No sales after midnight: evaluating the impact of a business curfew on drug-related crime in San Francisco’s tenderloin

One of the models that can be used to predict time series data is the Bayesian Structural Time Series (BSTS) model. The BSTS model is a more modern model and can handle data movement better. In the BSTS model, the Markov Chain Monte Carlo (MCMC) sampling algorithm is used to simulate the posterior distribution, which smoothes the forecasting results over a large number of potential models using Bayesian averaging models. The purpose of this study was to obtain the best BSTS model for Composite Stock Price Index (CSPI) data in Indonesia based on the state component and the number of MCMC iterations, and obtain forecasting results for CSPI value in Indonesia for the next 24 months, namely the period July 2023 to June 2024. The results obtained are based on a comparison of the R-square values in the model, the BSTS model with local linear trend and seasonal state components, and the number of MCMC iterations n = 5 00 is the best BSTS model that can be used for forecasting the CSPI value in Indonesia with an R-square value of 99.96%. The results of forecasting the CSPI value in Indonesia for the period July 2023 to June 2024 range from 6589 to 6760, with the lowest forecasting value in October 2023 and the highest in March 2023.

Assessing the future progression of COVID-19 in Iran and its neighbors using Bayesian models.

Air pollution poses a significant threat to human health and the environment, especially in developing nations facing rapid industrialization, urbanization, and increased vehicle emissions. As cities and factories continue to grow, the air quality problem worsens, making it crucial to enhance the monitoring, testing, and forecasting of air quality. In this context, this study focuses on building air quality models using Bayesian Structural Time Series (BSTS) models to predict air quality levels in Malaysia. The BSTS model integrates three main techniques: The structural model, which employs the Kalman filter approach to model trend and seasonality components; spike and slab regression for variable selection; and Bayesian model averaging to estimate the best-performing prediction model while accounting for uncertainty. The study utilized air quality time-series data spanning two years, from June 2017 to July 2019, obtained from the Malaysian Department of Environment (DOE). The primary objective of this study was to forecast air quality and assess the effectiveness of the Bayesian structural time series analysis on air quality time-series data. The results indicated that the BSTS technique is capable of modeling air quality time-series data with high accuracy, effectively capturing seasonal and trend components. The seasonal component showed a repetition of weekly concentration patterns, while the local linear trend component showed a steady decline in PM10 and PM2.5 concentration levels in most stations. Regression analysis demonstrated that humidity and ambient temperature significantly affected air quality in most locations in Malaysia.

Nigeria is recognized as being susceptible to climate change, and global warming if not taken care of, will lead to serious problems on livelihoods in Nigeria, especially in the area of agricultural activities. Rainfall is a major determinant of climate change the world over and climate change is one of the foremost global challenge facing humans at the moment. Using monthly time series rainfall data, Bayesian structural time series (BSTS) methodology was applied to fit models through MCMC algorithm. Also, Seasonal Autoregressive Moving Average (SARIMA) models were fitted to the same dataset using Box-Jenkins approach. The two models are considered based on their respective capacities to capture trend, seasonal and structural components of rainfall data. On the basis of model evaluation criteria (RMSE, MAE, MAPE and MASE), the SARIMA model had values that were clearly significantly smaller than that of the BSTS time series model. This implies that the SARIMA model is more robust in its estimations and forecasting abilities. Similarly, the R squared was larger for the SARIMA model than the BSTS (MCMC) model indicating that the SARIMA model was a better fit for the rainfall data. This study shows that SARIMA model is a more precise and robust in dealing with this type of dataset than BSTS (MCMC) model. It is better because its computational process using differencing, lags and moving averages ensure that the underlying components of the model are properly identified and estimated.

