New Control Chart Based On Neutrosophic Maxwell Distribution with Decision Making Applications

The neutrosophic approach is a potential area to provide a novel framework for dealing with uncertain data. This study aims to introduce the neutrosophic Maxwell distribution (M̃D) for dealing with imprecise data. The proposed notions are presented in such a manner that the proposed model may be used in a variety of circumstances involving indeterminate, ambiguous, and fuzzy data. The suggested distribution is particularly useful in statistical process control (SPC) for processing uncertain values in data collection. The existing formation of VSQ-chart is incapable of addressing uncertainty on the quality variables being investigated. The notion of neutrosophic VSQchart (Ṽ SQ) is developed based on suggested neutrosophic distribution. The parameters of the suggested Ṽ SQ-chart and other performance indicators, such as neutrosophic power curve (P̃C), neutrosophic characteristic curve (C̃C) and neutrosophic run length (R̃L) are established. The performance of the Ṽ SQ-chart under uncertain environment is also compared to the performance of the conventional model. The comparative findings depict that the proposed Ṽ SQ-chart outperforms in consideration of neutrosophic indicators. Finally, the implementation procedure for real data on the COVID-19 incubation period is explored to support the theoretical part of the proposed model © 2023,Neutrosophic Sets and Systems. All Rights Reserved.

Tolerance limits for mixture-of-normal distributions with application to COVID-19 data

Tolerance intervals (TIs) are widely used in various applications including manufacturing engineers, clinical research, and pharmaceutical industries. TIs can be used to construct limits of control charts for monitoring quality characteristics. For manufacturing processes where multiple factors may contribute to defects or multiple-stream processes, a mixture distribution of several suitable probabilistic models may be a better choice than a simple distribution for modeling the data. TIs for the normal mixture distribution have been studied in the literature. This article reviews the TIs of the normal mixture distribution, the applications of the mixture distribution, and the control charts of the mixture distribution. A rule for constructing modified two-sided TIs of the normal mixture distribution is summarized, and this rule may be extended to construct modified two-sided TIs for general mixture distributions. The feasibility of using TIs to build control charts for mixture distributions is also discussed. A real data example of coronavirus disease 2019 is used to illustrate the method by linking the TI to control charts. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification. © 2023 Wiley Periodicals LLC.

Pandemetrics: systematically assessing, monitoring, and controlling the evolution of a pandemic.

The still ongoing pandemic of SARS-CoV-2 virus and COVID-19 disease, affecting the population worldwide, has demonstrated the need of more accurate methodologies for assessing, monitoring, and controlling an outbreak of such devastating proportions. Authoritative attempts have been made in traditional fields of medicine (epidemiology, virology, infectiology) to address these shortcomings, mainly by relying on mathematical and statistical modeling. However, here, we propose approaching the methodological work from a different, and to some extent alternative, standpoint. Applied systematically, the concepts and tools of statistical engineering and quality management, developed not only in healthcare settings, but also in other scientific contexts, can be very useful in assessing, monitoring, and controlling pandemic events. We propose a methodology based on a set of tools and techniques, formulas, graphs, and tables to support the decision-making concerning the management of a pandemic like COVID-19. This methodological body is hereby named Pandemetrics. This name intends to emphasize the peculiarity of our approach to measuring, and graphically presenting the unique context of the COVID-19 pandemic.

Efficient algorithms for real-time syndromic surveillance.

