Bayesian Structural Time Series Model Research Articles

Assessing the effectiveness of quarantine measures during the COVID-19 pandemic in Chile using Bayesian structural time series models.

BackgroundWith the emergence of the COVID-19 pandemic, all existing health protocols were tested under the worst health crisis humanity has experienced since the Black Death in the 14th century. Countries in Latin America have been the epicenter of the COVID-19 pandemic, with more than 1.5 million people killed. Worldwide health measures have included quarantines, border closures, social distancing, and mask use, among others. In particular, Chile implemented total or partial quarantine measures depending on the number of infections in each region of the country. Therefore, it is necessary to study the effectiveness of these quarantines in relation to the public health measures implemented by government entities at the national level.ObjectiveThe main objective of this study is to analyze the effectiveness of national- and region-level quarantines in Chile during the pandemic based on information published by the Chilean Ministry of Health, and answers to the following question are sought: Were quarantine measures in Chile effective during the COVID-19 pandemic?MethodsThe causal effect between the rates of COVID-19 infections and the population rates in Phase 1 and Phase 2 quarantines in the period from March 2020 to March 2021 in different regions of Chile were evaluated using intervention analyses obtained through Bayesian structural time series models. In addition, the Kendall correlation coefficient obtained through the copula approach was used to evaluate the comovement between these rates.ResultsIn 75% of the Chilean regions under study (12 regions out of a total of 16), an effective Phase 1 quarantine, which was implemented to control and reduce the number of cases of COVID-19 infection, was observed. The main regions that experienced a decrease in cases were those located in the north and center of Chile. Regarding Phase 2, the COVID-19 pandemic was effectively managed in 31% (5 out of 16) of the regions. In the south-central and extreme southern regions of Chile, the effectiveness of these phases was null.ConclusionThe findings indicate that in the northern and central regions of Chile, the Phase 1 quarantine application period was an effective strategy to prevent an increase in COVID-19 infections. The same observation was made with respect to Phase 2, which was effective in five regions of northern Chile; in the rest of the regions, the effectiveness of these phases was weak or null.

Mortality, incidence and seasonality of canine and feline patients treated with tick antiserum in three far North Queensland veterinary clinics from 2000 to 2020.

Tick paralysis is a paralysis caused by bites from Ixodes holocyclus, affecting an estimated 10,000 companion animals in Australia annually. Despite tick antiserum being the cornerstone of treatment, there are no large-scale general practice studies that examine survival outcomes in tick antiserum-treated animals. In this retrospective study, clinical records from three far north Queensland general practice veterinary clinics were searched for tick antiserum-treated canine and feline patients were seen between 2000 and 2020. Patient records were assessed for survival outcomes,then logistic regression and Bayesian structural time-series model were used to assess trends in incidence and mortalityand the relationship between these and time of year, rainfall, and species. The study included 2019 dog and 953 cat records. When patients with unknown outcomes were removed, canine mortality was 11.8% (213/1799) and feline mortality was 5.3% (46/872). Dogs were found to have 2.41 odds of dying following treatment than cats. August and September had the highest mean number of monthly treatments, and rainfall in the previous 5-8months was positively correlated with the number of patients treated in each month. The odds of mortality did not vary significantly by month or season, and from 2015 onwards, there was a significant decrease in the proportion of dogs treated by the clinics. Overall, this study provides new information on tick antiserum treatment outcomes in general practice as well as new information on tick paralysis incidence in far north Queensland.

Impact of COVID-19 on the endoscopy department since the early phase of the pandemic in 2020: A questionnaire study among patients with canceled examinations at a single Japanese institution.

