Bayesian Structural Time Series Research Articles

Non-fatal suicide behaviours across phases in the COVID-19 pandemic: a population-based study in a Catalan cohort

The COVID-19 pandemic has been extensively discussed in the context of its effect on mental health. Although global suicide rates have remained stable during the pandemic, the specific effect on non-fatal suicide behaviours during and after the pandemic remains underexplored. This study aims to investigate patterns of non-fatal suicide behaviours before, during, and after the pandemic. In this cohort study, we used data from all hospitals in Catalonia, Spain, collected through the Catalan Suicide Risk Code, which is a specifically designed suicide attempt surveillance protocol, involving a face-to-face, in-depth psychiatric evaluation, after a Catalan resident presents any suicide risk behaviour in any public health-care setting. This evaluation centralises data from suicide registries across the territory. We included non-fatal suicide behaviours, meaning suicidal ideation or attempts that did not result in death, and excluded self-harm behaviours not judged to be linked with suicidal ideation. We considered three periods: the pre-confinement period (Jan 1, 2018, to the enforcement of the lockdown in Spain on March 14, 2020); the confinement period (March 14, 2020, to the end of lockdown on June 21, 2020); and the post-confinement period (June 21, 2020, to Dec 31, 2022). We used Bayesian structural time series models to assess the effect of pandemic phases on non-fatal suicide behaviours, and we ran stratified analyses by sex and age to identify distinct patterns among demographic cohorts. We obtained 26 482 records from Jan 1, 2018, to Dec 31, 2022. The mean age was 37·94 years (SD 18·07), and the sample included 17 584 (66·4%) women and 8898 (33·6%) men. Data on ethnicity were not collected. Temporal trends showed a mild increase in non-fatal suicide behaviours from Jan 1, 2018, to March 13, 2020; a reduction during the confinement period; and a subsequent rise after confinement. Bayesian models suggested a significant causal effect of lockdown easing, resulting in a 50·77% increase in non-fatal suicide behaviours (95% credible interval [CrI] 26·62-76·58; p<0·0001). Stratified analyses indicated that the easing of lockdown resulted in a significant increase in non-fatal suicide behaviours among women (25·92%; 6·71-44·72; p=0·011) and among individuals aged 18 years and younger (72·75%; 38·81-108·11; p<0·0001). This study provides a comprehensive examination of non-fatal suicide behaviours in Catalonia, Spain, emphasising the dynamics of different COVID-19 pandemic phases. The initial reduction during strict lockdown aligns with Joiner's Interpersonal Theory of Suicide, whereas the post-confinement rise reflects complex factors, including social isolation and economic challenges. Sex-specific and age-specific analyses underscore distinct vulnerabilities, emphasising the need for targeted preventive strategies. Centro de Investigación Biomédica en Red de Salud Mental annual budget of G21, Agència de Gestió d'Ajuts Universitaris i de Recerca of the Generalitat de Catalunya. For the Catalan and Spanish translations of the abstract see Supplementary Materials section.

Glomerulonephritis following COVID-19 infection or vaccination: a multicenter study in South Korea.

Despite the widespread impact of the severe acute respiratory syndrome coronavirus 2 (coronavirus disease 2019, COVID-19) and vaccination in South Korea, our understanding of kidney diseases following these events remains limited. We aimed to address this gap by investigating the characteristics of glomerular diseases following the COVID-19 infection and vaccination in South Korea. Data from multiple centers were used to identify de novo glomerulonephritis (GN) cases with suspected onset following COVID-19 infection or vaccination. Retrospective surveys were used to determine the COVID-19-related histories of patients who were initially not implicated. Bayesian structural time series and autoregressive integrated moving average models were used to determine causality. Glomerular diseases occurred shortly after the infection or vaccination. The most prevalent postinfection GN was podocytopathy (42.9%), comprising primary focal segmental glomerulosclerosis and minimal change disease, whereas postvaccination GN mainly included immunoglobulin A nephropathy (IgAN; 57.9%) and Henoch-Schönlein purpura nephritis (HSP; 15.8%). No patient progressed to end-stage kidney disease. Among the patients who were initially not implicated, nine patients with IgAN/HSP were recently vaccinated against COVID-19. The proportion of glomerular diseases changed during the pandemic in South Korea, with an increase in acute interstitial nephritis and a decrease in pauci-immune crescentic GN. This study showed the characteristics of GNs following COVID-19 infection or vaccination in South Korea. Understanding these associations is crucial for developing effective patient management and vaccination strategies. Further investigation is required to fully comprehend COVID-19's impact on GN.