OBJECTIVE: Outbreaks of influenza-like diseases often cause spikes in the demand for hospital beds. Early detection of these outbreaks can enable improved management of hospital resources. The objective of this study was to test whether surveillance algorithms designed to be responsive to a wide range of anomalous decreases in the time between emergency department (ED) presentations with influenza-like illnesses provide efficient early detection of these outbreaks. METHODS: Our study used data on ED presentations to major public hospitals in Queensland, Australia across 2017-2020. We developed surveillance algorithms for each hospital that flag potential outbreaks when the average time between successive ED presentations with influenza-like illnesses becomes anomalously small. We designed one set of algorithms to be responsive to a wide range of anomalous decreases in the time between presentations. These algorithms concurrently monitor three exponentially weighted moving averages (EWMAs) of the time between presentations and flag an outbreak when at least one EWMA falls below its control limit. We designed another set of algorithms to be highly responsive to narrower ranges of anomalous decreases in the time between presentations. These algorithms monitor one EWMA of the time between presentations and flag an outbreak when the EWMA falls below its control limit. Our algorithms use dynamic control limits to reflect that the average time between presentations depends on the time of year, time of day, and day of the week. RESULTS: We compared the performance of the algorithms in detecting the start of two epidemic events at the hospital-level: the 2019 seasonal influenza outbreak and the early-2020 COVID-19 outbreak. The algorithm that concurrently monitors three EWMAs provided significantly earlier detection of these outbreaks than the algorithms that monitor one EWMA. CONCLUSION: Surveillance algorithms designed to be responsive to a wide range of anomalous decreases in the time between ED presentations are highly efficient at detecting outbreaks of influenza-like diseases at the hospital level.

The P-Chart Implementation in Evaluating the Number of New COVID-19 Cases

The COVID-19's rapidly spread in Indonesia is a serious concern for government. Various government policies, such as large-scale social restrictions, which is called as PSBB, and the imposing restrictions on community activities, be called as PPKM, have been applied to slow the spread of COVID-19. These policies were supposed to control people activities in many areas, especially that will invite the crowd happening. In this paper, the control charts be explored to obtain the overview of the daily new COVID-19 cases number, which is considered as a production process. The proportion of positive COVID-19 cases be observed and monitored using P-control chart. The data used is from the specimen COVID-19 test in DKI Jakarta Province, since April 2020 to January 2021. It is obtained that the process of daily new COVID-19 cases number has not been statistically controlled. It is detected some mean shifts of proportion in some point of times. Several findings indicate an upward trend, suggesting that the proportion of positive cases COVID-19 is rising. The public's conduct in Jakarta has served as a model for the government in designing policies to tackle the spread of Covid-19. However, due to the high mobility of Jakarta residents and its lack of consistency in the region, the COVID-19 process still remained out of control. © 2022 American Institute of Physics Inc.. All rights reserved.

Insights into Variations in COVID-19 Infections in Malaysia

The present study aims to visualize the variations in the number of confirmed daily COVID-19 infections during the third wave in Malaysia through the application of control charts. This study also attempts to propose the target number of daily new cases that would bring the pandemic situation in Malaysia under control by utilizing the confirmed daily cases in Malaysia starting from 8(th) September 2020 until 30(th) June 2021. A modified Shewhart control chart was adopted to monitor the variations before and after the commencement of National Immunisation Programme (NIP). The chart shows a declining trend in the number of cases after the rollout of NIP whereby several days were brought to a state of statistical-in-control. But in less than three months after NIP commencement, there were huge variations in COVID-19 cases leading to drastic increase in the mean number of cases. These signal the presence of unnatural or assignable causes of variations which could be attributed to failure of curbing the risks of transmission, existence of various variants in the community, easing of containment measures and less adherence to the COVID-19 Standard Operating Procedures (SOPs). Significant shifts in the mean values prompt the development of a 3-phase modified Shewhart control chart. From the 3-phase chart, a series of daily number new cases that can be set as the target value to bring the pandemic situation in Malaysia under control, while flattening the epidemiological curve in the very near term.

A hybrid Shewhart chart for visualizing and learning from epidemic data.