IntroductionIn early 2020, the Japanese government declared a nationwide state of emergency for the COVID‐19 pandemic. We investigated the impact of the emergency declaration on endoscopy adherence and conducted a follow‐up study of patients with canceled examinations at a tertiary endoscopy facility in Japan in 2020.MethodsWe compared the number of endoscopies performed, and cancelations at the endoscopy unit between 2019 and 2020 and used the Bayesian structural time series (BSTS) model to estimate the decrease in the number of endoscopies in 2020. We administered a questionnaire to those who had not undergone a scheduled endoscopy.ResultsOf 14 146 and 13 338 scheduled examinations, 1233 (8.7%) and 1403 (10.5%) were canceled in 2019 and 2020, respectively. During both years, age < 50 years, age > 80 years, upper endoscopy, and experience of endoscopy in the past 5 years were significantly associated with cancelations. In 2020, cancelations in the 14th–26th week of the year, including the period of state of emergency, increased significantly, and more women canceled. Of the 409 questionnaire‐respondents, 174 (42.5%) indicated that COVID‐19 had influenced their cancelation, and 315 (77.0%) had not undergone similar endoscopic examinations since then. The BSTS model predicted a decrease of 957 (95% CI −1213 to −708, P = .003) examinations.ConclusionIn 2020, despite low numbers of COVID‐19 cases in the study site, the number of endoscopies decreased, and cancelation increased. Further research is needed on the future impact of a decrease in the number of endoscopies during a COVID‐19 pandemic.

Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model

This paper investigates the benefits of internet search data in the form of Google Trends for nowcasting real U.S. GDP growth in real time through the lens of mixed frequency Bayesian Structural Time Series (BSTS) models. We augment and enhance both model and methodology to make these better amenable to nowcasting with large number of potential covariates. Specifically, we allow shrinking state variances towards zero to avoid overfitting, extend the SSVS (spike and slab variable selection) prior to the more flexible normal-inverse-gamma prior which stays agnostic about the underlying model size, as well as adapt the horseshoe prior to the BSTS. The application to nowcasting GDP growth as well as a simulation study demonstrate that the horseshoe prior BSTS improves markedly upon the SSVS and the original BSTS model with the largest gains in dense data-generating-processes. Our application also shows that a large dimensional set of search terms is able to improve nowcasts early in a specific quarter before other macroeconomic data become available. Search terms with high inclusion probability have good economic interpretation, reflecting leading signals of economic anxiety and wealth effects.

Causal impact analysis of enhanced phosphorus effluent standard on river water quality

The effect of environmental policies on water quality is an important factor in evaluating a project's impact and economic feasibility. The aim of this study was to evaluate the causal relationship between strengthening the total phosphorus (TP) concentration standard in wastewater treatment plant (WWTP) effluents and river water quality in the Geum River watershed (Rep. of Korea). Data, including precipitation, streamflow, and water quality, were collected for 2005–2020 and temporally divided into “before” and “after” the event of strengthening standards (in 2012). Further, the data were spatially divided into influence sites (impact) affected by the event and control sites (control) unaffected by the event. A median difference test for a before/after and control/impact (BACI) analysis and a causal impact analysis (CIA) based on a Bayesian structural time-series model were performed to evaluate the changes in water quality after the event. The BACI test showed that the TP concentration was reduced significantly (p-value<0.05) at all impact sites after the event, whereas the difference was not significant at the control sites. In contrast, other water quality variables, except TP, showed different statistical significance depending on the site. The CIA was performed by controlling the rainfall, flow rate, suspended solids, water temperature, biochemical oxygen demand, and chemical oxygen demand as covariates, which were selected based on a directed acyclic graph and bi-variable correlation analysis. The CIA results showed that the TP concentration was reduced significantly (p-value<0.05) at all impact sites, except for the control sites after the event, which is consistent with the BACI results. The causal impact of environmental management policies was previously difficult to evaluate by objectively targeting the natural systems because of the confounding bias. Our study demonstrated that strengthening the TP concentration standard from WWTPs majorly contributed to reducing TP in the receiving river, even when confounding factors, such as fluctuations in non-point source pollution loads caused by rainfall and runoff, were excluded. The statistical approaches used in this study can be valuable and practical methods for such evaluations if sufficient prior and posterior data and appropriate covariates are obtained.

Short-run impact of the Ukrainian refugee crisis on the housing market in Poland

This study analysed the impact of the Russian aggression against Ukraine in February 2022 on rental and housing prices in Warsaw and Krakow, two major cities in Poland. We hypothesize that the tremendous urban population growth (by 15% in Warsaw and 23% in Krakow) in a short period generated a housing demand shock, which, combined with constrained availability, resulted in extraordinary changes in prices and rents. Quantile hedonic indices indicated a significant increase in rents since the beginning of the Russian invasion in both markets, which were affected by the refugee shock. However, similar effects were not observed in the case of housing prices. In March and April of 2022, rents increased by around 16.5% in Krakow and 14% in Warsaw, while housing prices in both cities rose by much less (by about 4.0% in Krakow and 1% in Warsaw). Using Bayesian Structural Time Series models, we demonstrated that this abnormal rent increase is not random, and we concluded that the inflow of Ukrainian refugees most likely caused it.

