Phylogenomic early warning signals for SARS-CoV-2 epidemic waves.

BACKGROUND: Epidemic waves of coronavirus disease 2019 (COVID-19) infections have often been associated with the emergence of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Rapid detection of growing genomic variants can therefore serve as a predictor of future waves, enabling timely implementation of countermeasures such as non-pharmaceutical interventions (social distancing), additional vaccination (booster campaigns), or healthcare capacity adjustments. The large amount of SARS-CoV-2 genomic sequence data produced during the pandemic has provided a unique opportunity to explore the utility of these data for generating early warning signals (EWS). METHODS: We developed an analytical pipeline (Transmission Fitness Polymorphism Scanner - designated in an R package mrc-ide/tfpscanner) for systematically exploring all clades within a SARS-CoV-2 virus phylogeny to detect variants showing unusually high growth rates. We investigated the use of these cluster growth rates as the basis for a variety of statistical time series to use as leading indicators for the epidemic waves in the UK during the pandemic between August 2020 and March 2022. We also compared the performance of these phylogeny-derived leading indicators with a range of non-phylogeny-derived leading indicators. Our experiments simulated data generation and real-time analysis. FINDINGS: Using phylogenomic analysis, we identified leading indicators that would have generated EWS ahead of significant increases in COVID-19 hospitalisations in the UK between August 2020 and March 2022. Our results also show that EWS lead time is sensitive to the threshold set for the number of false positive (FP) EWS. It is often possible to generate longer EWS lead times if more FP EWS are tolerated. On the basis of maximising lead time and minimising the number of FP EWS, the best performing leading indicators that we identified, amongst a set of 1.4 million, were the maximum logistic growth rate (LGR) amongst clusters of the dominant Pango lineage and the mean simple LGR across a broader set of clusters. In the case of the former, the time between the EWS and wave inflection points (a conservative measure of wave start dates) for the seven waves ranged between a 20-day lead time and a 7-day lag, with a mean lead time of 5.4 days. The maximum number of FP EWS generated prior to a true positive (TP) EWS was two and this only occurred for two of the seven waves in the period. The mean simple LGR amongst a broader set of clusters also performed well in terms of lead time but with slightly more FP EWS. INTERPRETATION: As a result of the significant surveillance effort during the pandemic, early detection of SARS-CoV-2 variants of concern Alpha, Delta, and Omicron provided some of the first examples where timely detection and characterisation of pathogen variants has been used to tailor public health response. The success of our method in generating early warning signals based on phylogenomic analysis for SARS-CoV-2 in the UK may make it a worthwhile addition to existing surveillance strategies. In addition, the method may be translatable to other countries and/or regions, and to other pathogens with large-scale and rapid genomic surveillance. FUNDING: This research was funded in whole, or in part, by the Wellcome Trust (220885_Z_20_Z). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. KOD, OB, VBF and EMV acknowledge funding from the MRC Centre for Global Infectious Disease Analysis (reference MR/X020258/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement and is also part of the EDCTP2 programme supported by the European Union. RMC acknowledges funding from the Wellcome Trust Collaborators Award (206298/Z/17/Z).

Quantifying resilience and the risk of regime shifts under strong correlated noise.

Early warning indicators often suffer from the shortness and coarse-graining of real-world time series. Furthermore, the typically strong and correlated noise contributions in real applications are severe drawbacks for statistical measures. Even under favourable simulation conditions the measures are of limited capacity due to their qualitative nature and sometimes ambiguous trend-to-noise ratio. In order to solve these shortcomings, we analyze the stability of the system via the slope of the deterministic term of a Langevin equation, which is hypothesized to underlie the system dynamics close to the fixed point. The open-source available method is applied to a previously studied seasonal ecological model under noise levels and correlation scenarios commonly observed in real world data. We compare the results to autocorrelation, standard deviation, skewness, and kurtosis as leading indicator candidates by a Bayesian model comparison with a linear and a constant model. We show that the slope of the deterministic term is a promising alternative due to its quantitative nature and high robustness against noise levels and types. The commonly computed indicators apart from the autocorrelation with deseasonalization fail to provide reliable insights into the stability of the system in contrast to a previously performed study in which the standard deviation was found to perform best. In addition, we discuss the significant influence of the seasonal nature of the data to the robust computation of the various indicators, before we determine approximately the minimal amount of data per time window that leads to significant trends for the drift slope estimations.

