Residual Autocorrelation Research Articles

Analyzing the Dynamics of Inflation, Exchange Rates and Economic Growth through the Gini Index: Modeling VAR in Morocco

In this study, we analyze the cross-impact of inflation, exchange rates and economic growth on income inequality in Morocco between 2000 and 2022, based on the GINI index as a measure of inequality. Its main objective is to understand how these macroeconomic variables influence income disparities. Our methodology is based on a VAR model to capture dynamic interactions between variables. To validate the robustness of the model, Granger causality tests and specification tests, including tests of homoscedasticity and autocorrelation of residuals, were used. The result is that inflation has a significant positive impact on income inequality, and exchange rate fluctuations directly influence inequality. Furthermore, economic growth helps to reduce inequality, although this effect depends on the distribution of the benefits of this growth. This study contributes to the existing literature by providing empirical evidence of the importance of macroeconomic stability and educational and fiscal policies in reducing income inequality.

Spatially varying associations between community-level predictors and COVID-19 outcomes in NYC

Abstract Background The cumulative impacts of COVID-19 on hospitalization and mortality were not uniformly distributed across New York City (NYC). To better understand potential drivers of this observed geospatial disparity, we explored the associations between community-level sociodemographic characteristics and cumulative COVID-19 hospitalizations and mortality using geographically weighted Poisson regression (GWPR). Methods Cumulative COVID-19 hospitalization and mortality rates in 177 NYC modified ZIP code tabulation areas as of Dec 2022 were taken from the NYC Department of Health and Mental Hygiene, and sociodemographic predictors were taken from the 2018 American Community Survey. GWPR was applied using both non-multiscale (single bandwidth) and multiscale (flexible bandwidth) models to assess which predictors were significant and which associations varied spatially (allowing them to potentially act as both risk and protective factors, depending on the location). Results Multiscale GWPR models outperformed non-multiscale models in rendering residual spatial autocorrelation insignificant. Although multiscale GWPR allowed flexible bandwidths for each predictor, most yielded global bandwidths, suggesting geographically consistent effects. For mortality, the percent of residents without health insurance acted solely as a risk factor. Similarly, for hospitalizations, the percent of residents with a disability acted solely as a risk factor. The percent of residents with a bachelors degree or higher acted solely as a protective factor against both outcomes. Conclusions Even when associations were allowed to vary spatially in GWPR models, we still found geographically consistent associations between many sociodemographic predictors and cumulative COVID-19 outcomes in NYC. Consistent risk factors, such as prevalence of disability, or protective factors, such as prevalence of higher education, highlight potential areas for city-wide policies to reduce the burden of future epidemics. Key messages • For cumulative COVID-19 outcomes across NYC communities, geographically consistent risk factors include disability prevalence for hospitalization and lack of health insurance for mortality. • Increasing prevalence of higher education acted as a geographically consistent protective factor against both hospitalization and mortality due to COVID-19.

The relationship between bank lending and economic factors in the regions of Kazakhstan

Understanding the impact of economic factors on bank lending is crucial in Kazakhstan’s modern economy, characterized by volatile inflation and fluctuations in real wages. This paper aims to investigate the link between bank lending and economic factors such as inflation, real wages, and consumer expenditure in a regional context. Data from the Bureau of National Statistics and the National Bank, covering the period from 2012 to 2022, were used to uncover how economic factors influence bank lending. For the analysis, various economic indicators were integrated through normalization and averaging. Analysis reveals significant regional disparities in real wages and consumer expenditures, which impact the demand for bank credit. The results of the correlation matrix showed that both real wages (P-value < 0.001) and inflation (P-value < 0.001) significantly impact bank lending, with an R² value of 0.998, indicating that the model explains 99.8% of the variation in bank lending. The regression analysis highlights that regions with higher real wages, such as Astana, Almaty, and Atyrau, provide the most favorable conditions for banking sector growth, demonstrated by a strong relationship between wages and bank lending. In contrast, regions with lower wage levels, such as Turkestan and Zhambyl, show a significantly weaker connection (around 0.65), reflecting their lower attractiveness for banking investment and emphasizing the need for policies to address social inequality. The Durbin-Watson test confirmed no autocorrelation in residuals (DW = 1.89), although heteroscedasticity was detected, suggesting the need for further model adjustments. The study emphasizes the importance of developing economic policies that can balance regional development and improve financial stability. AcknowledgmentsThis research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant “Development of mechanisms for reducing social inequality and improving the welfare of the population of Kazakhstan” AP19174744).