Enteroviruses pose a substantial public health challenge in Taiwan, often leading to increased healthcare visits. This study utilizes Taiwan CDC databases to analyse weekly enterovirus case data from emergency departments (EDs), as well as outpatient and inpatient settings. The objectives are to understand infection patterns through model fitting, forecast future visits for proactive epidemic management, and improve forecast accuracy by incorporating holiday effects. This approach enhances the reliability of predictions, supporting timely and effective surveillance and early detection of significant case surges. This study divides the time series data into an in-sample period (2016-2023) and an out-of-sample period covering weeks 1 to 27 in 2024. Using an expanding window approach, the analysis applies Bayesian structural time series (BSTS) models, exponential smoothing, and random forest to forecast one-week-ahead cases over the 27 weeks in 2024. The study evaluates forecast accuracy using five key metrics and identifies significant surges in cases by detecting values that exceed the 95% prediction intervals, enhancing anomaly detection. The results demonstrate that BSTS models, which incorporate trends, seasonal variations, summer, and Lunar New Year holiday effects, achieve superior forecasting accuracy. Specifically, by accounting for the Lunar New Year holiday within the out-of-sample period, the models attain mean absolute percentage error (MAPE) values of 6.509% for non-ED visits and 12.645% for ED visits. The BSTS model effectively addresses nonlinearity and non-stationarity and adapts well to structural changes. This study highlights the importance of holiday adjustments, particularly for the Lunar New Year, in improving forecast accuracy during periods of unusual healthcare demand. These adjustments enhance the BSTS model performance for predicting irregular healthcare service demand.

As competition becomes fierce and competitive environments change more dynamically than ever, understanding the implications of any strategic decision has become important for firms to maximize the effect of their resource allocation. Thus, evaluating the causal inference of strategy implementation is critical for strategists, yet few studies address practitioners’ needs in this manner. While a few studies have prioritized the need for causal inference by employing the differences-in-differences (DD) method in management research, the inherit limitations of this method pose problems for the suitability and reliability of its results. Thus, this study aims to introduce a new approach for causal inference in management research, the Bayesian structural time series (BSTS) model. The BSTS analysis enables us to investigate causal inference from existing whether or not to when and how long. Using customer data from a Korean e-commerce platform, we show why BSTS can be superior to DD thereby providing a basis for subsequent use of causal inference studies delivering practical management implications.

Forecasting the patterns of COVID-19 and causal impacts of lockdown in top five affected countries using Bayesian Structural Time Series Models.

Assessing the impact of non-pharmaceutical interventions against COVID-19 on 64 notifiable infectious diseases in Australia: A Bayesian Structural Time Series model.

Quantifying the impact of COVID-19 on non-motorized transportation: A Bayesian structural time series model

This study aims to forecast air passenger and cargo demand of the Indian aviation industry using the autoregressive integrated moving average (ARIMA) and Bayesian structural time series (BSTS) models. We utilized 10 years’ (2009–2018) air passenger and cargo data obtained from the Directorate General of Civil Aviation (DGCA-India) website. The study assessed both ARIMA and BSTS models’ ability to incorporate uncertainty under dynamic settings. Findings inferred that, along with ARIMA, BSTS is also suitable for short-term forecasting of all four (international passenger, domestic passenger, international air cargo, and domestic air cargo) commercial aviation sectors. Recommendations and directions for further research in medium-term and long-term forecasting of the Indian airline industry were also summarized.