OBJECTIVE: As the globe endures the coronavirus disease 2019 (COVID-19) pandemic, we developed a hybrid Shewhart chart to visualize and learn from day-to-day variation in a variety of epidemic measures over time. CONTEXT: Countries and localities have reported daily data representing the progression of COVID-19 conditions and measures, with trajectories mapping along the classic epidemiological curve. Settings have experienced different patterns over time within the epidemic: pre-exponential growth, exponential growth, plateau or descent and/ or low counts after descent. Decision-makers need a reliable method for rapidly detecting transitions in epidemic measures, informing curtailment strategies and learning from actions taken. METHODS: We designed a hybrid Shewhart chart describing four 'epochs' ((i) pre-exponential growth, (ii) exponential growth, (iii) plateau or descent and (iv) stability after descent) of the COVID-19 epidemic that emerged by incorporating a C-chart and I-chart with a log-regression slope. We developed and tested the hybrid chart using international data at the country, regional and local levels with measures including cases, hospitalizations and deaths with guidance from local subject-matter experts. RESULTS: The hybrid chart effectively and rapidly signaled the occurrence of each of the four epochs. In the UK, a signal that COVID-19 deaths moved into exponential growth occurred on 17 September, 44 days prior to the announcement of a large-scale lockdown. In California, USA, signals detecting increases in COVID-19 cases at the county level were detected in December 2020 prior to statewide stay-at-home orders, with declines detected in the weeks following. In Ireland, in December 2020, the hybrid chart detected increases in COVID-19 cases, followed by hospitalizations, intensive care unit admissions and deaths. Following national restrictions in late December, a similar sequence of reductions in the measures was detected in January and February 2021. CONCLUSIONS: The Shewhart hybrid chart is a valuable tool for rapidly generating learning from data in close to real time. When used by subject-matter experts, the chart can guide actionable policy and local decision-making earlier than when action is likely to be taken without it.

Systems engineering analysis of diagnostic referral closed-loop processes.

BACKGROUND: Closing loops to complete diagnostic referrals remains a significant patient safety problem in most health systems, with 65%-73% failure rates and significant delays common despite years of improvement efforts, suggesting new approaches may be useful. Systems engineering (SE) methods increasingly are advocated in healthcare for their value in studying and redesigning complex processes. OBJECTIVE: Conduct a formative SE analysis of process logic, variation, reliability and failures for completing diagnostic referrals originating in two primary care practices serving different demographics, using dermatology as an illustrating use case. METHODS: An interdisciplinary team of clinicians, systems engineers, quality improvement specialists, and patient representatives collaborated to understand processes of initiating and completing diagnostic referrals. Cross-functional process maps were developed through iterative group interviews with an urban community-based health centre and a teaching practice within a large academic medical centre. Results were used to conduct an engineering process analysis, assess variation within and between practices, and identify common failure modes and potential solutions. RESULTS: Processes to complete diagnostic referrals involve many sub-standard design constructs, with significant workflow variation between and within practices, statistical instability and special cause variation in completion rates and timeliness, and only 21% of all process activities estimated as value-add. Failure modes were similar between the two practices, with most process activities relying on low-reliability concepts (eg, reminders, workarounds, education and verification/inspection). Several opportunities were identified to incorporate higher reliability process constructs (eg, simplification, consolidation, standardisation, forcing functions, automation and opt-outs). CONCLUSION: From a systems science perspective, diagnostic referral processes perform poorly in part because their fundamental designs are fraught with low-reliability characteristics and mental models, including formalised workaround and rework activities, suggesting a need for different approaches versus incremental improvement of existing processes. SE perspectives and methods offer new ways of thinking about patient safety problems, failures and potential solutions.

Patient-level and hospital-level variation and related time trends in COVID-19 case fatality rates during the first pandemic wave in England: multilevel modelling analysis of routine data.

BACKGROUND: A report suggesting large between-hospital variations in mortality after admission for COVID-19 in England attracted much media attention but used crude rates. We aimed to quantify these variations between hospitals and over time during England's first wave (March to July 2020) and assess available patient-level and hospital-level predictors to explain those variations. METHODS: We used administrative data for England, augmented by hospital-level information. Admissions were extracted with COVID-19 codes. In-hospital death was the primary outcome. Risk-adjusted mortality ratios (standardised mortality ratios) and interhospital variation were calculated using multilevel logistic regression. Early-wave (March to April) and late-wave (May to July) periods were compared. RESULTS: 74 781 admissions had a primary diagnosis of COVID-19, with 21 984 in-hospital deaths (29.4%); the 30-day total mortality rate was 28.8%. The crude in-hospital death rate fell in all ages and overall from 32.9% in March to 13.4% in July. Patient-level predictors included age, male gender, non-white ethnic group (early period only) and several comorbidities (obesity early period only). The only significant hospital-level predictor was daily COVID-19 admissions in the late period; we did not find a relation with staff absences for COVID-19, mechanical ventilation bed occupancies, total bed occupancies or bed occupancies for COVID-19 admissions in either period. Just 4 (3%) and 2 (2%) hospitals were high, and 5 (4%) and 0 hospitals were low funnel plot mortality outliers at 3 SD for early and late periods, respectively, after risk adjustment. We found no strong correlation between early and late hospital-level mortality (r=0.17, p=0.06). CONCLUSIONS: There was modest variation in mortality following admission for COVID-19 between English hospitals after adjustment for risk and random variation, in marked contrast to early media reports. Early-period mortality did not predict late-period mortality.