[Accumulated long-term care benefits by risk assessment scales for incident functional disability: A six-year follow-up study of long-term care receipt data].

Objectives　This study aims to evaluate the differences in the cumulative benefit costs of public long-term care [LTC] insurance services, using a risk assessment scale score, which predicts incident functional disability among older people.Methods　A baseline survey was conducted in 2010 involving individuals aged 65 and above from 12 municipalities in Japan who were not eligible for public LTC insurance benefits (response rate: 64.7%). Using public LTC claim records, we followed LTC service costs among 46,616 individuals over a period of about six years (up to 76 months). We used risk assessment scales to assess incident functional disability (0-48). We adopted a classical linear regression model, Tobit regression model, and linear regression with multiple imputation for missing values.Results　Overall, 7,348 (15.8%) of the participants had used LTC services during the follow-up period. The risk assessment score for incident functional disability was positively associated with the cumulative costs of LTC services per person, length of usage period of LTC services, and proportion of people certified for long-term care/support need and for over long-term care level 2. After adjusting for confounding variables, the six-year cumulative costs of LTC services were around JPY 31.6 thousand higher per point of risk score (95% confidence interval [CI]: 28.3 to 35.0). The costs were around JPY 8.9 thousand (95%CI: 6.5 to 11.3)higher in the low score group (risk score ≤ 16), and JPY 75.3 thousand (95%CI: 67.4 to 83.1) higher in the high score group (risk score ≥ 17). When we adopted other estimated models, the major results and trends were not largely different.Conclusions　In this study, the risk assessment scale score could estimate subsequent LTC benefit costs. Community interventions to improve and maintain variable aspects of risk assessment scores may help contribute to a reduction in public LTC benefits within municipalities.

A Long Short-Term Memory Network Stock Price Prediction with Leading Indicators.

The accuracy of the prediction of stock price fluctuations is crucial for investors, and it helps investors manage funds better when formulating trading strategies. Using forecasting tools to get a predicted value that is closer to the actual value from a given financial data set has always been a major goal of financial researchers and a problem. In recent years, people have paid particular attention to stocks, and gradually used various tools to predict stock prices. There is more than one factor that affects financial trends, and people need to consider it from all aspects, so research on stock price fluctuations has also become extremely difficult. This paper mainly studies the impact of leading indicators on the stock market. The framework used in this article is proposed based on long short-term memory (LSTM). In this study, leading indicators that affect stock market volatility are added, and the proposed framework is thus named as a stock tending prediction framework based on LSTM with leading indicators (LSTMLI). This study uses stock markets in the United States and Taiwan, respectively, with historical data, futures, and options as data sets to predict stock prices in these two markets. We measure the predictive performance of LSTMLI relative to other neural network models, and the impact of leading indicators on stock prices is studied. Besides, when using LSTMLI to predict the rise and fall of stock prices in the article, the conventional regression method is not used, but the classification method is used, which can give a qualitative output based on the data set. The experimental results show that the LSTMLI model using the classification method can effectively reduce the prediction error. Also, the data set with leading indicators is better than the prediction results of the single historical data using the LSTMLI model.

Workforce perceptions of human factors as indicators of plant reliability and process safety.

Human factors, as perceived by the maintenance workforce, were used as the measure for comparing work areas within a petroleum company. These factors were then compared to an objective measure of reliability (Mean Time Between Failures) in order to determine which factors would be most predictive of plant reliability and process safety. Maintenance personnel were surveyed using scales based on Problem-solving, Vigilance, Design and maintenance, Job-related feedback and Information about change. Analysis of Variance was used to assess the strength of these variables in relation to Reliability Level. Significant differences were observed between different reliability levels based on workforce perceptions of problem-solving requirements and the design and maintainability of plant. Conclusions were that perceptions of human factors in the workplace can be predictive of group-level performance, and that if issues relating to design and maintainability are not addressed at the design stage, greater problem-solving abilities will be required from maintenance personnel. Practitioner summary: Workforce perceptions of plant performance could provide a statistically valid measure of current and future reliability. A survey of perceptions of human factors was conducted with maintenance personnel in a petroleum company. Results indicated significant relationships between reliability and requirements for Problem-solving, as well as Design and Maintenance of equipment. Abbreviations: HFIT: human factors investigation tool, FPSO: floating production, storage and offtake, MTBF: mean time between failures, CPS: cognitive problem- solving, WDS: work design questionnaire, SPSS: statistical package for the social sciences, PAF: principal axis factoring, ANOVA: analysis of variance, ANCOVA: analysis of co-variance, M: mean, SD: standard deviation.