The Iberian exception: Estimating the impact of a cap on gas prices for electricity generation on consumer prices and market dynamics

As the volatility of short-term energy market prices increases due to exogenous shocks and the changing nature of the energy mix, market interventions are gaining importance in the policy debate. Accurate and robust quantification of their impact is becoming therefore essential. This paper conducts a causality analysis to evaluate the Spanish “gas cap” for electricity generation during the 2022 energy crisis. We use Bayesian structural time series models to isolate its impact on affected consumers, primarily those under the regulated tariff, from June to December 2022. Our results show that the mechanism successfully lowered prices compared to a counterfactual scenario without the intervention. However, the policy also led to an increase in electricity generation from gas-fired combined cycle plants. In addition, exports to France increased by over 80% after implementation, as Spanish wholesale prices became cheaper than French prices. Our analysis (1) provides an accurate quantification to date of one of the most consequential energy market interventions in recent years; (2) demonstrates the fruitful use of a novel evaluation method for energy policy; (3) highlights the trade-off between the short-term goal of consumer price relief and the long-term goals of fossil fuel reduction and decarbonisation.

The Celebrity Factor: Exploring the Impact of Influencers on COVID-19 Vaccine Sentiment through Bayesian Modeling of Time Series

Online social networks allow for information to rapidly propagate throughout the world, and opinions expressed on such platforms can influence people’s decisions. During the COVID-19 pandemic, many influential public figures used these social networks to share their opinions about the vaccines developed to combat the virus. Many influencers encouraged vaccination, and a considerable number also expressed doubt and skepticism over the efficacy of the vaccines. This study modeled the impact that eleven influencers’ statements had on the overall sentiment towards COVID-19 vaccines, as expressed on Twitter. Sentiment is measured by collecting a series of publicly-available tweets made regarding the vaccine during the pandemic, and assigning each a sentiment score based on the VADER lexicon. Several models were used to analyze the impact of the influencers’ statements, including linear, sequential and tree-based models. The results were obtained by constructing a Bayesian structural time series model based on each model’s counterfactual estimate. The results found that influencers who share messages encouraging vaccination generally tend to increase the number of ”pro-vaccination” tweets over the next 20 days. Influencers sharing ”anti-vaccination” messages sometimes resulted in a decrease in anti-vaccine tweets, and other times in an increase over the next 20 days. The results from this study provide an introductory look into the complex issue of vaccine hesitancy and the effect of influencers on vaccine messaging, and inform public health strategy regarding this issue.

Exploring Public Interest in Limited-Use Areas and Compensation from Airports in Poland: A Google Trends Analysis

Abstract This article evaluates online social behavior regarding the establishment of limited-use areas (LUAs) around the airports in Warsaw and Gdansk. The study relied mainly on an analysis of Google Trends statistics, in particular the dynamics of keyword searches. The article suggests that assessments of online behavior can provide a deeper understanding of social behavior. The study involved an OLS regression analysis and a causal impact analysis of the intervention based on a Bayesian structural time-series model. This article analyses different phases of an information society's activity, from the introduction of LUAs around airports to the deadline for submitting compensation claims. The results indicate that the number of searches for the keyword "compensation" increased significantly after the introduction of LUAs and that RSV decreased after the end of the compensation process, which confirms that the intervention significantly influenced the analyzed time series.

Contradictory Results: Reassessing the Impact of Loan‐to‐Value Restrictions on House Price Inflation

AbstractUsing Bayesian Structural Time Series analysis, this study examines the causal impact of loan‐to‐value (LTV) restrictions imposed by the Reserve Bank of New Zealand in October 2013. By incorporating state‐space components, such as local linear trend, seasonality and regression, counterfactual values of house price indices are predicted. Surprisingly, the study reveals that the implementation of LTV restrictions had no significant effect on national house price indices, contradicting prior Central Bank studies that reported a nearly 3 percentage‐point decrease in housing cost inflation. This contradictory evidence challenges existing perceptions of the effectiveness of LTV restrictions in curbing house price inflation.

COVID-19 impact on taxi-metro integrated usage: Changes and role of built environment, taking Lanzhou, China as an example

The COVID-19 pandemic has had a profound impact on how and how often people travel. The extent of changes in taxi-metro integrated usage (cooperative trips and competitive trips) before and after the pandemic is still unclear. Therefore, this paper aims to explore the impacts of COVID-19 on taxi-metro integrated usage and the relationship with built environment. This paper develops a novel method to infer the impact of COVID-19 on taxi-metro integrated usage and measures the resilience of cooperative and competitive ridership using a series of Bayesian structural time series models. The bootstrap aggregating regression tree model is applied to investigate the contribution of built environment on the relative impacts and resilience. Partial Dependence Plots (PDP) analysis is employed to explore the nonlinear effects of built environment elements. Results suggests: (1) The travel restrictions have a more significant impact than the number of COVID-19 cases on taxi-metro integrated usage. (2) Built environment factors contribute similarly to resisting the impact of COVID-19 on cooperative and competitive ridership during short-term and long-term period. (3) The recovery of the ridership shows a significant spatial heterogeneity. (4) Variables such as residential ratio, road network density, and bus stop density show nonlinear and threshold effects on the resilience of the ridership. Our findings have implications for travel policies during recovery from the pandemic.

Long-term impact of COVID-19 pandemic on antibiotic use in primary care: lessons to optimize antimicrobial use

ABSTRACT Background Lockdowns due to the COVID-19 pandemic was associated with changes in the pattern of use of antimicrobials, but persistence of changes after lockdowns has not been described. The objective was to describe the number of patients with dispensed antibiotic treatment and consumption of antibiotics in outpatients from primary care in Catalonia 18 months after the end of the emergency period. Research design and methods Data for the COVID-19 pandemic period was obtained from March 2020 to December 2021. Four high transmission rate (Rt > 100) periods were established. To compare data, a simple Bayesian structural time series model was used. Results The observed number of patients with dispensed antibiotics decreased respect to the estimated, especially during the four high transmission rate periods: April–May 2020 (lockdown period) (−42.57% and −42.68%); December 2020-February 2021 (−41.65%, −49.97% and −43.64%); October 2021 (−16.23%), and December 2021 (−20.16%). Overall antibiotic consumption was reduced by 23.37% (p = 0.002). These differences were mainly observed in those ≤ 15 years. Conclusions We describe the reduction in the number of patients with dispensed antibiotics and antibiotic consumption after the COVID-19 lockdown persisted in a period of recovery of healthcare accessibility. This information may help to improve antimicrobial use at the primary care level.