Forecasting the Trends of Covid-19 and Causal Impact of Vaccines Using Bayesian Structural time Series and ARIMA.

Several researchers have used standard time series models to analyze future patterns of COVID-19 and the Causal impact of vaccinations in various countries. Bayesian structural time series (BSTS) and ARIMA (Autoregressive Integrated Moving Average) models are used to forecast time series. The goal of this study is to look at a much more adaptable effective methodology for dissecting the major components of a time series that breaks down the main parts of a time series. Within the period of March 1, 2020, to June 30, 2021, we used these state space model to explore the forecast patterns of COVID-19 in five afflicted nations.In addition, we used intervention analysis under BSTS models to examine the casual effect of vaccines in these countries, and we reached higher levels of accuracy than ARIMA models. According to forecasts, the number of confirmed cases in the United States, the United Kingdom, the United Arab Emirates, Bahrain, and India will climb by 1.17%, 19.4%, 15.5%, 13.8% , and 8%, respectively, during the next 60 days. On the other side, death rates in the United States, the United Kingdom, the United Arab Emirates, Bahrain, and India are expected to rise by 2.7%, 3.5%, 15.8%, 9.4%, and 14.8%, respectively. In addition, By using effective and quick vaccination, the United States, United Kingdom, and UAE have been able to reduce the number of mortality. On the other hand, vaccination is currently unable to decrease the rate of cases and deaths in India. Overall, the Indian healthcare system is likely to be seriously over-burdened in the next month. Though the USA and UK have managed to cut down the rates of COVID-19 deaths,but in UK and UAE number of confirmed cases are high as compared to other nations,so serious efforts will be required to keep these controllable. On the other hand,To keep things under control, Bahrain and four other countries has to speed up vaccinations.

Cervical Cancer Prevention in the Era of the COVID-19 Pandemic.

Background and Objectives: Cervical cancer (CC) is the fourth most common cause of cancer-related morbidity and mortality among women worldwide. CC prevention is based on screening and HPV vaccination. The COVID-19 pandemic has caused difficulties in implementing CC-preventative measures. The aim of this study was to collect data on the implementation of CC prophylaxis in Poland provided by public and private health care with a particular focus on the impact of the COVID-19 pandemic and attempt to estimate the level of CC-screening implementation by 2026 under public and private health care. Materials and Methods: Data on the implementation of privately funded (2016–2021) and publicly funded (2014–2021) CC-preventative measures in Poland were examined. The Prophet algorithm, which positions itself as an automatic forecasting procedure and represents a local Bayesian structural time-series model, was used to predict data. The correlation test statistic was based on Pearson’s product moment correlation coefficient and follows a t distribution. An asymptotic confidence interval was given based on Fisher’s Z transform. Results: In 2021, a significantly higher population screening coverage was observed in private health care (71.91%) than in the public system (12.6%). Our estimation assumes that the adverse downward trend of population coverage (pap smear CC screening) in the public system will continue to 5.02% and in the private health system to 67.92% in 2026. Correlation analysis showed that with the increase in the sum of HPV tests and LBC, the percentage of Pap smear coverage in the private healthcare sector decreases r = −0.62, p = 0.260 df = 3, CI = [−0.97, 0.57]. The amount of HPV vaccinations provided in private health care is steadily increasing. Immunization coverage of the population of girls aged 9–18 years under private health care at the end of the observation period was 4.3% (2021). Conclusions: It is necessary to reorganize the public CC-screening system in Poland based on a uniform reporting system for tests performed in both public and private health care using the model of action proposed by us. We recommend the introduction of a national free HPV vaccination program funded by the government and implemented in public and private health care facilities.

Weather integrated malaria prediction system using Bayesian structural time series model for northeast states of India.

