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Car-following behavior modeling is critical for understanding traffic flow dynamics and developing high-fidelity microscopic simulation models. Most existing impulse-response car-following models prioritize computational efficiency and interpretability by using a parsimonious nonlinear function based on immediate preceding state observations. However, this approach disregards historical information, limiting its ability to explain real-world driving data. Consequently, serially correlated residuals are commonly observed when calibrating these models with actual trajectory data, hindering their ability to capture complex and stochastic phenomena. To address this limitation, we propose a dynamic regression framework incorporating time series models, such as autoregressive processes, to capture error dynamics. This statistically rigorous calibration outperforms the simple assumption of independent errors and enables more accurate simulation and prediction by leveraging higher-order historical information. We validate the effectiveness of our framework using HighD and OpenACC data, demonstrating improved probabilistic simulations. In summary, our framework preserves the parsimonious nature of traditional car-following models while offering enhanced probabilistic simulations. The code of this work is available at https://github.com/Chengyuan-Zhang/IDM_Bayesian_Calibration. • Novel calibration for car-following models using dynamic regression framework. • Framework maintains a parsimonious nature with better probabilistic simulations. • Efficient probabilistic simulation with reliable uncertainty quantification. • Captures stochastic driving behavior, replicating real-world traffic. [ABSTRACT FROM AUTHOR]

In the last decade, tourists gradually became more connected, gathering information about their destinations on the Internet. This suggests that the readily available search data is increasingly more relevant for up to date descriptive and predictive analysis regarding tourism data. This benefits the tourism sector, which is a very important sector for the economy of many countries, including Portugal. In this work, the relation between guests' data in tourism accommodation in Portugal and the search data of the main destinations in the country is analyzed for the last few years. The search data used is from Google Trends, considering different lag values. Short horizon tourist demand forecasts were produced considering exponential smoothing (ETS), autoregressive integrated moving average (ARIMA) and dynamic regression models, through a cross-validation experimental setup. It was evaluated if the inclusion of search data improved the forecasts. The study was performed considering the eight main guests' countries of origin, which are: Portugal, Great Britain, Ireland, Spain, France, Germany, Brazil, and Italy. The results indicate correlation between moderate and very strong for all countries. Furthermore, it was possible to observe that the correlation was higher for almost all the countries when the search data considered was lagged between 0 and 3 months. Regarding the comparative analysis between the forecasting models, the ARIMA models presented the best results for most of the countries. [ABSTRACT FROM AUTHOR]

Abstract Coupling microbial dynamics with the complete dynamics of the packaging gases is still a challenge. In this work the microbial growth kinetic parameters for Pseudomonas and Lactic Acid Bacteria (LAB) in MAP are identified based on accurate estimation of diffusivity of gases and parameter scaled sensitivity approaches. The microbial dynamics are also compared with those estimated based on partial pressure measurement. Scaled sensitivity coefficient analysis using dissolved gases as variable inputs, shows that in most cases the only coefficients large enough for estimation were those for CO 2max-diss , and for μ max. The current data showed that dissolved gases led significant differences on the microbial parameter of CO 2max values when compared with the headspace gases. On the other hand, the (so-called) dissolved specific growth rate follows a clear trend down for both microorganisms in relation to the increase of the initial headspace CO 2. Finally, current results indicate a possible correlation between CO 2max - diss , CO 2max - headspace , and μ max as functions of CO 2init. [ABSTRACT FROM AUTHOR]

• Resistance to fluoroquinolones and cephalosporins in Escherichia coli was mainly correlated with fluoroquinolone consumption. • Resistance to fluoroquinolones and cephalosporins in E. coli was negatively correlated with anti-pseudomonal activity penicillin with beta-lactamase inhibitors consumption. • The predictive abilities of dynamic regression models are moderate. • The results should be used with caution for the implementation of antimicrobial stewardship programs. The aim of this study was to determine the correlation between antimicrobial consumption (AMC) and antimicrobial resistance (AMR) in Escherichia coli at a hospital level, and assess the capacity of dynamic regression (DR) models to predict AMR for their use in deployment of antimicrobial stewardship programs (ASPs). A retrospective epidemiological study was conducted in a French tertiary hospital between 2014 and 2019. DR models were used to assess the correlation between AMC and AMR from 2014 to 2018. The predictive abilities of the models were estimated by comparing the predicted data with those observed in 2019. Rates of fluoroquinolone and cephalosporin resistance decreased. AMC increased overall but decreased for fluoroquinolone. DR models highlighted that the decrease in use of fluoroquinolone and the increase in use of anti-pseudomonal activity penicillin with beta-lactamase inhibitor (AAPBI) explained 54% of the decrease in fluoroquinolone resistance and 15% of the decrease in cephalosporin resistance. In addition, penicillin/beta-lactamase inhibitor (PBI) consumption explained 53% of PBI resistance, and beta-lactam use explained 36% of penicillin resistance, with both remaining stable over time. DR models had predictive capabilities with margins of error from 8% to 34%. Over a six-year period in a French tertiary hospital, decreasing rates of resistance to fluoroquinolones and cephalosporins were correlated with decreasing use of fluoroquinolone and increasing use of AAPBI, whereas rates of resistance to penicillin remained high and stable. The results indicate that DR models should be used with caution for AMR forecasting and ASP implementation. [ABSTRACT FROM AUTHOR]

