UNASSIGNED: Rescuing individuals at sea is a pressing global public health issue, garnering substantial attention from emergency medicine researchers with a focus on improving prevention and control strategies. This study aims to develop a Dynamic Bayesian Networks (DBN) model utilizing maritime emergency incident data and compare its forecasting accuracy to Auto-regressive Integrated Moving Average (ARIMA) and Seasonal Auto-regressive Integrated Moving Average (SARIMA) models.
UNASSIGNED: In this research, we analyzed the count of cases managed by five hospitals in Hainan Province from January 2016 to December 2020 in the context of maritime emergency care. We employed diverse approaches to construct and calibrate ARIMA, SARIMA, and DBN models. These models were subsequently utilized to forecast the number of emergency responders from January 2021 to December 2021. The study indicated that the ARIMA, SARIMA, and DBN models effectively modeled and forecasted Maritime Emergency Medical Service (EMS) patient data, accounting for seasonal variations. The predictive accuracy was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R 2) as performance metrics.
UNASSIGNED: In this study, the ARIMA, SARIMA, and DBN models reported RMSE of 5.75, 4.43, and 5.45; MAE of 4.13, 2.81, and 3.85; and R 2 values of 0.21, 0.54, and 0.44, respectively. MAE and RMSE assess the level of difference between the actual and predicted values. A smaller value indicates a more accurate model prediction. R 2 can compare the performance of models across different aspects, with a range of values from 0 to 1. A value closer to 1 signifies better model quality. As errors increase, R 2 moves further from the maximum value. The SARIMA model outperformed the others, demonstrating the lowest RMSE and MAE, alongside the highest R 2, during both modeling and forecasting. Analysis of predicted values and fitting plots reveals that, in most instances, SARIMA\'s predictions closely align with the actual number of rescues. Thus, SARIMA is superior in both fitting and forecasting, followed by the DBN model, with ARIMA showing the least accurate predictions.
UNASSIGNED: While the DBN model adeptly captures variable correlations, the SARIMA model excels in forecasting maritime emergency cases. By comparing these models, we glean valuable insights into maritime emergency trends, facilitating the development of effective prevention and control strategies.

OBJECTIVE: Online platforms have transformed gambling into a daily activity for many, raising concerns about its potential harm. Notably, marketing strategies play a crucial role in influencing gambling behaviors and normalizing gambling. This study aims to explore the relationship between monthly marketing expenditure by the gambling industry, the online amount of money bet, and the number of online accounts (active and new) in Spain. A secondary goal is to assess the impact of marketing restrictions under the Spanish Royal Decree 958/2020 on the relationship between marketing and online gambling behavior.
METHODS: Longitudinal study.
METHODS: Data covering January 2013 to December 2023. Dependent variables included: new accounts, active accounts, gambler deposits, and the total money bet. Independent variables included: expenditure on advertising, bonuses, affiliate marketing, and sponsorship. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was employed to assess marketing\'s impact on online gambling behavior.
RESULTS: Findings show that investment in advertising (P ≤ 0.025), promotions (P < 0.001), and sponsorships (P ≤ 0.004) significantly increase the number of new and active accounts, deposits, and total money bet. For instance, it has been estimated that, for every €1 invested in bonuses and sponsorship, gamblers deposit €1.6 and €4 into their accounts, respectively. Moreover, the Spanish law regulating gambling advertising has seemingly weakened the link between marketing expenditure and gambling behavior, with the notable exception of bonuses, where the impact has intensified.
CONCLUSIONS: These results underline the importance of ongoing monitoring and regulation of gambling behavior in Spain, emphasizing the need for strict adherence to regulations.