Malaria is an endemic disease in India and targeted to eliminate by the year 2030. The present study is aimed at understanding the epidemiological patterns ofmalaria transmission dynamics in Assam and Arunachal Pradesh followed by the development of a malaria prediction model using monthly climate factors. A total of 144,055 cases in Assam during 2011-2018 and 42,970 cases in Arunachal Pradesh were reported during the 2011-2019 period observed, and Plasmodium falciparum (74.5%) was the most predominant parasite in Assam, whereas Plasmodium vivax (66%) in Arunachal Pradesh. Malaria transmission showed a strong seasonal variation where most of the cases were reported during the monsoon period (Assam, 51.9%, and Arunachal Pradesh, 53.6%). Similarly, the malaria incidence was highest in the male population in both states (Asam, 55.75%, and Arunachal Pradesh, 51.43%), and the disease risk is also higher among the > 15years age group (Assam, 61.7%, and Arunachal Pradesh, 67.9%). To predict the malaria incidence, Bayesian structural time series (BSTS) and Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMAX) models were implemented. A statistically significant association between malaria cases and climate variables was observed. The most influencing climate factors are found to be maximum and mean temperature with a 6-month lag, and it showed a negative association with malaria incidence. The BSTS model has shown superior performance on the optimal auto-correlated dataset (OAD) which contains auto-correlated malaria cases, cross-correlated climate variables besides malaria cases in both Assam (RMSE, 0.106; MAE, 0.089; and SMAPE, 19.2%) and Arunachal Pradesh (RMSE, 0.128; MAE, 0.122; and SMAPE, 22.6%) than the SARIMAX model. The findings suggest that the predictive performance of the BSTS model is outperformed, and it may be helpful for ongoing intervention strategies by governmental and nongovernmental agencies in the northeast region to combat the disease effectively.

Modelling Customs Revenue in Ghana Using Novel Time Series Methods

Governments across the world rely on their Customs Administration to provide functions that include border security, intellectual property rights protection, environmental protection, and revenue mobilisation amongst others. Analyzing the trends in revenue being collected from Customs is necessary to direct government policies and decisions. Models that can capture the trends being purported from the nominal (nonreal) tax values with respect to the trade volumes (value) over the period are indispensable. Predominant amongst the existing models are the econometric models (the GDP-based model, the monthly receipts model, and the microsimulation model), which are laborious and sometimes unreliable when studying trends in time series data. In this study, we modelled monthly revenue data obtained from the Ghana Revenue Authority-Customs Division (GRA-CD) for the period January 2010 to December 2019 using two traditional time series models, ARIMA model and ARIMA Error Regression Model (ARIMAX), and two machine learning time series models, Bayesian Structural Time Series (BSTS) model and a Neural Network Autoregression model. The Neural Network Autoregression model of the form NNAR (1, 3) provided the best forecasts with the least Mean Squared Error (MSE) of 53.87 and relatively lower Mean Absolute Percentage Error (MAPE) of 0.08. Generally, the machine learning models (NNAR (1, 3) and BSTS) outperformed the traditional time series models (ARIMA and ARIMAX models). The forecast values from the NNAR (1, 3) indicated a potential decline in revenue and this emphasizes the need for relevant authorities to institute measures to improve revenue generation in the immediate future.

Executive orders or public fear: What caused transit ridership to drop in Chicago during COVID-19?

The COVID-19 pandemic has induced significant transit ridership losses worldwide. This paper conducts a quantitative analysis to reveal contributing factors to such losses, using data from the Chicago Transit Authority's bus and rail systems before and after the COVID-19 outbreak. It builds a sequential statistical modeling framework that integrates a Bayesian structural time-series model, a dynamics model, and a series of linear regression models, to fit the ridership loss with pandemic evolution and regulatory events, and to quantify how the impacts of those factors depend on socio-demographic characteristics. Results reveal that, for both bus and rail, remote learning/working answers for the majority of ridership loss, and their impacts depend highly on socio-demographic characteristics. Findings from this study cast insights into future evolution of transit ridership as well as recovery campaigns in the post-pandemic era.

From Outbreak to Near Disappearance: How Did Non-pharmaceutical Interventions Against COVID-19 Affect the Transmission of Influenza Virus?