Monitoring scheme for early detection of coronavirus and other respiratory virus outbreaks.

In December 2019, an outbreak of pneumonia caused by a novel coronavirus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) began in Wuhan, China. SARS-CoV-2 exhibited efficient person-to-person transmission of what became labeled as COVID-19. It has spread worldwide with over 83,000,000 infected cases and more than 1,800,000 deaths to date (December 31, 2020). This research proposes a statistical monitoring scheme in which an optimized np control chart is utilized by sentinel metropolitan airports worldwide for early detection of coronavirus and other respiratory virus outbreaks. The sample size of this chart is optimized to ensure the best overall performance for detecting a wide range of shifts in the infection rate, based on the available resources, such as the inspection rate and the allowable false alarm rate. The effectiveness of the proposed optimized np chart is compared with that of the traditional np chart with a predetermined sample size under both sampling inspection and 100% inspection. For a variety of scenarios including a real case, the optimized np control chart is found to substantially outperform its traditional counterpart in terms of the average number of infections. Therefore, this control chart has potential to be an effective tool for early detection of respiratory virus outbreaks, promoting early outbreak investigation and mitigation.

Spatiotemporal dynamics of temperature and precipitation with reference to COVID-19 pandemic lockdown: perspective from Indian subcontinent.

ABSTRACT: This study exclusively focuses on spatial and temporal change of temperature and precipitation before and after COVID-19 lockdown and also examines the extent of their variation and the spatial relationship between them. Our main objective is to analyze the spatiotemporal changes of two climatic variables in Indian subcontinent for the period of 2015-2020. Monthly precipitation and temperature data are collected from NOAA and NASA for January to May month across the four zones (northeast, northwest, central, and peninsular zone) of India. To conduct a zone-wise statistical analysis, we have adopted statistical process control (SPC) methods like exponentially weighted moving average (EWMA) control charts, individual charts (I- Chart) to detect the shift in temperature and precipitation over the study period and Pearson correlation coefficient applied to measure the spatial association between the two variables. The findings revealed that temperature parameter has experienced a lot of positive and negative trends in the span of 6 years and detected a weak to moderate negative correlation in many parts of the country in April 2020 after 2016. This study also identified a weak negative correlation mainly in NE zone in 2020 after 2017. This research provides vital scientific contribution to the effects of monthly temperature and precipitation before and after COVID-19 pandemic lockdown.

Understanding variation in reported covid-19 deaths with a novel Shewhart chart application.

OBJECTIVE: Motivated by the coronavirus disease 2019 (covid-19) pandemic, we developed a novel Shewhart chart to visualize and learn from variation in reported deaths in an epidemic. CONTEXT: Without a method to understand if a day-to-day variation in outcomes may be attributed to meaningful signals of change-rather than variability we would expect-care providers, improvement leaders, policy-makers, and the public will struggle to recognize if epidemic conditions are improving. METHODS: We developed a novel hybrid C-chart and I-chart to detect within a geographic area the start and end of exponential growth in reported deaths. Reported deaths were the unit of analysis owing to erratic reporting of cases from variability in local testing strategies. We used simulation and case studies to assess chart performance and define technical parameters. This approach also applies to other critical measures related to a pandemic when high-quality data are available. CONCLUSIONS: The hybrid chart detected the start of exponential growth and identified early signals that the growth phase was ending. During a pandemic, timely reliable signals that an epidemic is waxing or waning may have mortal implications. This novel chart offers a practical tool, accessible to system leaders and frontline teams, to visualize and learn from daily reported deaths during an epidemic. Without Shewhart charts and, more broadly, a theory of variation in our epidemiological arsenal, we lack a scientific method for a real-time assessment of local conditions. Shewhart charts should become a standard method for learning from data in the context of a pandemic or epidemic.