Syphilis remains a serious public health problem in mainland China that requires attention, modelling to describe and predict its prevalence patterns can help the government to develop more scientific interventions. The seasonal autoregressive integrated moving average (SARIMA) model, long short-term memory network (LSTM) model, hybrid SARIMA-LSTM model, and hybrid SARIMA-nonlinear auto-regressive models with exogenous inputs (SARIMA-NARX) model were used to simulate the time series data of the syphilis incidence from January 2004 to November 2023 respectively. Compared to the SARIMA, LSTM, and SARIMA-LSTM models, the median absolute deviation (MAD) value of the SARIMA-NARX model decreases by 352.69%, 4.98%, and 3.73%, respectively. The mean absolute percentage error (MAPE) value decreases by 73.7%, 23.46%, and 13.06%, respectively. The root mean square error (RMSE) value decreases by 68.02%, 26.68%, and 23.78%, respectively. The mean absolute error (MAE) value decreases by 70.90%, 23.00%, and 21.80%, respectively. The hybrid SARIMA-NARX and SARIMA-LSTM methods predict syphilis cases more accurately than the basic SARIMA and LSTM methods, so that can be used for governments to develop long-term syphilis prevention and control programs. In addition, the predicted cases still maintain a fairly high level of incidence, so there is an urgent need to develop more comprehensive prevention strategies.

BACKGROUND: Influenza is a highly contagious respiratory disease that presents a significant challenge to public health globally. Therefore, effective influenza prediction and prevention are crucial for the timely allocation of resources, the development of vaccine strategies, and the implementation of targeted public health interventions.
METHODS: In this study, we utilized historical influenza case data from January 2013 to December 2021 in Fuzhou to develop four regression prediction models: SARIMA, Prophet, Holt-Winters, and XGBoost models. Their predicted performance was assessed by using influenza data from the period from January 2022 to December 2022 in Fuzhou. These models were used for fitting and prediction analysis. The evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), were employed to compare the performance of these models.
RESULTS: The results indicate that the epidemic of influenza in Fuzhou exhibits a distinct seasonal and cyclical pattern. The influenza cases data displayed a noticeable upward trend and significant fluctuations. In our study, we employed SARIMA, Prophet, Holt-Winters, and XGBoost models to predict influenza outbreaks in Fuzhou. Among these models, the XGBoost model demonstrated the best performance on both the training and test sets, yielding the lowest values for MSE, RMSE, and MAE among the four models.
CONCLUSIONS: The utilization of the XGBoost model significantly enhances the prediction accuracy of influenza in Fuzhou. This study makes a valuable contribution to the field of influenza prediction and provides substantial support for future influenza response efforts.

UNASSIGNED: Commercial air travel can result in global dispersal of infectious diseases. During the coronavirus disease 2019 (COVID-19) pandemic, many countries implemented border measures, including restrictions on air travel, to reduce the importation risk of COVID-19. In the context of inbound air travel to Canada, this study aimed to: 1) characterize travel trends before and during the pandemic, and 2) statistically assess the association between travel volumes and travel restrictions during the pandemic.
UNASSIGNED: Monthly commercial air travel volume data from March 2017 to February 2023 were obtained from the International Air Transport Association (IATA). National and airport-level travel trends to Canada were characterized by inbound travel volumes, the number of countries contributing travellers and the ranking of the top ten countries contributing travellers across the study period, by six year-length subperiod groupings (three pre-pandemic and three pandemic). Using seasonal autoregressive integrated moving average (SARIMA) models, interrupted time series (ITS) analyses assessed the association between major travel restrictions and travel volumes by including variables to represent changes to the level and slope of the time series.
UNASSIGNED: The pre-pandemic inbound travel volume increased by 3% to 7% between consecutive subperiods, with three seasonal peaks (July-August, December-January, March). At the onset of the pandemic, travel volume decreased by 90%, with the number of contributing countries declining from approximately 200 to 140, followed by a slow recovery in volume and seasonality. A disruption in the ranking of countries that contributed travellers was also noticeable during the pandemic. Results from the ITS analysis aligned with the timing of travel restrictions as follows: implementation in March 2020 coincided with a sharp reduction in volumes, while the easing of major restrictions, starting with the authorization of fully vaccinated travellers from the United States to enter Canada in August 2021, coincided with an increase in the slope of travel volumes. Descriptive and statistical results suggest a near-return of pre-pandemic travel patterns by the end of the study period.
UNASSIGNED: Study results suggest resilience in commercial air travel into Canada. Although the COVID-19 pandemic led to a disruption in travel trends, easing of travel restrictions appeared to enable pre-pandemic trends to re-emerge. Understanding trends in air travel volumes, as demonstrated here, can provide information that supports preparedness and response regarding importation risk of infectious pathogens.