Causal impact analysis of weir opening on cyanobacterial blooms and water quality in the Yeongsan River, Korea: A bayesian structural time-series analysis and median difference test

The construction of weirs in Korea's Four Major Rivers Project has led to an increase in cyanobacterial blooms, posing environmental challenges. To address this, the government began opening weirs in 2017. However, interpreting experimental results has proven to be complex due to the multifaceted nature of blooms. This study aimed to assess the impact of opening the Juksan Weir on cyanobacterial blooms and water quality in the Yeongsan River. Using a median difference test (MDT) and causal impact analysis (CIA) with Bayesian structural time-series (BSTS) models, changes in cyanobacterial cell density (Cyano) and chlorophyll a concentration (Chl-a) before (January 2013 to June 2017) and after (July 2017 to December 2021) the weir-opening event were analyzed. The MDT revealed no significant change in Cyano post-weir opening (p = 0.267), but Chl-a significantly increased by 48.1 % (p < 0.01). As a result of CIA, Cyano decreased, albeit statistically insignificantly (p = 0.454), while Chl-a increased by 59.0 % (p < 0.01). These findings contradict the expectation that Cyano decrease due to the increased flow velocity resulting from weir opening. The absence of changes in Cyano and the increase in Chl-a can be attributed to several factors, including the constrained and inadequate duration of full weir opening combined with conducive conditions for the proliferation of other algae such as diatoms and green algae. These findings suggest that the effectiveness of weir opening in controlling Cyano may have been compromised by factors influencing the overall aquatic ecosystem dynamics. Further analysis revealed that factors such as elevated water temperatures (≥ 30 °C) and reduced flow rates (< 37 m3/s) contributed to the flourishing of cyanobacteria, whose concentrations exceeded 10,000 cells/mL. In analyzing causal relationships in environmental management, especially when there are complex causal interactions, the application of MDT and CIA provides valuable insights.

Bayesian Structural Time Series Models for Predicting the $${\textrm{CO}}_2$$ Emissions in Afghanistan

Bayesian Structural Time Series Models for Predicting the $${\textrm{CO}}_2$$ Emissions in Afghanistan

"Breaking the silence" suicide Prevention media campaign in Oregon: Evaluation of impact on help-seeking and suicide mortality.

Research has established that suicide-related media can impact suicide rates both positively and negatively, supporting efforts to engage the media in the service of suicide prevention. The goal of the current study is to evaluate the impact of a suicide prevention media campaign implemented April 7-14, 2019 in Oregon. Several indices of help-seeking behavior and suicide risk were employed: suicide-related Google Health API searches, National Suicide Prevention Lifeline (Lifeline) (currently known as the 988 Suicide and Crisis Lifeline) call volume, and state suicide mortality data from April 7, 2016-May 6, 2019. Eight states with similar 2016-2018 average suicide rates were compared with Oregon. Bayesian structural time-series modeling in R was used to test intervention effects. During the 30 days following the start of the campaign, there was a significant increase in Lifeline calls from Oregon area codes (2488 observed vs. 2283 expected calls, p = 0.03). There were no significant changes in suicide mortality or suicide-related Google searches in Oregon. The campaign appeared to increase help-seeking behavior in the form of Lifeline calls, without any indication of an iatrogenic suicide contagion effect. However, the campaign's potential to reduce suicide mortality was unmet.

Assessing the impact of COVID-19 interventions on the hand, foot and mouth disease in Guangdong Province, China: a Bayesian modeling study.

The non-pharmaceutical interventions (NPIs) against COVID-19 may have affected the transmission of hand, foot and mouth disease (HFMD). We aimed to assess the impact of the NPIs on HFMD in the high epidemic area of HFMD, Guangdong Province. The data of HFMD cases, etiological information, and meteorological factors in Guangdong from January 1, 2012, to December 31, 2021, were collected. Using a Bayesian structural time series (BSTS) model integrated counterfactual framework, we assessed the effect of NPIs on HFMD by different intervention periods, populations (gender, age, occupation), and cities. We further explored the correlation between the reduction of HFMD and socioeconomic factors in 21 cities. A total of 351,217 HFMD cases were reported and 455,327 cases were averted in Guangdong Province during 2020-2021 with a reduction of 84.94% (95%CI: 81.63-87.22%) in 2020 and 29.49% (95%CI: 15.26-39.54%) in 2021. The impact of NPIs on HFMD differed by age and gender. The effects of NPIs were more remarkable for children aged 0-2 years and scattered children. We found that the relative reductions in 21 cities were related to the composition ratio of children and COVID-19 incidence. The reduction of HFMD incidence was significantly associated with COVID-19 NPIs, and school closure was an effective intervention to prevent HFMD outbreaks. Our findings will contribute to the development of HFMD prevention and control measures.

