies.