Evaluating the impact of private land conservation with synthetic control design.

Programs to protect biodiversity on private land are increasingly being used worldwide. To understand the efficacy of such programs, it is important to determine their impact: the difference between the program's outcome and what would have happened without the program. Typically, these programs are evaluated by estimating the average program-level impact, which readily allows comparisons between programs or regions, but masks important heterogeneity in impact across the individual conservation interventions. We used synthetic control design, statistical matching, and time-series data to estimate the impact of individual protected areas over time and combined individual-level impacts to estimate program-level impact with a meta-analytic approach. We applied the method to private protected areas governed by conservation covenants (legally binding on-title agreements to protect biodiversity) in the Goldfields region of Victoria, Australia using woody vegetation cover as our outcome variable. We compared our results with traditional approaches to estimating program-level impact based on a subset of covenants that were the same age. Our results showed an overall program-level impact of a 0.3-0.8% increase in woody vegetation cover per year. However, there was significant heterogeneity in the temporal pattern of impact for individual covenants, ranging from -4 to +7% change in woody vegetation cover per year. Results of our approach were consistent with results based on traditional approaches to estimating program-level impact. Our study provides a transparent and robust workflow to estimate individual and program-level impacts of private protected areas.

Evaluación del impacto del suelo privado de conservación con diseño de control sintético Resumen Los programas de protección de la biodiversidad en suelo privado se utilizan cada vez más en todo el mundo. Para entender la eficacia de estos programas, es importante determinar la diferencia entre el resultado del programa y lo que habría ocurrido sin él. Normalmente, estos programas se evalúan estimando el impacto medio a nivel de programa, lo que permite fácilmente las comparaciones entre programas o regiones, pero oculta una importante heterogeneidad en el impacto entre las intervenciones individuales de conservación. Utilizamos un diseño de control sintético, un emparejamiento estadístico y datos de series temporales para estimar el impacto de las áreas protegidas individuales a lo largo del tiempo y combinamos los impactos a nivel individual para estimar el impacto a nivel de programa con un enfoque meta-analítico. Aplicamos el método a áreas protegidas privadas regidas por acuerdos de conservación (acuerdos con vínculos jurídicos sobre la titularidad para proteger la biodiversidad) destinados a mejorar la cubierta vegetal leñosa en la región de Goldfields de Victoria, Australia. Comparamos nuestros resultados con los métodos tradicionales de estimación del impacto a nivel de programa basados en un subconjunto de pactos de la misma antigüedad. Nuestros resultados mostraron un impacto global a nivel de programa de un aumento del 0.3-0.8% de la cubierta vegetal leñosa al año. Sin embargo, hubo una heterogeneidad significativa en el patrón temporal del impacto para los pactos individuales, que osciló entre −4 y +7% de cambio en la cubierta vegetal leñosa por año. Los resultados de nuestra estrategia fueron consecuentes con los resultados basados en las estrategias tradicionales usadas para estimar el impacto a nivel de programa. Nuestro estudio proporciona un flujo de trabajo transparente y sólido para estimar el impacto individual a nivel de programa de las áreas protegidas privadas.

College reopening and community spread of COVID-19 in the United States.

OBJECTIVE: After months of lockdown due to the COVID-19 outbreak, the US postsecondary institutions implemented different instruction approaches to bring their students back for the Fall 2020 semester. Given public health concerns with reopening campuses, the study evaluated the impact of Fall 2020 college reopenings on COVID-19 transmission within the 632 US university counties. STUDY DESIGN: This was a retrospective and observational study. METHODS: Bayesian Structural Time Series (BSTS) models were conducted to investigate the county-level COVID-19 case increases during the first 21 days of Fall 2020. The case increase for each county was estimated by comparing the observed time series (actual daily cases after school reopening) to the BSTS counterfactual time series (predictive daily cases if not reopening during the same time frame). We then used multilevel models to examine the associations between opening approaches (in-person, online, and hybrid) and county-level COVID-19 case increases within 21 and 42 days after classes began. The multigroup comparison between mask and non-mask-required states for these associations were also performed, given that the statewide guidelines might moderate the effects of college opening approaches. RESULTS: More than 80% of our university county sample did not experience a significant case increase in Fall 2020. There were no significant relationships between opening approaches and community transmission in both mask-required and non-mask-required states. Only small metropolitan counties and counties with a non-community college or a higher percentage of student population showed significantly positive associations with the case number increase within the first 21-day period of Fall 2020. For the longer 42-day period, the counties with a higher percentage of the student population showed a significant case increase. CONCLUSION: The overall findings underscored the outcomes of US higher education reopening efforts when the vaccines were still under development in Fall 2020. For individual county results, we invite the college- and county-level decision-makers to interpret their results using our web application.

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.

Trends in the detection of viruses causing gastroenteritis over a 10-year period and impact of nonpharmaceutical interventions.

BACKGROUND: Viral gastroenteritis continues to be a leading cause of death in low-income countries. The impact of nonpharmaceutical interventions (NPIs) on the transmission of gastroenteritis-causing viruses during the COVID-19 pandemic is understudied. OBJECTIVES: To investigate the 10-year trends of enteric viruses and estimate the impact of implementing and mitigating NPIs. STUDY DESIGN: Data regarding norovirus, rotavirus, adenovirus, astrovirus, and sapovirus detection were collected from five Korean hospitals between January 2013 and April 2023. We compared positivity between the pre-pandemic, pandemic, and post-pandemic periods. The causal effects of implementing and mitigating NPIs were quantified using the Bayesian Structural Time Series (BSTS) model. RESULTS: Norovirus was most frequently detected (9.9 %), followed by rotavirus (6.7 %), adenovirus (3.3 %), astrovirus (1.4 %), and sapovirus (0.6 %). During the pandemic, the positivity of all five viruses decreased, ranging from -1.0 % to -8.1 %, with rotavirus showing the greatest decrease. In the post-pandemic period, positivity rebounded for all viruses except for rotavirus. The BSTS model revealed that NPI implementation negatively affected the detection of all five viruses, resulting in reductions ranging from -73.0 % to -91.0 % compared to the prediction, with rotavirus being the least affected. Conversely, NPI mitigation positively affected the detection of all viruses, ranging from 79.0 % to 200.0 %, except for rotavirus. CONCLUSIONS: Trends observed over 10 years show that NPIs have had a major impact on changes in enteric virus detection. The effect of vaccines, in addition to NPIs, on rotavirus detection requires further investigation. Our findings emphasize the importance of NPIs in infection control and prevention.

Bayesian structural time series, an alternative to interrupted time series in the right circumstances.

OBJECTIVES: Compare two approaches to analyzing time series data-interrupted time series with segmented regression (ITS-SR) and Bayesian structural time series using the CausalImpact R package (BSTS-CI)-highlighting advantages, disadvantages, and implementation considerations. STUDY DESIGN AND SETTING: We analyzed electronic health records using each approach to estimate the antibiotic prescribing reduction associated with an educational program delivered to Australian primary care physicians between 2012 and 2017. Two outcomes were considered: antibiotics for upper respiratory tract infections (URTIs) and antibiotics of specified formulations. RESULTS: For URTI indication prescribing, average monthly prescriptions changes were estimated at -4,550; (95% confidence interval, -5,486 to -3,614) and -4,270; (95% credible interval, -5,934 to -2,626) for ITS-SR and BSTS-CI, respectively. Similarly for specified formulation prescribing, monthly average changes were estimated at -7,923; (95% confidence interval, -15,887 to 40) for ITS-SR and -20,269; (95% credible interval, -25,011 to -15,635) for BSTS-CI. CONCLUSION: Differing results between ITS-SR and BSTS-CI appear driven by divergent explanatory and outcome series trends. The BSTS-CI may be a suitable alternative to ITS-SR only if the explanatory series represent the secular trend of the outcome series before the intervention and are equally affected by exogenous or confounding factors. When appropriately applied, BSTS-CI provides an alternative to ITS with more readily interpretable Bayesian effect estimates.

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

Introduction: 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. Methods: 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. Results and discussion: 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.

The Impact of Gastronomic Tourism on Thailand Economy: Under the Situation of COVID-19 Pandemic.

With the COVID-19 pandemic's complexity and inexorable devastation, this research article attempts to forecast Thailand's economic move forward through gastronomic tourism promotion. The dynamic input-output (I-O) model was the primary method for classifying gastronomic activities in tourism I-O data, which was investigated sector by sector. The Ministry of Tourism and Sports in Bangkok, Thailand, officially gathered the 2017 I-O table. To briefly explain the empirical results, it found that the main sectors of gastronomic tourism that highly impact Thailand's economy are the processing and preserving of foods, other foods, food and beverage serving activities, and other food services. In terms of forecasting during the period of the COVID-19 pandemic, the Bayesian Structural Time Series (BSTS) based on the dynamic input-output (I-O) model suggests that approximately 1% to 2% of Thailand's gastronomic tourism will be able to contribute to the GDP of this country substantially. By the way, if this research result is significant, then both the private sector and the government sector need to be concerned and promote those sectors as much as they can.

Inferring causal impacts of extreme water-level drawdowns on lake water clarity using long-term monitoring data.

Although long-term ecosystem monitoring provides essential knowledge for practicing ecosystem management, analyses of the causal effects of ecological impacts from large-scale observational data are still in an early stage of development. We used causal impact analysis (CIA)-a synthetic control method that enables estimation of causal impacts from unrepeated, long-term observational data-to evaluate the causal impacts of extreme water-level drawdowns during summer on subsequent water quality. We used more than 100 years of transparency and water level monitoring data from Lake Biwa, Japan. The results of the CIA showed that the most extreme drawdown in recorded history, which occurred in 1994, had a significant positive effect on transparency (a maximum increase of 1.75 m on average over the following year) in the north basin of the lake. The extreme drawdown in 1939 was also shown to be a trigger for an increase in transparency in the north basin, whereas that in 1984 had no significant effects on transparency. In the south basin, contrary to the pattern in the north basin, the extreme drawdown had a significant negative effect on transparency shortly after the extreme drawdown. These different impacts of the extreme drawdowns were considered to be affected by the timing and magnitude of the extreme drawdowns and the depths of the basins. Our approach of inferring the causal impacts of past events on ecosystems will be helpful in implementing water-level management for ecosystem management and improving water quality in lakes.

The impact of enterovirus A71 vaccination program on hand, foot, and mouth disease in Guangdong, China: A longitudinal surveillance study.

Enterovirus A71 (EV71) vaccination program was introduced in 2016 in China. Based on a longitudinal surveillance dataset from 2012 to 2019 in Guangdong, China, we estimated the impact of the EV71 vaccination program on hand, foot, and mouth disease (HFMD) incidence, by using a counterfactual prediction made from synthetic control approach integrated with a Bayesian time-series model. We observed a relative reduction of 41.4% for EV71-associated HFMD cases during the post-vaccination period of 2017-2019, corresponding to 26,226 cases averted. The reduction of EV71-associated HFMD cases raised with the elevation of EV71 vaccine coverage by year. We found an indirect effect for the children aged 6-14 years who were less likely to be vaccinated. Whereas, the EV71 vaccine may not protect against non-EV71-associated HFMD. This study provides a template for ongoing public health surveillance of EV71 vaccine effectiveness with a counterfactual study design. Our results show strong evidence of the EV71 vaccination program working on reducing EV71-associated HFMD in real-world settings. The finding will benefit policy-making of EV71 vaccination and the prevention of HFMD.

Spatiotemporal impact of non-pharmaceutical interventions against COVID-19 on the incidence of infectious diarrhea in Xi'an, China.

Background: Non-pharmaceutical interventions (NPIs) against COVID-19 may prevent the spread of other infectious diseases. Our purpose was to assess the effects of NPIs against COVID-19 on infectious diarrhea in Xi'an, China. Methods: Based on the surveillance data of infectious diarrhea, and the different periods of emergence responses for COVID-19 in Xi'an from 2011 to 2021, we applied Bayesian structural time series model and interrupted time series model to evaluate the effects of NPIs against COVID-19 on the epidemiological characteristics and the causative pathogens of infectious diarrhea. Findings: A total of 102,051 cases of infectious diarrhea were reported in Xi'an from 2011 to 2021. The Bayesian structural time series model results demonstrated that the cases of infectious diarrhea during the emergency response period was 40.38% lower than predicted, corresponding to 3,211 fewer cases, during the COVID-19 epidemic period of 2020-2021. The reduction exhibited significant variations in the demography, temporal and geographical distribution. The decline in incidence was especially evident in children under 5-years-old, with decreases of 34.09% in 2020 and 33.99% in 2021, relative to the 2017-2019 average. Meanwhile, the incidence decreased more significantly in industrial areas. Interpretation: NPIs against COVID-19 were associated with short- and long-term reductions in the incidence of infectious diarrhea, and this effect exhibited significant variations in epidemiological characteristics.

The effect of the COVID-19 pandemic on mental health calls for police service.

Drawing upon seven years of police calls for service data (2014-2020), this study examined the effect of the COVID-19 pandemic on calls involving persons with perceived mental illness (PwPMI) using a Bayesian Structural Time Series. The findings revealed that PwPMI calls did not increase immediately after the beginning of the pandemic in March 2020. Instead, a sustained increase in PwPMI calls was identified in August 2020 that later became statistically significant in October 2020. Ultimately, the analysis revealed a 22% increase in PwPMI calls during the COVID-19 pandemic than would have been expected had the pandemic not taken place. The delayed effect of the pandemic on such calls points to a need for policymakers to prioritize widely accessible mental health care that can be deployed early during public health emergencies thus potentially mitigating or eliminating the need for increased police intervention, as was the case here. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40163-021-00157-6.

The Economic Impact of the SARS-COV-2 (COVID-19) Pandemic in Spain.

BACKGROUND: The COVID-19 pandemic has hit both the Spanish economy and the population's health hard. The result is an unprecedented economic and social crisis due to uncertainty about the remedy and the socioeconomic effects on people's lives. METHODS: We performed a retrospective analysis of the macroeconomic impact of the COVID-19 pandemic in 2020 using key indicators of the Spanish economy for the 17 Autonomous Communities (ACs) of the country. National statistics were examined in the search for impacts or anomalies occurring since the beginning of the pandemic. To estimate the strength of the impact on each of the indicators analyzed, we used Bayesian structural time series. We also calculated the correlation between the rate of GDP decline during 2020 and the cumulative incidence of COVID-19 cases per 100,000 inhabitants in the ACs. RESULTS: In 2020, the cumulative impact on the gross domestic product was of -11.41% (95% credible interval: -13.46; -9.29). The indicator for business turnover changed by -9.37% (-12.71; -6.07). The Spanish employment market was strongly affected; our estimates showed a cumulative increase of 11.9% (4.27; 19.45) in the rate of unemployment during 2020. The worst indicators were recorded in the ACs most economically dependent on the services sector. There was no statistical association between the incidence of COVID-19 in 2020 and the fall in GDP in the ACs. CONCLUSIONS: Our estimates portray a dramatic situation in Spain, where the COVID-19 crisis has had more serious economic and health consequences than in other European countries. The productive system in Spain is too dependent on sectors vulnerable to the pandemic, and it is necessary to design and implement profound changes through the European Next Generation program.

Quantifying what could have been - The impact of the Australian and New Zealand governments' response to COVID-19.

BACKGROUND: The Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March. It remains difficult to quantify the impact this had in reducing the spread of the virus. METHODS: Bayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed. Our models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively. CONCLUSION: This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.

Modelo bayesiano de series temporales estructurales. Aproximación a la evaluación de impacto de VA-MENGOC-BC®

Introducción: Una nueva intervención de salud pública, como la introducción de una vacuna, implica el monitoreo de indicadores que aseguren una intervención efectiva, y que exista la posibilidad de cuantificar sus beneficios. Obtener estimaciones precisas del impacto de una intervención de salud se considera un desafío importante. Objetivos: Estimar el impacto causal de uno de los productos líderes del Instituto Finlay de La Habana: la vacuna cubana registrada y comercializada VA-MENGOC-BC®. Métodos: Se seleccionaron datos en los anuarios estadísticos de salud desde 2009 hasta 2017. Se usaron como variable de interés, la tasa de incidencia de la enfermedad meningocócica en Cuba ( 100 000 habitantes y un conjunto de covariables que no se afectaron por la intervención: tasas de incidencia de fiebre tifoidea, de incidencia de tuberculosis, de mortalidad por enfermedades infecciosas (parasitarias e intestinales) todas ( 100 000 habitantes. Se consideró 1989 como año de la intervención. Se aplicó el método bayesiano de series temporales estructurales, que evaluó el impacto causal de la vacunación sostenida con VA-MENGOC-BC® desde 1989 hasta el presente. Resultados: Se estimó un impacto causal acumulativo significativo en la reducción de la incidencia de la enfermedad meningocócica. Se verificó que se produjo una disminución de la enfermedad en 97,2 %. Conclusiones: La aplicación del método de series de tiempo estructural bayesina para estimar el impacto de la vacuna VA-MENGOC-BC®, constituyó una herramienta novedosa para evaluar el contrafactual. Se proporcionó una apreciación del impacto de la vacunación con VA-MENGOC-BC®, una vacuna implementada y reconocida a nivel mundial.

Introduction: A new public health intervention, such as the introduction of a vaccine, implies monitoring the indicators that guarantee its effectiveness, and the possibility of quantifying its benefits. Obtaining accurate estimates of the impact of a health intervention is considered a major challenge. Objective: To estimate the causal impact of one of the leading products of the Finlay Institute in Havana: the registered and marketed Cuban vaccine VA-MENGOC-BC®. Methods: Data from the health statistics yearbooks from 2009 to 2017 were selected. The incidence rate of the meningococcal disease in Cuba per 100 000 population and a set of co-variables that were not affected by the intervention, such as incidence rate of typhoid fever, tuberculosis, and fatality cases due to infectious diseases (parasitic or intestinal) per 100 000 population were used as variables of interest. The intervention year was 1989. The Bayesian structural time series model was applied to evaluate the causal impact of the continued vaccination with VA-MENGOC-BC® from 1989 to date. Results: A significant cumulative causal impact in reducing the incidence of meningococcal disease was estimated. A decrease of 97.2% in the disease was verified. Conclusions: The application of the Bayesian structural time series model to estimate the impact of the vaccine VA-MENGOC-BC® was a novel tool to estimate the counterfactual. It was provided an estimate of the impact of the vaccination with VA-MENGOC-BC®, an implemented and globally well-known vaccine.