Multiscale cooperative optimization in multiscale geographically weighted regression models

Scale in multiscale geographically weighted regression (MGWR) directly impacts the accuracy of coefficient estimates and shapes the comprehensive evaluation of the intensity of spatially non-stationary relationships. Presently, MGWR primarily utilizes back-fitting for sequentially optimizing multiple scales (MGWR-BF). However, the set of individual optima obtained through sequential optimization may not necessarily represent the global optimum. To address this issue, this paper proposes a multi-scale cooperative optimization within MGWR (MGWR-GA) model. Specifically, MGWR-GA employs a genetic algorithm to simultaneously input potential scale combinations, each comprising P scales. Subsequently, it introduces a dedicated overall estimation algorithm designed for these P scales, ultimately determining the optimal scale combinations based on the AICc. Simulation experiments have shown that, at least for global stationarity, the scales obtained by MGWR-GA approximate the true values across twelve different test environments. Additionally, the coefficient estimation bias of MGWR-GA is lower than that of MGWR-BF, especially in low signal-to-noise ratio settings. Empirical experiments further confirm the effectiveness of MGWR-GA in identifying both globally stationary and locally non-stationary scales. Furthermore, MGWR-GA outperforms MGWR-BF in terms of goodness-of-fit, adjusted goodness-of-fit, AICc and spatial autocorrelation of residuals. These findings indicate that MGWR-GA can serve as a valuable tool for modeling spatially non-stationary relationships.

Diagnostic checking of periodic vector autoregressive time series models with dependent errors

In this article, we study the asymptotic behaviour of the residual autocorrelations for periodic vector autoregressive time series models (PVAR henceforth) with uncorrelated but dependent innovations (i.e., weak PVAR). We then deduce the asymptotic distribution of the Ljung–Box-McLeod modified Portmanteau statistics for weak PVAR models. In Monte Carlo experiments, we illustrate that the proposed test statistics have reasonable finite sample performance. When the innovations exhibit conditional heteroscedasticity or other forms of dependence, it appears that the standard test statistics (under independent and identically distributed innovations) are generally unreliable, overrejecting, or underrejecting severely, while the proposed test statistics offer satisfactory levels. The proposed methodology is employed in the analysis of two river flows.

Time Series Methods to Assess the Impact of Regulatory Action: A Study of UK Primary Care and Hospital Data on the Use of Fluoroquinolones.

To illustrate the interest in using interrupted time series (ITS) methods, this study evaluated the impact of the UK MHRA's March 2019 Risk Minimisation Measures (RMM) on fluoroquinolone usage. Monthly and quarterly fluoroquinolone use incidence rates from 2012 to 2022 were analysed across hospital care (Barts Health NHS Trust), primary care (Clinical Practice Research Datalink (CPRD) Aurum and CPRD GOLD), and linked records from both settings (East Scotland). Rates were stratified by age (19-59 and ≥ 60 years old). Seasonality-adjusted segmented regression and ARIMA models were employed to model quarterly and monthly rates, respectively. Post-RMM, with segmented regression, both age groups in Barts Health experienced nearly complete reductions (> 99%); CPRD Aurum saw 20.19% (19-59) and 19.29% ( 60) reductions; no significant changes in CPRD GOLD; East Scotland had 45.43% (19-59) and 41.47% ( 60) decreases. Slope analysis indicated increases for East Scotland (19-59) and both CPRD Aurum groups, but a decrease for CPRD GOLD's 60; ARIMA detected significant step changes in CPRD GOLD not identified by segmented regression and noted a significant slope increase in Barts Health's 19-59 group. Both models showed no post-modelling autocorrelations across databases, yet Barts Health's residuals were non-normally distributed with non-constant variance. Both segmented regression and ARIMA confirmed the reduction of fluoroquinolones use after RMM across four different UK primary care and hospital databases. Model diagnostics showed good performance in eliminating residual autocorrelation for both methods. However, diagnostics for hospital databases with low incident use revealed the presence of heteroscedasticity and non-normal white noise using both methods.