Article numbers as a leading indicator of publication time / Números de artigos como um indicador importante do tempo de publicação

Aims: in health professions education (HPE), the use of statistics is commonly associated with somewhat larger samples, whereas smaller samples or single subjects (i.e., N = 1) are usually labelled as needing some kind of 'qualitative' approach. However, statistical methods can be very useful in small samples and for individual subjects as well, especially where we have time series of repeated measurements of the same outcome variable(s) of interest. The aim of this article is twofold: to demonstrate an example of a cross-correlation function for single subjects in a HPE context and to suggest a few settings in HPE where this cross-correlation function can be of use. Method: the example uses data from a recent Open Access publication on among others article numbers and publication time in a number of major HPE journals to examine the relation between the number of articles published and median publication time over time in the zero-cost Open-Source statistical program R version 4.0.5. Results: as to be expected, the number of articles published appears somewhat of a leading indicator of publication time: both number of articles in year 'y' and number of articles in year 'y minus 1' correlate > 0.6 with median publication time in year 'y', while correlations of other time differences (e.g., number of articles in year 'y minus 2' and median publication time in year 'y', or median publication time in year 'y' and number of articles in year 'y plus 1') are substantially smaller. Conclusion: in line with recent literature, this article demonstrates that the cross-correlation function can be used in the context of small samples and single subjects. While the example focusses on article numbers and publication times, it can equally be applied in for example studying relations between knowledge, skills and attitude in individuals, or relations between behaviors of individuals working in pairs or small groups.

Introdução: na educação de profissionais de saúde, o uso de estatísticas é associado comumente a amostras um pouco maiores, enquanto as amostras menores ou assuntos únicos (ou seja, N = 1) são geralmente rotulados como precisando de algum tipo de abordagem "qualitativa". No entanto, os métodos estatísticos podem ser muito úteis em pequenas amostras e para sujeitos individuais, especialmente quando temos séries temporais de medições repetidas da(s) mesma(s) variável(is) de desfecho de interesse. O objetivo deste artigo é demonstrar um exemplo de uma função de correlação cruzada para sujeitos individuais em um contexto de educação de profissionais de saúde e sugerir algumas configurações em que essa função pode ser útil. Método: o exemplo usa dados de uma publicação recente de acesso aberto sobre, entre outros, números de artigos e tempo de publicação em vários dos principais periódicos da educação de profissionais de saúde para examinar a relação entre o número de artigos publicados e o tempo médio de publicação ao longo do tempo, no programa R versão 4.0.5, programa estatístico de código aberto de custo zero. Resultados: o número de artigos publicados parece ser um indicador importante do tempo de publicação: tanto o número de artigos no ano "y" quanto o número de artigos no ano "y menos 1" se correlacionam > 0,6 com o tempo médio de publicação no ano "y", enquanto as correlações de outras diferenças de tempo são substancialmente menores, como, por exemplo, número de artigos no ano " y menos 2" e tempo médio de publicação no ano " y", ou tempo médio de publicação no ano "y" e número de artigos no ano "y mais 1"). Conclusão: de acordo com a literatura recente, este artigo demonstra que a função de correlação cruzada pode ser usada no contexto de pequenas amostras e indivíduos únicos. Embora o exemplo se concentre em números de artigos e tempos de publicação, pode igualmente ser aplicado, por exemplo, no estudo de relações entre conhecimento, habilidades e atitudes em indivíduos, ou relações entre comportamentos de indivíduos que trabalham em pares ou pequenos grupos.