UNASSIGNED: Light of recent global upheavals, including volatile oil prices, the Russo-Ukrainian conflict, and the COVID-19 pandemic this study delves into their profound impact on the import and export dynamics of global foodstuffs. With rising staple food prices reminiscent of the 2010-2011 global food crisis, understanding these shifts comprehensively is imperative.
UNASSIGNED: Our objective is to evaluate this impact by examining six independent variables (year, month, Brent crude oil, COVID-19, the Russo-Ukrainian conflict) alongside six food indicators as dependent variables. Employing Pearson\'s correlation, linear regression, and seasonal autoregressive integrated moving averages (SARIMA), we scrutinize intricate relationships among these variables.
UNASSIGNED: Our findings reveal varying degrees of association, notably highlighting a robust correlation between Brent crude oil and food indicators. Linear regression analysis suggests a positive influence of the Russo-Ukrainian conflict, Brent oil on food price indices, and COVID-19. Furthermore, integrating SARIMA enhances predictive accuracy, offering insights into future projections.
UNASSIGNED: Finally, this research has a significant role in providing a valuable analysis into the intricate dynamics of global food pricing, informing decision-making amidst global challenges and bridging critical gaps in prior research on forecasting food price indices.

BACKGROUND: Respiratory Syncytial Virus (RSV) is responsible for the majority of acute lower respiratory infections in infants and can affect also older age groups. Restrictions linked to the emergence of the SARS-CoV-2 pandemic and their subsequent lifting caused a change in the dynamics of RSV circulation. It is therefore fundamental to monitor RSV seasonal trends and to be able to predict its seasonal peak to be prepared to the next RSV epidemics.
METHODS: We performed a retrospective descriptive study on laboratory-confirmed RSV infections from Bambino Gesù Children\'s Hospital in Rome from 1st January 2018 to 31st December 2022. Data on RSV-positive respiratory samples (n = 3,536) and RSV-confirmed hospitalizations (n = 1,895) on patients aged 0-18 years were analyzed. In addition to this, a SARIMA (Seasonal AutoRegressive Integrated Moving Average) forecasting model was developed to predict the next peak of RSV.
RESULTS: Findings show that, after the 2020 SARS-CoV-2 pandemic season, where RSV circulation was almost absent, RSV infections presented with an increased and anticipated peak compared to pre-pandemic seasons. While mostly targeting infants below 1 year of age, there was a proportional increase in RSV infections and hospitalizations in older age groups in the post-pandemic period. A forecasting model built using RSV weekly data from 2018 to 2022 predicted the RSV peaks of 2023, showing a reasonable level of accuracy (MAPE 33%). Additional analysis indicated that the peak of RSV cases is expected to be reached after 4-5 weeks from case doubling.
CONCLUSIONS: Our study provides epidemiological evidence on the dynamics of RSV circulation before and after the COVID-19 pandemic. Our findings highlight the potential of combining surveillance and forecasting to promote preparedness for the next RSV epidemics.

Electric vehicles (EVs) are the future of the automobile industry, as they produce zero emissions and address environmental and health concerns caused by traditional fuel-poared vehicles. As more people shift towards EVs, the demand for power consumption forecasting is increasing to manage the charging stations effectively. Predicting power consumption can help optimize operations, prevent grid overloading, and power outages, and assist companies in estimating the number of charging stations required to meet demand. The paper uses three time series models to predict the electricity demand for charging stations, and the SARIMA (Seasonal Auto Regressive Integrated Moving Average) model outperforms the ARMA (Auto Regressive Moving Average) and ARIMA (Auto Regressive Integrated Moving Average) models, with the least RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) scores in forecasting power demand and revenue. The data used for validation consists of charging activities over a four-year period from public charging outlets in Colorado, six months of charging data from ChargeMOD\'s public charging terminals in Kerala, India. Power usage is also forecasted based on wheels of vehicles, and finally, a plan subscription data from the same source is utilized to anticipate income, that helps companies develop pricing strategies to maximize profits while remaining competitive. Utility firms and charging networks may use accurate power consumption forecasts for a variety of purposes, such as power scheduling and determining the expected energy requirements for charging stations. Ultimately, precise power consumption forecasting can assist in the effective planning and design of EV charging infrastructure. The main aim of this study is to create a good time series model which can estimate the electric vehicle charging stations usage of power and verify if the firm has a good income along with some accuracy measures. The results show that SARIMA model plays a vital role in providing us with accurate information. According to the data and study here, four wheelers use more power than two and three wheelers. Also, DC charging facility uses more electricity than AC charging stations. These results can be used to determine the cost to operate the EVs and its subscriptions.