Random forests with spatial proxies for environmental modelling: opportunities and pitfalls

Abstract. Spatial proxies, such as coordinates and distance fields, are often added as predictors in random forest (RF) models without any modifications being made to the algorithm to account for residual autocorrelation and improve predictions. However, their suitability under different predictive conditions encountered in environmental applications has not yet been assessed. We investigate (1) the suitability of spatial proxies depending on the modelling objective (interpolation vs. extrapolation), the strength of the residual spatial autocorrelation, and the sampling pattern; (2) which validation methods can be used as a model selection tool to empirically assess the suitability of spatial proxies; and (3) the effect of using spatial proxies in real-world environmental applications. We designed a simulation study to assess the suitability of RF regression models using three different types of spatial proxies: coordinates, Euclidean distance fields (EDFs), and random forest spatial prediction (RFsp). We also tested the ability of probability sampling test points, random k-fold cross-validation (CV), and k-fold nearest neighbour distance matching (kNNDM) CV to reflect the true prediction performance and correctly rank models. As real-world case studies, we modelled annual average air temperature and fine particulate air pollution for continental Spain. In the simulation study, we found that RFs with spatial proxies were poorly suited for spatial extrapolation to new areas due to significant feature extrapolation. For spatial interpolation, proxies were beneficial when both strong residual autocorrelation and regularly or randomly distributed training samples were present. In all other cases, proxies were neutral or counterproductive. Random k-fold cross-validation generally favoured models with spatial proxies even when it was not appropriate, whereas probability test samples and kNNDM CV correctly ranked models. In the case studies, air temperature stations were well spread within the prediction area, and measurements exhibited strong spatial autocorrelation, leading to an effective use of spatial proxies. Air pollution stations were clustered and autocorrelation was weaker and thus spatial proxies were not beneficial. As the benefits of spatial proxies are not universal, we recommend using spatial exploratory and validation analyses to determine their suitability, as well as considering alternative inherently spatial modelling approaches.

Fiscal Policy and Economic Resilience: The Impact of Government Consumption Alongside Oil and Non-Oil Revenues on Saudi Arabia’s GDP during Crises (1969–2022)

This study investigates the impact of Government Consumption (GC) on Saudi Arabia’s GDP during major economic crises from 1969 to 2022, focusing on periods marked by fluctuations in oil and non-oil revenues. By integrating these revenue streams, the research provides a more comprehensive analysis of fiscal policy effectiveness during economic downturns. Using an Autoregressive Distributed Lag (ARDL) model, the study reveals the complex role of Government Consumption (GC) in stabilizing and stimulating the Saudi economy amidst revenue volatility. Key findings indicate that while GC does not significantly influence GDP in the short term, its long-term effectiveness varies across different crises. Specifically, GC has acted as a buffer against immediate economic shocks during certain crises while providing a stimulus for economic recovery in others. During the 2020 COVID-19 pandemic, timely fiscal measures significantly boosted GDP, underscoring the importance of adaptive and proactive fiscal policies. Conversely, the 2014–2016 oil price collapse demonstrated that GC alone was insufficient to counteract economic downturns, emphasizing the need for diversified revenue strategies. These findings underscore the dual role of GC in economic stabilization and recovery. During the COVID-19 pandemic, GC played a crucial role in both mitigating negative economic impacts and supporting recovery efforts, showcasing its effectiveness in times of global disruptions. This demonstrates GC’s capability as an immediate buffer against economic shocks and a stimulus for economic recovery. In contrast, during the 2014–2016 oil price collapse, GC was less effective, indicating the limitations of relying solely on government spending without broader economic diversification. This highlights the necessity of diversified revenue strategies to complement fiscal measures for long-term economic resilience. The robustness of the findings was ensured through various diagnostic tests, including checks for autocorrelation, heteroskedasticity, and stationarity of residuals. The absence of significant autocorrelation and heteroskedasticity, along with the stationarity of differenced variables, confirms the validity of the econometric models used. The study contributes to the discourse on fiscal policy in oil-dependent economies by illustrating the critical role of diversified revenue strategies and adaptive fiscal measures in enhancing economic resilience. Recommendations are offered for policymakers to optimize fiscal strategies, ensuring robust economic recovery and long-term stability in volatile markets. This research highlights the necessity for Saudi Arabia to refine its fiscal policies towards greater economic diversification and stability.