Multi-dimensional model for determining the leading performance indicators of safety management systems.

BACKGROUND: Health and safety performance measurements aimed to provide information on the progress and current situation of organizational strategies and activities. OBJECTIVES: We developed a model to determine and select safety key performance indicators in order to assess safety management systems. METHODS: This study has been designed in six steps aiming at defining a model of leading performance indicators (LPIs) and selecting key performance indicators (KPIs) using the AHP method. RESULTS: According to the results analysis, 116 structural and operational indicators were defined based on the components of the OHSAS 18001 management system. For this purpose, 19 structural, 27 operational and 33 active KPIs were selected by AHP and BN techniques. CONCLUSION: Development of LPIs is influenced by various organizational, managerial, and operational factors. LPIs extracted from the components of the OHS-MS deployed in an organization are often passive and cannot show the changes in the safety status of a workplace in a short period. The model presented in this study was designed with an emphasis on extraction of active and operational indicators, as they were capable of detecting performance changes in construction industries.

A daily fever curve for the Swiss economy.

Because macroeconomic data is published with a substantial delay, assessing the health of the economy during the rapidly evolving COVID-19 crisis is challenging. We develop a fever curve for the Swiss economy using publicly available daily financial market and news data. The indicator can be computed with a delay of 1 day. Moreover, it is highly correlated with macroeconomic data and survey indicators of Swiss economic activity. Therefore, it provides timely and reliable warning signals if the health of the economy takes a turn for the worse.

Development and validation of a new safety climate scale for petrochemical industries.

BACKGROUND: While a considerable body of research has studied safety climate and its role as a leading indicator of organizational safety, much of this work has been conducted with Western manufacturing samples. OBJECTIVE: The current study puts emphasis on the cross-validation of a safety climate model in the non-Western industrial context of Iranian petrochemical industries. METHODS: The current study was performed in one petrochemical company in Iran. The scale was developed through conducting a literature review followed by a qualitative study with expert participation. After performing a screening process, the initial number of items on the scale was reduced to 68. RESULTS: Ten dimensions (including management commitment, workers' empowerment, communication, blame culture, safety training, job satisfaction, interpersonal relationship, supervision, continuous improvement, and reward system) together with 37 items were extracted from the exploratory factor analysis (EFA) to measure safety climate. Acceptable ranges of internal consistency statistics for the sub-scales were observed. Confirmatory factor analysis (CFA) confirmed the construct validity of the developed safety climate scale for the petrochemical industry workers. The results of reliability showed that the Cronbach's alpha coefficient for the designed scale was 0.94. The ICC was obtained 0.92. CONCLUSION: This study created a valid and reliable scale for measuring safety climate in petrochemical industries.

Developing leading indicators from OHS management audit data: Determining the measurement properties of audit data from the field.

INTRODUCTION: OHS management audits are one means of obtaining data that may serve as leading indicators. The measurement properties of such data are therefore important. This study used data from Workwell audit program in Ontario, a Canadian province. The audit instrument consisted of 122 items related to 17 OHS management elements. The study sought answers regarding (a) the ability of audit-based scores to predict workers' compensation claims outcomes, (b) structural characteristics of the data in relation to the organization of the audit instrument, and (c) internal consistency of items within audit elements. METHOD: The sample consisted of audit and claims data from 1240 unique firms that had completed one or two OHS management audits during 2007-2010. Predictors derived from the audit results were used in multivariable negative binomial regression modeling of workers' compensation claims outcomes. Confirmatory factor analyses were used to examine the instrument's structural characteristics. Kuder-Richardson coefficients of internal consistency were calculated for each audit element. RESULTS: The ability of audit scores to predict subsequent claims data could not be established. Factor analysis supported the audit instrument's element-based structure. KR-20 values were high (≥0.83). CONCLUSIONS: The Workwell audit data display structural validity and high internal consistency, but not, to date, construct validity, since the audit scores are generally not predictive of subsequent firm claim experience. Audit scores should not be treated as leading indicators of workplace OHS performance without supporting empirical data. PRACTICAL APPLICATIONS: Analyses of the measurement properties of audit data can inform decisionmakers about the operation of an audit program, possible future directions in audit instrument development, and the appropriate use of audit data. In particular, decision-makers should be cautious in their use of audit scores as leading indicators, in the absence of supporting empirical data.