BackgroundThe implementation of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic may inadvertently influence the epidemiology of tuberculosis (TB). (TB). However, few studies have explored how NPIs impact the long-term epidemiological trends of TB. We aimed to estimate the impact of NPIs implemented against COVID-19 on the medium- and long-term TB epidemics and to forecast the epidemiological trend of TB in Henan.MethodsWe first collected monthly TB case data from January 2013 to September 2022, after which we used the data from January 2013 to December 2021 as a training data set to fit the Bayesian structural time series (BSTS) model and the remaining data as a testing data set to validate the model's predictive accuracy. We then conducted an intervention analysis using the BSTS model to evaluate the impact of the COVID-19 pandemic on TB epidemics and to project trends for the upcoming years.ResultsA total of 590 455 TB cases were notified from January 2013 to September 2022, resulting in an annual incidence rate of 57.4 cases per 100 000 population, with a monthly average of 5047 cases (5.35 cases per 100 000 population). The trend in TB incidence showed a significant decrease during the study period, with an annual average percentage change of −7.3% (95% confidence interval (CI) = −8.4, −6.1). The BSTS model indicated an average monthly reduction of 25% (95% CI = 17, 32) in TB case notifications from January 2020 to December 2021 due to COVID-19 (probability of causal effect = 99.80%, P = 0.002). The mean absolute percentage error in the forecast set was 14.86%, indicating relatively high predictive accuracy of the model. Furthermore, TB cases were projected to total 43 584 (95% CI = 29 471, 57 291) from October 2022 to December 2023, indicating a continued downward trend.ConclusionsCOVID-19 has had medium- and long-term impacts on TB epidemics, while the overall trend of TB incidence in Henan is generally declining. The BSTS model can be an effective option for accurately predicting the epidemic patterns of TB, and its results can provide valuable technical support for the development of prevention and control strategies.

BackgroundCOVID-19 is currently on full flow in Pakistan. Given the health facilities in the country, there are serious threats in the upcoming months which could be very testing for all the stakeholders. Therefore, there is a need to analyze and forecast the trends of COVID-19 in Pakistan.MethodsWe have analyzed and forecasted the patterns of this pandemic in the country, for next 30 days, using Bayesian structural time series models. The causal impacts of lifting lockdown have also been investigated using intervention analysis under Bayesian structural time series models. The forecasting accuracy of the proposed models has been compared with frequently used autoregressive integrated moving average models. The validity of the proposed model has been investigated using similar datasets from neighboring countries including Iran and India.ResultsWe observed the improved forecasting accuracy of Bayesian structural time series models as compared to frequently used autoregressive integrated moving average models. As far as the forecasts are concerned, on August 10, 2020, the country is expected to have 333,308 positive cases with 95% prediction interval [275,034–391,077]. Similarly, the number of deaths in the country is expected to reach 7,187 [5,978–8,390] and recoveries may grow to 279,602 [208,420–295,740]. The lifting of lockdown has caused an absolute increase of 98,768 confirmed cases with 95% interval [85,544–111,018], during the post-lockdown period. The positive aspect of the forecasts is that the number of active cases is expected to decrease to 63,706 [18,614–95,337], on August 10, 2020. This is the time for the concerned authorities to further restrict the active cases so that the recession of the outbreak continues in the next month.

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.

Effects of 2019’s social protests on emergency health services utilization and case severity in Santiago, Chile: a time-series analysis

Norovirus is a leading cause of acute gastroenteritis, adding to strain on healthcare systems. Diagnostic test reporting of norovirus is often delayed, resulting in incomplete data for real-time surveillance. To nowcast the real-time case burden of norovirus a generalised additive model (GAM), semi-mechanistic Bayesian joint process and delay model "epinowcast", and Bayesian structural time series (BSTS) model including syndromic surveillance data were developed. These models were evaluated over weekly nowcasts using a probabilistic scoring framework. Using the weighted interval score (WIS) we show a heuristic approach is outperformed by models harnessing time delay corrections, with daily mean WIS = 7.73, 3.03, 2.29 for the baseline, "epinowcast", and GAM, respectively. Forecasting approaches were reliable in the event of temporally changing reporting values, with WIS = 4.57 for the BSTS model. However, the syndromic surveillance (111 online pathways) did not improve the BSTS model, WIS = 10.28, potentially indicating poor correspondence between surveillance indicators. Analysis of surveillance data enhanced by nowcasting delayed reporting improves understanding over simple model assumptions, important for real-time decision making. The modelling approach needs to be informed by the patterns of the reporting delay and can have large impacts on operational performance and insights produced.