Evaluating the effects of the 2017 National Health Insurance coverage expansion on amenable mortality and its disparities between areas in South Korea using Bayesian structural time-series models

To improve the low coverage rate of the National Health Insurance (NHI), South Korea implemented the NHI coverage expansion plan in 2017 to cover medically essential non-covered services and reduce copayment rates. This study aimed to estimate the effects of the 2017 NHI coverage expansion on amenable mortality and its disparities between areas in South Korea under a controlled interrupted time-series design using Bayesian structural time-series models. Age-standardized amenable mortality rates and rate differences (RDs) and rate ratios (RRs) between areas for amenable mortality were calculated monthly between July 2012 and December 2021 and used as the response series. The non-equivalent control series were monthly non-avoidable mortality rates and their regional disparities. After the coverage expansion, amenable mortality rates decreased for both males (−8.8%, 95% credible interval [CrI] −13.4% to −3.9%) and females (−8.3%, 95% CrI −13.4% to −2.4%), with the largest decline in the non-Seoul-Capital metropolitan area (−11.6%, 95% CrI −16.5% to −6.3%) rather than the Seoul Capital Area (−7.5%, 95% CrI −11.9% to −2.5%) and a non-significant reduction in the non-Seoul-Capital non-metropolitan area in females. RDs and RRs between areas for amenable mortality decreased non-significantly (−16.2%, 95% CrI −31.3% to 2.6% for RD and −1.2%, 95% CrI −3.7% to 1.5% for RR), except for a significant decrease in RD in males (−21.8%, 95% CrI −38.0% to −1.5%), and decreased less in females than in males. The coverage expansion was generally effective in reducing amenable mortality rates by area, but had limited effects in closing amenable mortality disparities between areas, favoring males and the non-Seoul-Capital metropolitan area. These results implied that additional measures are necessary to improve access to quality health care for females and underserved areas to enhance the effectiveness of the coverage expansion.

The impact of mass vaccination policy and control measures on lumpy skin disease cases in Thailand: insights from a Bayesian structural time series analysis.

In 2021, Thailand reported the highest incidence of lumpy skin disease (LSD) outbreaks in Asia. In response to the widespread outbreaks in cattle herds, the government's livestock authorities initiated comprehensive intervention measures, encompassing control strategies and a national vaccination program. Yet, the efficacy of these interventions remained unevaluated. This research sought to assess the nationwide intervention's impact on the incidence of new LSD cases through causal impact analysis. Data on weekly new LSD cases in Thailand from March to September 2021 was analyzed. The Bayesian structural time series (BSTS) analysis was employed to evaluate the causal relationship between new LSD cases in the pre-intervention phase (prior to the vaccination campaign) and the post-intervention phase (following the vaccination campaign). The assessment involved two distinct scenarios, each determined by the estimated effective intervention dates. In both scenarios, a consistent decline in new LSD cases was observed after the mass vaccination initiative, while other control measures such as the restriction of animal movement, insect control, and the enhancement of the active surveillance approach remained operational throughout the pre-intervention and the post-intervention phases. According to the relative effect results obtained from scenario A and B, it was observed that the incidence of LSD cases exhibited reductions of 119% (95% Credible interval [CrI]: -121%, -38%) and 78% (95% CrI: -126, -41%), respectively. The BSTS results underscored the significant influence of these interventions, with a Bayesian one-sided tail-area probability of p < 0.05. This model-based study provides insight into the application of BSTS in evaluating the impact of nationwide LSD vaccination based on the national-level data. The present study is groundbreaking in two respects: it is the first study to quantify the causal effects of a mass vaccination intervention on the LSD outbreak in Thailand, and it stands as the only endeavor of its kind in the Asian context. The insights collected from this study hold potential value for policymakers in Thailand and other countries at risk of LSD outbreaks.

Bayesian Structural Time Series and Geographically Weighted Logistic Regression Modelling Impacts of COVID-19 Lockdowns on the Spatiotemporal Patterns of London’s Crimes

Given the paramount impacts of COVID-19 on people’s lives in the capital of the UK, London, it was foreseeable that the city’s crime patterns would have undergone significant transformations, especially during lockdown periods. This study aims to testify the crime patterns’ changes in London, using data from March 2020 to March 2021 to explore the driving forces for such changes, and hence propose data-driven insights for policy makers and practitioners on London’s crime deduction and prevention potentiality in post-pandemic era. (1) Upon exploratory data analyses on the overall crime change patterns, an innovative BSTS model has been proposed by integrating restriction-level time series into the Bayesian structural time series (BSTS) model. This novel method allows the research to evaluate the varied effects of London’s three lockdown periods on local crimes among the regions of London. (2) Based on the predictive results from the BSTS modelling, three regression models were deployed to identify the driving forces for respective types of crime experiencing significant increases during lockdown periods. (3) The findings solidified research hypotheses on the distinct factors influencing London’s specific types of crime by period and by region. In light of the received evidence, insights on a modified policing allocation model and supporting the unemployed group was proposed in the aim of effectively mitigating the surges of crimes in London.

Evaluating the impact of COVID-19 outbreak on hepatitis B and forecasting the epidemiological trend in mainland China: a causal analysis

BackgroundIt is uncertain how COVID-19 outbreak influences the hepatitis B epidemics. This study aims to evaluate the effects on hepatitis B owing to the COVID-19 outbreak and forecast the hepatitis B epidemiological trend in mainland China to speed up the course of the “End viral hepatitis Strategy”.MethodsWe estimated the causal impacts and created a forecast through adopting monthly notifications of hepatitis B each year from 2005 to 2020 in mainland China using the Bayesian structural time series (BSTS) method.ResultsThe hepatitis B epidemics fluctuates irregularly during the period 2005–2007(APC = 8.7, P = 0.246) and 2015–2020(APC = 1.7, P = 0.290), and there is a downturn (APC=-3.2, 95% CI -5.2 to -1.2, P = 0.006) from 2007 to 2015 in mainland China. The COVID-19 outbreak was found to have a monthly average reduction on the hepatitis B epidemics of 26% (95% CI 18–35%) within the first three months in 2020,17% (95% CI 7.7–26%) within the first six months in 2020, and 10% (95% CI19–22%) all year as a result of the COVID-19 outbreak, (probability of causal effect = 96.591%, P = 0.034) and the forecasts showed an upward trend from 2021 to 2025 (annual percentage change = 4.18, 95% CI 4.0 to 4.3, P < 0.001).ConclusionThe COVID-19 has a positive effect on the decline of hepatitis B cases. And the potential of BSTS model to forecast the epidemiological trend of the hepatitis B can be applied in automatic public health policymaking in mainland China.

Impact of organised colorectal cancer screening on age-specific population incidences: evidence from a quasi-experimental study in Sweden