SPATIALLY INFORMED INSIGHTS: MODELING PERCENTAGE POVERTY IN EAST JAVA PROVINCE USING SEM WITH SPATIAL WEIGHT VARIATIONS

The East Java Province stands as one of Indonesia's regions grappling with a notably elevated poverty rate, accounting for 11.32% of the populace. A strategic approach employed to comprehend and redress this issue involves the application of spatial analysis, wherein spatial factors are intricately integrated into the modeling and cartographic representation of poverty data. The primary objective of this research is to discern the principal determinants influencing the incidence of poverty in East Java Province, employing data reflective of the population's poverty percentages within the province for the year 2021. The study incorporates six pivotal variables, namely: the population poverty rate, open unemployment rate, labor force participation rate, average years of schooling, adjusted per capita expenditure, and the gross regional domestic product (GRDP), predicated on adjusted expenditure. Diverse weighting schemes are applied based on both distance (1) and contiguity (2). The optimal predictive model utilized is the Spatial Error Model (SEM) incorporating a Distance Band Weighing (DBW) mechanism with a designated maximum distance ( ) of 75000 meters. Outcomes indicate that the variable wielding the most substantial influence on the poverty percentage in East Java Province is the average years of schooling. Specifically, an increase in the pursuit of formal education manifests as a negative correlate to the poverty percentage, implying an inverse relationship. Moreover, the SEM model adheres to the requisite assumptions, encompassing (1) the normality of residuals, (2) homogeneity of residuals, and (3) non-spatial autocorrelation of residuals. Comparative analyses reveal that the SEM model utilizing DBW yields diminished values for MAE, MSE, RMSE, AIC, and MAPE in comparison to its linear regression counterpart. Furthermore, the pseudo- values obtained from the SEM surpass those derived from the linear regression model. Rigorous likelihood ratio tests underscore substantial disparities between the SEM and linear regression models, with the former proving more efficient and markedly enhancing the model's explanatory prowess concerning variations in the dataset.

Estimating Forest Aboveground Biomass Using a Combination of Geographical Random Forest and Empirical Bayesian Kriging Models

Accurately estimating forest aboveground biomass (AGB) is imperative for comprehending carbon cycling, calculating carbon budgets, and formulating sustainable forest management plans. Currently, random forest (RF) and other machine learning models are widely used to estimate forest AGB, as they can effectively handle nonlinear relationships. However, by constructing a global model using all the samples collected from a study area, these models fail to account for the spatial heterogeneity in the AGB and cannot correct the prediction biases, thereby constraining the estimation accuracy. To overcome these limitations, we proposed a novel approach termed geographical random forest and empirical Bayesian kriging (GRFEBK). This hybrid model combines the localized modeling capability of geographical random forest (GRF) with the bias correction strength of empirical Bayesian kriging (EBK). GRF adapts RF to account for the spatial heterogeneity of the AGB, while EBK utilizes the spatial autocorrelation of residuals to correct the prediction deviations. This study was conducted in Hainan Island, utilizing spectral bands, vegetation indices, tasseled cap components derived from Landsat-8 imagery, backscattering coefficients from ALOS-2 synthetic aperture radar, topographic features, and the forest canopy height as the explanatory variables. A total of 195 forest aboveground biomass (AGB) samples were collected for modeling and assessing the predictive accuracy. The results demonstrate that, among the tested models, including GRFEBK, RF, support vector machine (SVM), k-nearest neighbor (KNN), geographically weighted regression (GWR), GRF, and EBK, GRFEBK attains the highest R2 (0.78) and the lowest RMSE (36.04 Mg/ha) and RRMSE (22.87%), significantly outperforming the conventional models and using GRF or EBK alone. These results demonstrate that by accounting for local non-stationarity in AGB and correcting prediction biases, GRFEBK achieves significantly higher accuracy than conventional RF and other models. While the results are promising, the computational cost of GRFEBK and its performance under varying geographical conditions warrant further investigation at larger scales to assess its broader applicability. Nevertheless, GRFEBK provides an innovative and more reliable approach for accurate forest AGB estimation with great potential to support global forest resource monitoring.

Heat vulnerability and spatial equity of cooling center: Planning implications from the Korean case

Over the past decade, the number of patients with heat-related illnesses has steeply increased in South Korea. South Korea's main heat adaptation planning revolves around providing cooling shelters, mostly in the form of elderly centers, to those who cannot afford adequate cooling resources. While implementing such a policy is important, it is also important to evaluate the effectiveness of existing policies to derive useful planning implications for future heat adaptation planning. Controlling for the spatial autocorrelation of residuals using the Generalized Least Squares (GLS), we found that the cooling centers in Seoul are overall well-distributed which was attributable to the local heat response policies. However, cooling shelters have been mostly designated in senior shelters lacking mainstreamed discussion that heat vulnerability can be influenced by much diverse factors than age.