Identification of the most relevant factors for explaining road accident occurrence is an important issue in road safety research, particularly for future decision-making processes in transport policy. However model selection for this particular purpose is still an ongoing research. In this paper we propose a methodological development for model selection which addresses both explanatory variable and adequate model selection issues. A variable selection procedure, TIM (two-input model) method is carried out by combining neural network design and statistical approaches. The error structure of the fitted model is assumed to follow an autoregressive process. All models are estimated using Markov Chain Monte Carlo method where the model parameters are assigned non-informative prior distributions. The final model is built using the results of the variable selection. For the application of the proposed methodology the number of fatal accidents in Spain during 2000–2011 was used. This indicator has experienced the maximum reduction internationally during the indicated years thus making it an interesting time series from a road safety policy perspective. Hence the identification of the variables that have affected this reduction is of particular interest for future decision making. The results of the variable selection process show that the selected variables are main subjects of road safety policy measures. [ABSTRACT FROM AUTHOR]

Summary: Objectives: Influenza seasonality remains poorly studied in Equatorial regions. Here we assessed the seasonal characteristics and environmental drivers of influenza epidemics in French Guiana, where influenza surveillance was established in 2006. Methods: Sentinel GPs monitored weekly incidence of Influenza-like illnesses (ILI) from January 2006 through December 2010 and collected nasopharyngeal specimens from patients for virological confirmation. Times series analysis was used to investigate relationship between ILI and climatic parameters (rainfall and specific humidity). Results: Based on 1533 viruses identified during the study period, we observed marked seasonality in the circulation of influenza virus in the pre-pandemic period, followed by year-round activity in the post-pandemic period, with a peak in the rainy season. ILI incidence showed seasonal autoregressive variation based on ARIMA analysis. Multivariate dynamic regression revealed that a 1 mm increase of rainfall resulted in an increase of 0.33% in ILI incidence one week later, adjusting for specific humidity (SH). Conversely, an increase of 1 g/kg of SH resulted in a decrease of 11% in ILI incidence 3 weeks later, adjusting for rainfall. Conclusions: Increased rainfall and low levels of specific humidity favour influenza transmission in French Guiana. [Copyright &y& Elsevier]

Abstract: Nephrops landings from Spanish trawlers in Porcupine bank (West of Ireland) have decreased a 90% in the last 25 years. With the aim of studying the causes of this situation, the relation between CPUE (1979–2005) and several environmental and population factors is investigated through a monthly time series approach. Discards in the fishery are negligible. As LPUE time series exhibits a seasonal pattern, a seasonal decomposition of the CPUE is carried out showing a decreasing trend from 1982 to 2003. The LPUE series is modelized resulting in an autoregressive and moving average model (ARMA) that highlights a monthly positive and seasonal negative autodependence. Population and environmental parameters (i.e. recruits per unit effort “RPUE”, sex ratio, ovigerous females’ proportion, North Atlantic Oscillation index “NAO” and Atlantic Multidecadal Oscillation index “AMO”) were tested at different lags as independent variables of the ARMA model. The outcoming multivariated dynamic ARMA model states that CPUE is positively related to sex ratio lagged by 8 years and negatively related to the NAO index from 6.5 years before. In accordance with those results 1978, 1983, 1986, 1989, 1992, 1995 and 1996 Porcupine Bank stormy, warm and wet conditions would be linked to the 1984, 1989, 1993, 1996, 1998, 2001 and 2003 Nephrops CPUE low values. Those results show an expectable model in relation to Nephrops density in which environmental conditions would have a relevant role. [Copyright &y& Elsevier]

Research has shown that the temporal focus of individuals can have a real effect on behavior. In the context of the COVID-19 pandemic, this study posits that temporal focus will affect adherence behavior regarding health control measures, such as social distancing, hand washing and mask wearing, which will be manifested through the degree of spread of COVID-19. It is suggested that social media can provide an indicator of the general temporal focus of the population at a particular time. In this study, we examine the temporal focus of Twitter text data and the number of COVID-19 cases in the US over a 317-day period from the inception of the pandemic, using text analytics to classify the temporal content of 0.76 million tweets. The data is then analyzed using dynamic regression via advanced ARIMA modelling, differencing the data, removing weekly seasonality and creating a stationary time series. The result of the dynamic regression finds that past orientation does indeed have an effect on the growth of COVID-19 cases in the US. However, a present focus tends to reduce the spread of COVID cases. Future focus had no effect in the model. Overall, the research suggests that detecting and managing temporal focus could be an important tool in managing public health during a pandemic. • Examines temporal focus of US social media users and COVID infection rates. • Applies text analytics and dynamic regression via ARIMA on 0.76 million tweets. • Finds that past temporal focus is related to the growth of COVID-19 cases. • Present focus was related to a reduction in the spread of COVID cases. • Temporal focus could be helpful in managing public health during the pandemic. [ABSTRACT FROM AUTHOR]