Influenza shares the same putative transmission pathway with coronavirus disease 2019 (COVID-19), and causes tremendous morbidity and mortality annually globally. Since the transmission of COVID-19 in China, a series of non-pharmaceutical interventions (NPIs) against to the disease have been implemented to contain its transmission. Based on the surveillance data of influenza, Search Engine Index, and meteorological factors from 2011 to 2021 in Xi'an, and the different level of emergence responses for COVID-19 from 2020 to 2021, Bayesian Structural Time Series model and interrupted time series analysis were applied to quantitatively assess the impact of NPIs in sequent phases with different intensities, and to estimate the reduction of influenza infections. From 2011 to 2021, a total of 197,528 confirmed cases of influenza were reported in Xi'an, and the incidence of influenza continuously increased from 2011 to 2019, especially, in 2019–2020, when the incidence was up to 975.90 per 100,000 persons; however, it showed a sharp reduction of 97.68% in 2020–2021, and of 87.22% in 2021, comparing with 2019–2020. The highest impact on reduction of influenza was observed in the phase of strict implementation of NPIs with an inclusion probability of 0.54. The weekly influenza incidence was reduced by 95.45%, and an approximate reduction of 210,100 (95% CI: 125,100–329,500) influenza infections was found during the post-COVID-19 period. The reduction exhibited significant variations in the geographical, population, and temporal distribution. Our findings demonstrated that NPIs against COVID-19 had a long-term impact on the reduction of influenza transmission.

The impact of anti-COVID-19 nonpharmaceutical interventions on hand, foot, and mouth disease-A spatiotemporal perspective in Xi'an, northwestern China.

Growing evidence has shown that anti‐COVID‐19 nonpharmaceutical interventions (NPIs) can support prevention and control of various infectious diseases, including intestinal diseases. However, most studies focused on the short‐term mitigating impact and neglected the dynamic impact over time. This study is aimed to investigate the dynamic impact of anti‐COVID‐19 NPIs on hand, foot, and mouth disease (HFMD) over time in Xi'an City, northwestern China. Based on the surveillance data of HFMD, meteorological and web search data, Bayesian Structural Time Series model and interrupted time series analysis were performed to quantitatively measure the impact of NPIs in sequent phases with different intensities and to predict the counterfactual number of HFMD cases. From 2013 to 2021, a total number of 172,898 HFMD cases were reported in Xi'an. In 2020, there appeared a significant decrease in HFMD incidence (−94.52%, 95% CI: −97.54% to −81.95%) in the first half of the year and the peak period shifted from June to October by a small margin of 6.74% compared to the previous years of 2013 to 2019. In 2021, the seasonality of HFMD incidence gradually returned to the bimodal temporal variation pattern with a significant average decline of 61.09%. In particular, the impact of NPIs on HFMD was more evident among young children (0–3 years), and the HFMD incidence reported in industrial areas had an unexpected increase of 51.71% in 2020 autumn and winter. Results suggested that both direct and indirect NPIs should be implemented as effective public health measures to reduce infectious disease and improve surveillance strategies, and HFMD incidence in Xi'an experienced a significant rebound to the previous seasonality after a prominent decline influenced by the anti‐COVID‐19 NPIs.

Effect of excise tax on sugar-sweetened beverages in Catalonia, Spain, three and a half years after its introduction

BackgroundThe World Health Organisation urges countries to levy specific excise taxes on SSBs. Currently, more than 50 countries have introduced some type of tax on SSBs. In March 2017, the Autonomous Region of Catalonia approved the introduction of a tiered excise tax on SSBs for public health reasons. To evaluate the effect of the Catalonian excise tax on the price and purchase of sugar-sweetened beverages (SSBs) and their possible substitutes, i.e., non-sugar-sweetened beverages (NSSBs) and bottled water, three and half years after its introduction, and 1 year after the outbreak of the COVID-19 pandemic.MethodsWe analysed purchase data on soft drinks, fruit drinks and water, sourced from the Ministry of Agriculture food-consumption panel, in a random sample of 12,500 households across Spain. We applied the synthetic control method to infer the causal impact of the intervention, based on a Bayesian structural time-series model which predicts the counterfactual response that would have occurred in Catalonia, had no intervention taken place.ResultsAs compared to the predicted (counterfactual) response, per capita purchases of SSBs fell by 0.17 l three and a half years after implementing the SSB tax in Catalonia, a 16.7% decline (95% CI: − 23.18, − 8.74). The mean SSB price rose by 0.11 €/L, an 11% increase (95% CI: 9.0, 14.1). Although there were no changes in mean NSSB prices, NSSB consumption rose by 0.19 l per capita, a 21.7% increase (95% CI: 18.25, 25.54). There were no variations in the price or consumption of bottled water. The effects were progressively greater over time, with SSB purchases decreasing by 10.4% at 1 year, 12.3% at 2 years, 15.3% at 3 years, and 16.7% at three and a half years of the tax’s introduction.ConclusionsThe Catalonian SSB excise tax had a sustained and progressive impact over time, with a fall in consumption of as much as 16.7% three and half years after its introduction. The observed NSSB substitution effect should be borne in mind when considering the application of this type of tax to the rest of Spain.