Traffic Exposure and Breast Cancer Mortality by Area of Residence: Incorporating Clinical and Socioeconomic Data.

Incorporating clinical and environmental data holds promise for monitoring vulnerable populations at the community level. This spatial epidemiology study explores the link between traffic-related air pollution and breast cancer mortality in Seoul, using public socioeconomic and clinical data from Samsung Medical Center's registry (N=6,089). Traffic and socioeconomic status were collected from official sources and integrated for spatial analysis. The findings revealed a significant association between adult breast cancer mortality and districts with high road density, NO2 emissions, and family income (p<0.05). Significant spatial autocorrelation of residuals was observed (Moran's I test p<0.001).

ПРОГНОЗ ФАКТОРОВ, ОКАЗЫВАЮЩИХ ВЛИЯНИЕ НА ПОКАЗАТЕЛИ ИННОВАЦИОННОЙ АКТИВНОСТИ РЕГИОНОВ РОССИИ

the article examines the problem of limitations of existing rating models in forecasting and analyzing the development of innovation processes in a regional context due to limited possibilities for integrating various factors, such as educational systems and financial instruments. The need to develop and implement new forecast models that take into account multiple aspects related to education and the financial component of regional development is emphasized. The study also addresses the importance of socio-economic development in determining the readiness of regions to introduce innovations and create an innovative economic environment. The article presents an analysis of the factors influencing the innovative development of regions, with an emphasis on the level of monopolization of the market environment as an important factor affecting the dynamics of innovation, and also puts forward proposals for stimulating technological innovation and ensuring their financial support to improve financial stability at the state and regional levels. Using econometric modeling methods, the authors assess the impact of various variables. Studying the impact of these factors on the dependent variable allows us to determine their significance for the economic process and the development of resource management strategies. It is shown that investments in innovative research, human resources, educational structure and technological parameters of production have a significant impact on innovative activity. The study is conducted using econometric modeling techniques, including multiple linear regression and short regression models to identify significant factors. Models are checked for compliance with the conditions of the Gauss-Markov theorem, heteroscedasticity and autocorrelation of residuals using appropriate statistical tests. The results of the analysis confirm the statistical significance and adequacy of the models, and also allow us to identify key factors. At the same time, opportunities for improving the coordination of actions to develop the innovative potential of regions are identified based on the results of econometric modeling.

Spatial patterns and sociodemographic predictors of chronic obstructive pulmonary disease in Florida.

Chronic obstructive pulmonary disease (COPD) is a chronic, inflammatory respiratory disease that obstructs airflow and decreases lung function and is a leading cause death globally. In the United States (US), the prevalence among adults is 6.2%, but increases with age to 12.8% among those 65 years or older. Florida has one of the largest populations of older adults in the US, accounting for 4.5 million adults 65 years or older. This makes Florida an ideal geographic location for investigating COPD as disease prevalence increases with age. Understanding the geographic disparities in COPD and potential associations between its disparities and environmental factors as well as population characteristics is useful in guiding intervention strategies. Thus, the objectives of this study are to investigate county-level geographic disparities of COPD prevalence in Florida and identify county-level socio-demographic predictors of COPD prevalence. This ecological study was performed in Florida using data obtained from the US Census Bureau, Florida Health CHARTS, and County Health Rankings and Roadmaps. County-level COPD prevalence for 2019 was age-standardized using the direct method and 2020 US population as the standard population. High-prevalence spatial clusters of COPD were identified using Tango's flexible spatial scan statistics. Predictors of county-level COPD prevalence were investigated using multivariable ordinary least squares model built using backwards elimination approach. Multicollinearity of regression coefficients was assessed using variance inflation factor. Shapiro-Wilks, Breusch Pagan, and robust Lagrange Multiplier tests were used to assess for normality, homoskedasticity, and spatial autocorrelation of model residuals, respectively. County-level age-adjusted COPD prevalence ranged from 4.7% (Miami-Dade) to 16.9% (Baker and Bradford) with a median prevalence of 9.6%. A total of 6 high-prevalence clusters with prevalence ratios >1.2 were identified. The primary cluster, which was also the largest geographic cluster that included 13 counties, stretched from Nassau County in north-central Florida to Charlotte County in south-central Florida. However, cluster 2 had the highest prevalence ratio (1.68) and included 10 counties in north-central Florida. Together, the primary cluster and cluster 2 covered most of the counties in north-central Florida. Significant predictors of county-level COPD prevalence were county-level percentage of residents with asthma and the percentage of current smokers. There is evidence of spatial clusters of COPD prevalence in Florida. These patterns are explained, in part, by differences in distribution of some health behaviors (smoking) and co-morbidities (asthma). This information is important for guiding intervention efforts to address the condition, reduce health disparities, and improve population health.