Colorectal cancer (CRC) incurs a significant disease burden globally. Organised CRC screening programmes have been widely implemented for early detection and prevention. To understand the public health impact of these programmes, quantitative evidence of changes in overall and age-specific population incidences is fundamental. We aimed to provide such evidence by exploiting a time lag in the implementation of organised screening in Sweden: two out of 21 regions (these two regions comprise nearly 20% of the total Swedish population) have offered organised screening since 2008; the other regions have offered CRC screening since 2021. Using registry data on diagnosed CRC cases and socio-demographics for all regions in Sweden over the period 1970–2019, Bayesian structural time series modelling and difference-in-differences were applied to analyse the impact of screening on age-specific population incidences over time (CRC cases per 100.000 persons/year). After inviting birth-year cohorts aged 60–69 years for stool-based testing, the incidence rate in the 70–74-year age group decreased significantly over time, with an average reduction of − 44·40 (95% CI − 58·15 to − 31·31) from 2011 to 2019 in the intervention regions. In the overall population aged 60–74 years, there was a net incidence decrease of − 7·99 (95% CI − 13·85 to − 2·39) since the initiation of organised screening in the intervention regions (2008–2019). Organised CRC screening for 60–69-year-olds generated a change in age-specific incidence patterns with a long-lasting incidence decrease in the 70–74-year-old population, implying reductions in the excess mortality and burden of the disease.

Evaluation of the impact of the implementation of the new primary health care information system on records of patient care and home visits in Brazil

The replacement of the Primary Care Information System (SIAB, 1998-2015), as of January 2016, by the new Health Information System for Primary Care (SISAB) determined new forms of collecting, processing, and using information, with a possible impact on the records of activities carried out in primary health care in Brazil. This study aimed to evaluate the implementation impact of the new information system on records of physicians' and nurses' patient care and home visits of community health workers (CHW) in Brazil from 2007 to 2019. To this end, a Bayesian structural time-series model approach was used, based on a diffuse state-space regression. From 2016 to 2019, 463.47 million physician care, 210.61 million nursing care, and 1.28 billion CHW visits were recorded. Following the trend recorded before the implementation, 598.86 million, 430.46 million, and 1.5 billion physician and nursing appointments and CHW visits would be expected, respectively. In relative terms, there was a decrease of 25% in physician care, 51% in nursing care, and 15% in CHW visits when compared to the value expected by the Bayesian method. The negative impact on the records of patient care and home visits identified in this study, whether due to difficulties in adapting to the new system or a reduction in improper records, must be investigated so that the challenge of improving the primary care information system can be understood and overcome in a planned way.

FORECASTING USING SARIMA AND BAYESIAN STRUCTURAL TIME SERIES METHOD FOR RANGE SEASONAL TIME

Data containing seasonal patterns, the SARIMA and Bayesian Structural Time Series methods, are time series methods that can be used on this type of data. This research aims to determine the steps of the SARIMA model and Bayesian Structural Time Series, applying the SARIMA model and Structural Bayesians Time Series, get the forecasting results of the SARIMA model and Bayesian Structural Time Series with MAPE measurements. The research method used is a quantitative method applied to data on the number of PT KAI train passengers in the Java region for 2006-2019. The results of this research show that the best model for forecasting the number of PT KAI train passengers in the Java region in 2006-2019 is SARIMA (2,1,0)(0,1,2)[12] with a MAPE value of 4.77% compared to the Bayesian method structural time series [12] namely 5.25%.

Effectiveness of public policies related to traffic emissions in improving air quality in Brazil: A causal inference study using Bayesian structural time-series models

Air pollution is a major environmental health risk, particularly in low- and middle-income countries. In Brazil, the government has implemented progressive stages of traffic emission controls since 1986 through a program called “Proconve”. In this study, we evaluated the effectiveness of two of these stages, Proconve stages L-5 and P-7, in reducing air pollution in the 10 most populated cities in Brazil. We used Bayesian structural time-series models to estimate the causal effect of the vehicle emission control measures on the air pollution time series for each city and air pollutant (PM2.5, O3, NO2, and CO), accounting for potential confounding factors such as temperature and humidity. While previous studies have shown positive effects of control measures on air quality, our study suggests that the effects may not always be statistically significant, which has implications for the design and interpretation of future studies. Our study provides valuable information for policymakers in Brazil and adds to the international literature on the effectiveness of air pollution control measures. Despite the lack of statistically significant results, our study highlights the importance of considering statistical power and unmeasured confounding factors when evaluating the effectiveness of air pollution control measures.