The impact of COVID-19 on air passenger demand and CO2 emissions in Brazil

This paper estimates the impact of the COVID-19 on air travel demand and emissions in Brazil, the largest aviation market in Latin America. Combining detailed flight data and data on combustion emission factors, we estimate the CO2 emissions of domestic flights. A Bayesian structural time-series model was used to estimate the impact of COVID-19 on daily trends of air travel demand and emissions. The COVID-19 caused a reduction of 68% in national passengers and 63% in total CO2 emissions compared to what would have occurred if the pandemic had not happened. Despite such a sharp drop, fuel efficiency decreased after the COVID-19 outbreak, and passenger demand recovered to 64.2% of pre-pandemic levels by the end of 2020. The fast recovery in domestic flights by December 2020 indicates that the emissions could soon return to pre-pandemic levels, demonstrating the challenges of reducing emissions in the aviation sector in the short term.

Estimating the effectiveness of permanent price reductions for competing products using multivariate Bayesian structural time series models

The Florence branch of an Italian supermarket chain recently implemented a strategy that permanently lowered the price of numerous store brands in several product categories. To quantify the impact of such a policy change, researchers often use synthetic control methods for estimating causal effects when a subset of units receive a single persistent treatment and the rest are unaffected by the change. In our applications, however, competitor brands not assigned to treatment are likely impacted by the intervention because of substitution effects; more broadly, this type of interference occurs whenever the treatment assignment of one unit affects the outcome of another. This paper extends the synthetic control methods to accommodate partial interference, allowing interference within predefined groups but not between them. Focusing on a class of causal estimands that capture the effect both on the treated and control units, we develop a multivariate Bayesian structural time series model for generating synthetic controls that would have occurred in the absence of an intervention, enabling us to estimate our novel effects. In a simulation study we explore our Bayesian procedures’ empirical properties and show that it achieves good frequentists coverage, even when the model is misspecified. We use our new methodology to make causal statements about the impact on sales of the affected store brands and their direct competitors. Our proposed approach is implemented in the CausalMBSTS R package.

Using absolutist word frequency from online searches to measure population mental health dynamics

The assessment of population mental health relies on survey data from representative samples, which come with considerable costs. Drawing on research which established that absolutist words (e.g. never) are semantic markers for depression, we propose a new measure of population mental health based on the frequency of absolutist words in online search data (absolute thinking index; ATI). Our aims were to first validate the ATI, and to use it to model public mental health dynamics in France and the UK during the current COVID-19 pandemic. To do so, we extracted time series for a validated dictionary of 19 absolutist words, from which the ATI was computed (weekly averages, 2019–2020, n = 208) using Google Trends. We then tested the relationship between ATI and longitudinal survey data of population mental health in the UK (n = 36,520) and France (n = 32,000). After assessing the relationship between ATI and survey measures of depression and anxiety in both populations, and dynamic similarities between ATI and survey measures (France), we tested the ATI’s construct validity by showing how it was affected by the pandemic and how it can be predicted by COVID-19-related indicators. A final step consisted in replicating ATI’s construct validity tests in Japan, thereby providing evidence for the ATI’s cross-cultural generalizability. ATI was linked with survey depression scores in the UK, r = 0.68, 95%CI[0.34,0.86], β = 0.23, 95%CI[0.09,0.37] in France and displayed similar trends. We finally assessed the pandemic’s impact on ATI using Bayesian structural time-series models. These revealed that the pandemic increased ATI by 3.2%, 95%CI[2.1,4.2] in France and 3.7%, 95%CI[2.9,4.4] in the UK. Mixed-effects models showed that ATI was related to COVID-19 new deaths in both countries β = 0.14, 95%CI[0.14,0.21]. These patterns were replicated in Japan, with a pandemic impact of 4.9%, 95%CI[3.1,6.7] and an influence of COVID-19 death of β = 0.90, 95%CI[0.36,1.44]. Our results demonstrate the validity of the ATI as a measure of population mental health (depression) in France, the UK and to some extent in Japan. We propose that researchers use it as cost-effective public mental health “thermometer” for applied and research purposes.