Exploring urban land surface temperature with geospatial and regression modelling techniques in Uttarakhand using SVM, OLS and GWR models

Given the climate change challenges, Uttarakhand has become crucial for examining land dynamics and regional climate interactions. This study employed a Support Vector Machine (SVM) for land use and land cover mapping for 2024, achieving 94% accuracy and a Kappa coefficient of 0.90, indicating robust mapping. Key land indices such as NDVI, NDWI, NDBI, NDSI, and NDBaI were calculated, along with Land Surface Temperature (LST) from Landsat 8 imagery. These indices were selected for their relevance in representing vegetation health (NDVI), measuring water content (NDWI), assessing urban areas (NDBI), identifying snow cover (NDSI), and highlighting the barren land (NDBaI), which all influence LST. Hotspot analysis with Getis-Ord Gi∗ revealed spatial distribution patterns of LST. Regression analysis showed significant relationships: a strong positive correlation between LST and NDBI (R2 = 0.78) and a substantial negative correlation between LST and NDSI (R2 = −0.80). The strong positive correlation highlights how urbanization contributes to rising surface temperatures, while the substantial negative correlation underscores the cooling effect of snow cover, which is particularly relevant as reduced snow cover could lead to higher LST in the context of climate change. These correlations offer deeper insights into how land cover changes can exacerbate or mitigate climate impacts in Uttarakhand. Two regression models were used for statistical modeling and spatial analysis: Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR). In OLS, the results reveal non-stationarity (p = 0.000) with an R2 value of 0.79 while GWR significantly enhanced performance, achieving an R2 value of 0.94. The improved performance of GWR (R2 = 0.94) compared to OLS (R2 = 0.79) can be attributed to GWR’s ability to account for spatial non-stationarity. This method allows for variations in relationships between LST and explanatory variables across different locations, effectively capturing local patterns that OLS may overlook. Spatial autocorrelation analysis, utilizing Moran’s I, exhibited a decrease from 0.606 (OLS) to 0.02 (GWR), This reduction indicates that GWR effectively captures spatial non-stationarity, minimizing residual autocorrelation by modeling local relationships between LST and its predictors that often remain in global models like OLS, thereby demonstrating its advantages in heterogeneous regions. The findings underscore the importance of employing GWR to better elucidate the connection between LST and its predictors, specifically in regions characterized by spatial non-stationarity, thereby offering insights crucial for informed decision-making amidst changing climatic conditions.

Short-term Passenger Flow Prediction of Urban Rail Transit based on ARIMA Model

Urban rail transit is very important in modern urban transportation, which meets the needs of People's Daily travel. This paper aims to introduce the ARIMA model into the short-term passenger flow forecast of urban rail transit, analyze its principle and application method, and build ARIMA model combined with AFC data to forecast short-term passenger flow of urban rail transit. Through the research and improvement of ARIMA model, it is anticipated that it would give urban rail transport operators more precise and trustworthy short-term passenger flow forecasting capabilities, so as to realize more intelligent and efficient operation of urban rail transit system. In this study, the ARIMA model was established, and rigorous scientific methods such as residual test, autocorrelation function (ACF) and partial autocorrelation function (PACF) test, and comparison of predicted value and real value were adopted to conduct tests step by step according to the experimental process. Finally, it was confirmed that the excellent prediction accuracy of the ARIMA prediction model used in this paper, can provide accurate prediction results, and can be used in practical scenarios.

Diagnostic checks in time series models based on a new correlation coefficient of residuals

For checking time series models, the Ljung–Box, Li–Mak and Zhu–Wang statistics play an important role, which use the Pearson's correlation coefficient to implement (squared) residual (partial) autocorrelation tests. In this paper, we replace the Pearson's correlation coefficient with a new rank correlation coefficient and propose a new test statistic to conduct diagnostic checks for residuals in autoregressive moving average models, autoregressive conditional heteroscedasticity models and integer-valued time series models, respectively. We conduct simulations to assess the performance of the new test statistic, and compare it with existing ones, and the results show the superiority of the proposed one. We use three real examples to exhibit its usefulness.