Assessment of work compatibility across employees' demographics: a case study.

'Work compatibility' (WC) is a multi-dimensional diagnostic tool for measuring human performance that affects safety performance of work force. There are a dearth of literature on the use of WC in industrial applications. In this study, the status of WC and its components across employees' demographics such as age, experience, designation and location of work were examined in a steel plant in India. Data on 119 employees collected using Demand-Energizer Instrument was analysed. The results revealed that supervisors perceive higher energizers, higher demands and low WC as compared to workers. Older and high experience employees perceive higher energizers, lower demands and high WC as compared to younger and less experienced employees. All employee groups perceive higher demand for physical environment and physical task content. The problematic work groups identified are less experienced employees and workers in 'allied sections'. The outcomes of the study help the management in three ways to improve human performance at work places: (i) it provides useful information about the work factors to be considered for intervention design, (ii) it identifies the work groups to be targeted while preparing intervention strategies and (iii) it can be used as a leading indicator of human performance.

Early warning of critical transitions in biodiversity from compositional disorder.

Global environmental change presents a clear need for improved leading indicators of critical transitions, especially those that can be generated from compositional data and that work in empirical cases. Ecological theory of community dynamics under environmental forcing predicts an early replacement of slowly replicating and weakly competitive "canary" species by slowly replicating but strongly competitive "keystone" species. Further forcing leads to the eventual collapse of the keystone species as they are replaced by weakly competitive but fast-replicating "weedy" species in a critical transition to a significantly different state. We identify a diagnostic signal of these changes in the coefficients of a correlation between compositional disorder and biodiversity. Compositional disorder measures unpredictability in the composition of a community, while biodiversity measures the amount of species in the community. In a stochastic simulation, sequential correlations over time switch from positive to negative as keystones prevail over canaries, and back to positive with domination of weedy species. The model finds support in empirical tests on multi-decadal time series of fossil diatom and chironomid communities from lakes in China. The characteristic switch from positive to negative correlation coefficients occurs for both communities up to three decades preceding a critical transition to a sustained alternate state. This signal is robust to unequal time increments that beset the identification of early-warning signals from other metrics.

Construcción de un indicador líder compuesto para el Maule / Construction of a composite leading indicator for Maule

En términos generales, la investigación tiene como objetivo crear un indicador económico para el Maule que permita anticiparse al devenir de su ciclo económico, en consideración de sus principales actividades productivas. En específico, se pretende someter a pruebas estadísticas de significancia y validez a las principales series económicas de la región, de manera tal de seleccionar, por un lado, una serie de referencia de la actividad económica y, por otro, las series componentes del indicador. La metodología utilizada es aquella aplicada por la Nacional Bureau of Economic Research (NBER) en la creación de este tipo de indicadores para los países integrantes de la Organización para la Cooperación y el Desarrollo Económico (OCDE). Como resultado de la investigación se logra seleccionar y validar empíricamente como serie de referencia para el Maule, al Índice de Actividad Económica Regional (INACER), y a las siguientes series componentes del indicador; ocupados, cesantes, buscan trabajo por primera vez, inactivos, edificación aprobada total obras nuevas y total de exportaciones. Con tales series, se construye un indicador predictivo del comportamiento económico para la región, denominado Índice Líder Compuesto para el Maule (ILCM).

Overall, the research aims to create an economic indicator for the Maule that allows anticipate the evolution of the economic cycle, in consideration of its main productive activities. Specifically, it aims to test statistical significance and validity to the main economic series in the region, so as to select the one hand, a number of reference of economic activity and other components series indicator. The methodology used is that applied by the National Bureau of Economic Research (NBER) in the creation of this type of indicators for the member countries of the Organization for Economic Cooperation and Development (OECD). As a result of the research is done select and validate empirically as reference series for the Maule, the Regional Economic Activity Index (INACER), and the following components of the indicator series; employed, unemployed, seeking work for the first time, inactive, all new approved building works Total exports. With such series, a predictive indicator of economic performance for the region, called for the Maule Composite Index (ILCM) Leader is built.