BACKGROUND: Public health preparedness is based on timely and accurate information. Time series forecasting using disease surveillance data is an important aspect of preparedness. This study compared two approaches of time series forecasting: seasonal auto-regressive integrated moving average (SARIMA) modelling and the artificial neural network (ANN) algorithm. The goal was to model weekly seasonal influenza activity in Canada using SARIMA and compares its predictive accuracy, based on root mean square prediction error (RMSE) and mean absolute prediction error (MAE), to that of an ANN.
METHODS: An initial SARIMA model was fit using automated model selection by minimizing the Akaike information criterion (AIC). Further inspection of the autocorrelation function and partial autocorrelation function led to \'manual\' model improvements. ANNs were trained iteratively, using an automated process to minimize the RMSE and MAE.
RESULTS: A total of 378, 462 cases of influenza was reported in Canada from the 2010-2011 influenza season to the end of the 2019-2020 influenza season, with an average yearly incidence risk of 20.02 per 100,000 population. Automated SARIMA modelling was the better method in terms of forecasting accuracy (per RMSE and MAE). However, the ANN correctly predicted the peak week of disease incidence while the other models did not.
CONCLUSIONS: Both the ANN and SARIMA models have shown to be capable tools in forecasting seasonal influenza activity in Canada. It was shown that applying both in tandem is beneficial, SARIMA better forecasted overall incidence while ANN correctly predicted the peak week.

Hemorrhagic fever with renal syndrome (HFRS) is one of the 10 major infectious diseases that jeopardize human health and is distributed in more than 30 countries around the world. China is the country with the highest number of reported HFRS cases worldwide, accounting for 90% of global cases. The incidence level of HFRS in Quzhou is at the forefront of Zhejiang Province, and there is no specific treatment for it yet. Therefore, it is crucial to grasp the epidemiological characteristics of HFRS in Quzhou and establish a prediction model for HFRS to lay the foundation for early warning of HFRS.
Descriptive epidemiological methods were used to analyze the epidemic characteristics of HFRS, the incidence map was drawn by ArcGIS software, the Seasonal AutoRegressive Integrated Moving Average (SARIMA) and Prophet model were established by R software. Then, root mean square error (RMSE) and mean absolute error (MAE) were used to evaluate the fitting and prediction performances of the model.
A total of 843 HFRS cases were reported in Quzhou City from 2005 to 2022, with the highest annual incidence rate in 2007 (3.93/100,000) and the lowest in 2022 (1.05/100,000) (P trend<0.001). The incidence is distributed in a seasonal double-peak distribution, with the first peak from October to January and the second peak from May to July. The incidence rate in males (2.87/100,000) was significantly higher than in females (1.32/100,000). Farmers had the highest number of cases, accounting for 79.95% of the total number of cases. The incidence is high in the northwest of Quzhou City, with cases concentrated on cultivated land and artificial land. The RMSE and MAE values of the Prophet model are smaller than those of the SARIMA (1,0,1) (2,1,0)12 model.
From 2005 to 2022, the incidence of HFRS in Quzhou City showed an overall downward trend, but the epidemic in high-incidence areas was still serious. In the future, the dynamics of HFRS outbreaks and host animal surveillance should be continuously strengthened in combination with the Prophet model. During the peak season, HFRS vaccination and health education are promoted with farmers as the key groups.