Initiations of safer supply hydromorphone increased during the COVID-19 pandemic in Ontario: An interrupted time series analysis.

Calls to prescribe safer supply hydromorphone (SSHM) as an alternative to the toxic drug supply increased during the COVID-19 pandemic but it is unknown whether prescribing behaviour was altered. We aimed to evaluate how the number of new SSHM dispensations changed during the pandemic in Ontario. We conducted a retrospective interrupted time-series analysis using provincial administrative databases. We counted new SSHM dispensations in successive 28-day periods from March 22, 2016 to August 30, 2021. We used segmented Poisson regression methods to test for both a change in level and trend of new dispensations before and after March 17, 2020, the date Ontario's pandemic-related emergency was declared. We adjusted the models to account for seasonality and assessed for over-dispersion and residual autocorrelation. We used counterfactual analysis methods to estimate the number of new dispensations attributable to the pandemic. We identified 1489 new SSHM dispensations during the study period (434 [mean of 8 per 28-day period] before and 1055 [mean of 56 per 28-day period] during the pandemic). Median age of individuals initiating SSHM was 40 (interquartile interval 33-48) with 61.7% (N = 919) male sex. Before the pandemic, there was a small trend of increased prescribing (incidence rate ratio [IRR] per period 1.002; 95% confidence interval [95CI] 1.001-1.002; p<0.001), with a change in level (immediate increase) at the pandemic date (relative increase in IRR 1.674; 95CI 1.206-2.322; p = 0.002). The trend during the pandemic was not statistically significant (relative increase in IRR 1.000; 95CI 1.000-1.001; p = 0.251). We estimated 511 (95CI 327-695) new dispensations would not have occurred without the pandemic. The pandemic led to an abrupt increase in SSHM prescribing in Ontario, although the rate of increase was similar before and during the pandemic. The absolute number of individuals who accessed SSHM remained low throughout the pandemic.

Echo state network-based metamodel for the problem of stagnant petroleum cooling in pipe-in-pipe flowlines

The low temperatures found in subsea water combined with the transport of hydrocarbons in flowlines that are immersed in this environment brings the problem of solid deposition inside the flowlines, mainly of paraffin and hydrates, which consists of considerable problems to the petroleum industry. To overcome this issue, computational simulation might be an ally. However, such simulations sometimes can be computationally demanded, making their use unpractical. In these cases, metamodels, which are computationally cheaper models, can be an alternative. The present paper proposes using an Echo State Network as a surrogate model for the problem of stagnant production fluid cooling in a subsea pipe-in-pipe system with active electric heating. The implemented metamodel shows to be twenty-four times faster than the Finite Volume Method, although it showed consistent correlations for all lags when a residual autocorrelation analysis was performed.

Ecological drivers for poultry farms predisposed to highly pathogenic avian influenza virus infection during the initial phase of the six outbreaks between 2010-2021: a nationwide study in South Korea.

Highly pathogenic avian influenza (HPAI) has caused substantial economic losses worldwide. An understanding of the environmental drivers that contribute to spillover transmission from wild birds to poultry farms is important for predicting areas at risk of introduction and developing risk-based surveillance strategies. We conducted an epidemiological study using data from six HPAI outbreak events in South Korea. An aggregate-level study design was implemented using third-level administrative units in South Korea. Only regions with high natural reservoir suitability were included. The incidence of HPAI at chicken and duck farms during the initial phase (30 and 45 days after the first case) of each outbreak event was used as the outcome variable, assuming that cross-species transmission from wild birds was the dominant exposure leading to infection. Candidate environmental drivers were meteorological factors, including temperature, precipitation, humidity, and altitude, as well as the proportion of protected area, farm density, deforestation level, and predator species richness. Logistic regression models were implemented; conditional autoregression models were used in cases of spatial autocorrelation of residuals. Lower temperature, higher farm density, and lower predator species richness were significantly associated with a higher risk of HPAI infection on chicken farms. Lower temperature, higher proportion of protected area, and lower predator species richness were significantly associated with a higher risk of HPAI infection on duck farms. The predicted dominant transmission routes on chicken and duck farms were horizontal and spillover, respectively. These results reveal a potential protective effect of predator species richness against HPAI outbreaks. Further studies are required to confirm a causal relationship.