Crime registration and distress calls during COVID-19: two sides of the coin

ABSTRACT This paper inspects changes in crime trends brought about by the COVID-19 pandemic in Tamil Nadu, India. Using Bayesian structural time-series models, the authors study the changes in two metrics of criminal activity – crime registration and distress calls. In addition to absolute changes, the authors analyse the relative changes in one metric with respect to the other during two stay-at-home orders across six categories of crimes: property offences; offences against the human body; cases of missing persons and unidentified dead bodies; vehicle- and traffic-related offences; crimes against women, children and elders; and emerging crimes. While there was an overall decrease in most types of crime, results show that each category was impacted by restrictions in human mobility in different ways; emerging crimes, in particular, increased significantly during both periods. The crime and call trends exhibited a synchronous pattern with respect to a majority of offences except in the category of crimes against women and missing persons, where a contrary trend of decreasing crime registrations and increasing distress calls was noticed during the partial lockdown period. This finding highlights inherent inadequacies such as under-reporting of crimes by victims and non-registration of crime by the police. Understanding these inadequacies could potentially enhance preparedness leading to improved public service delivery by reordering priorities. Breaking down these trends at a crime-specific level assists law enforcement practitioners in gaining insights on crime motivators and enablers, thus contributing towards more effective crime prevention under ordinary circumstances.

Impacts of the 2017 Brazilian National Primary Care Policy on public primary health care in Rio de Janeiro, Brazil.

In 2017, in a scenario of financial restrictions caused by an economic crisis in Brazil, a new primary health care policy promoted changes in the way different primary health care models were prioritized and implemented, with possible negative effects on the access to primary health care. This study aims to investigate if the 2017 Brazilian National Primary Care Policy (PNAB) negatively affected the primary care organization based on the Family Health Strategy (FHS) model and on the access to public primary care services in the city of Rio de Janeiro. The annual averages and the pre- and post-2017 averages of 15 variables were analyzed to identify possible trend breaks in 2017. A Bayesian structural time series model was used to determine the differences between actual and predicted post-2017 averages of each variable. The data were obtained via the Brazilian Health Informatics Department (DATASUS), the Department of Informatics of the Brazilian Unified National Health System. The annual average of family health teams was 1,179.9 teams, in 2017, and 788.8 teams in 2020, while the annual average of equivalent family health teams was 163.6, in 2017, and 125.4, in 2020. The actual post-2017 average of 989.3 family health teams (p = 0.004) was 16.7% lower than the predicted post-2017 average of 1,187.4 teams. In total, 62.6% and 40.5% of the population in Rio de Janeiro were covered by the FHS in 2017, and 2020, respectively. The provision of public primary care services decreased after 2017. Results show a deterioration of the FHS in Rio de Janeiro after 2017 and no increase in the traditional primary care model. Access to public primary care services reduced in the same period.

The Causal Impact of the Rapid Czech Interest Rate Hike on the Czech Exchange Rate Assessed by the Bayesian Structural Time Series Model

I have employed the Bayesian Structural Time Series model to assess the recent interest rate hike by the Czech Central Bank and its causal impact on the Koruna exchange rate. By forecasting exchange rate time series in the absence of the intervention we can subtract the observed values from the prediction and estimate the causal effect. The results show that the impact was little and time limited in one model specification and none in the second version. It implies that the Czech Central Bank possesses the ability to diverge significantly from the Eurozone benchmark interest rate at least in the short term. It also shows that the interest rate hike will not be able to curb global inflation forces on the domestic price level.