Bayesian Time Series Research Articles

A thirty-year time series analyses identifies coherence between oscillations in Anthrax outbreaks and El Niño in Karnataka, India

Anthrax is an economically important zoonotic disease affecting both livestock and humans. The disease is caused by a spore forming bacterium, Bacillus anthracis, and is considered endemic to the state of Karnataka, India. It is critical to quantify the role of climatic factors in determining the temporal pattern of anthrax outbreaks, so that reliable forecasting models can be developed. These models will aid in establishing public health surveillance and guide strategic vaccination programs, which will reduce the economic loss to farmers, and prevent the spill-over of anthrax from livestock to humans. In this study, correlation and coherence between time series of anthrax outbreaks in livestock (1987–2016) and meteorological variables and Sea Surface Temperature anomalies (SST) were identified using a combination of cross-correlation analyses, spectral analyses (wavelets and empirical mode decomposition) and further quantified using a Bayesian time series regression model accounting for temporal autocorrelation. Monthly numbers of anthrax outbreaks were positively associated with a lagged effect of rainfall and wet day frequency. Long-term periodicity in anthrax outbreaks (approximately 6–8 years) was coherent with the periodicity in SST anomalies and outbreak numbers increased with decrease in SST anomalies. These findings will be useful in planning long-term anthrax prevention and control strategies in Karnataka state of India.

Streamflow in the United States: Characteristics, trends, regime shifts, and extremes

Long-term streamflow observations contain essential information for understanding hydrological changes and managing water resources. A continental-scale dataset or analysis of temporal streamflow change is still lacking across hydrologic gauges in the Conterminous United States (CONUS). Here, we compiled 70 years of streamflow records from 1951 to 2021 at ~ 8000 hydrologic stations across the CONUS and characterized temporal trends, regime shifts, and extreme events using a Bayesian time series analysis algorithm. We found that the occurrences of sudden streamflow changes (e.g., regime shifts and extreme events) have been increasing with time across the CONUS. In addition, we derived 181 streamflow indicators that are valuable for hydrological and biological applications, such as the duration and frequency of high or low streamflow events. The Mississippi River Basin, especially the middle and lower parts, was a hot spot of high-frequency high-flow events. Overall, we anticipate the dataset generated here offers valuable information for understanding and quantifying changes in water resources across the CONUS.

A Bayesian non-stationary heteroskedastic time series model for multivariate critical care data.

We propose a multivariate GARCH model for non-stationary health time series by modifying the observation-level variance of the standard state space model. The proposed model provides an intuitive and novel way of dealing with heteroskedastic data using the conditional nature of state-space models. We follow the Bayesian paradigm to perform the inference procedure. In particular, we use Markov chain Monte Carlo methods to obtain samples from the resultant posterior distribution. We use the forward filtering backward sampling algorithm to efficiently obtain samples from the posterior distribution of the latent state. The proposed model also handles missing data in a fully Bayesian fashion. We validate our model on synthetic data and analyze a data set obtained from an intensive care unit in a Montreal hospital and the MIMIC dataset. We further show that our proposed models offer better performance, in terms of WAIC than standard state space models. The proposed model provides a new way to model multivariate heteroskedastic non-stationary time series data. Model comparison can then be easily performed using the WAIC.

Multivariate Bayesian Time-Series Model with Multi-temporal Convolution Network for Forecasting Stock Market During COVID-19 Pandemic

The paper proposes a hybrid algorithm for forecasting multiple correlated time-series data, which consists of two main steps. First, it employs a multivariate Bayesian structural time series (MBSTS) approach as a base step. This method allows for the incorporation of potentially high-dimensional regression components, and it utilizes spike and slab priors to identify a parsimonious model. Second, the algorithm includes a post-model fitting diagnostic step where the residuals from the MBSTS step are processed through a multi-input/output temporal convolutional network (M-TCN) with multiple time scale feature learning. This step serves as an alternative to traditional subjective residual-based diagnostic procedures in time-series analysis, with the aim of improving forecasting accuracy. The key advantage of the M-TCN is its ability to capture sequential information efficiently. The M-TCN expands the field of convolution kernel without increasing the number of parameters, thus enhancing the capacity of model to capture complex sequential patterns. The paper presents two applications showcasing the effectiveness of the proposed hybrid algorithm. First, it utilizes pre-lockdown data from eleven Nifty stock sectoral indices to predict stock price movements, including the initial post-lockdown upturn. In the second application, it focuses on stock market data from pharmaceutical companies involved in manufacturing COVID-19 vaccines. In both cases, sentiment data sourced from newspapers and social media serve as the regression component. Through rigorous analysis, the paper demonstrates that the hybrid model outperforms various benchmark models, including LSTM, Bidirectional Encoder Representations from Transformers (BERT)-based LSTM, Deep Transformer Model, and GRU, among others, in terms of forecasting accuracy. This underscores the utility of the hybrid algorithm, particularly in predicting stock market trends during the COVID-19 pandemic period and its associated market dynamics.

A Bayesian Interrupted Time Series framework for evaluating policy change on mental well-being: An application to England’s welfare reform

Factors contributing to social inequalities are associated with negative mental health outcomes and disparities in mental well-being. We propose a Bayesian hierarchical controlled interrupted time series to evaluate the impact of policies on population well-being whilst accounting for spatial and temporal patterns. Using data from the UKs Household Longitudinal Study, we apply this framework to evaluate the impact of the UKs welfare reform implemented in the 2010s on the mental health of the participants, measured using the GHQ-12 index. Our findings indicate that the reform led to a 2.36% (95% CrI: 0.57%–4.37%) increase in the national GHQ-12 index in the exposed group, after adjustment for the control group. Moreover, the geographical areas that experienced the largest increase in the GHQ-12 index are from more disadvantage backgrounds than affluent backgrounds.

THE IMPACT OF A CLOSING PROTOCOL (CP) IMPLEMENTATION ON WOUND MORBIDITY IN ABDOMINAL WALL RECONSTRUCTION (AWR) WITH MESH

Abstract Background Wound complications (WC) are associated with increased cost, recurrence, and mesh infection. Operative CPs have not been evaluated in AWR. Our aim was to evaluate the effect of a CP on wound infection (WI) and WC. Method A prospective, institutional database was used to compare AWR with and without CP. Descriptive statistics and a Bayesian Structured time series analysis (BSTA) were performed to evaluate rates of WI and WC. Data is reported as means unless stated. The CP involves antibiotic wound irrigation, glove and instrument exchange, re-draping the field, surgeon-only handling the mesh prior to mesh implantation. The introduction of on-going quality improvement efforts were accounted for in the model. Results A total of 2541 patients were examined with an age of 57.9 ± 12.6 years, 56.7% female, BMI 32.9 ± 9.8 kg/m2, 24.2% diabetic, and 41.2% former-smokers. Defect size was 203.1 ± 205.8 cm2 and 83.1% were CDC Class 1 or 2 wounds. Follow-up was 31.5 ± 41.4 months. Overall WI and WC rate decreased after CP (14.5% vs. 2.6% and 29.3% vs. 10.3%, P < 0.001). Specifically, the rate of wound cellulitis (9.7% vs. 2.7%, P < 0.001), wound infection (13.8 vs. 1.8%, P < 0.001), and mesh infection (2.1% vs. 0.6%, P < 0.004) were reduced after CP. For WI, BSTA revealed a relative effect of 83% (±2%, 95%CI:-87%,-78%; P = 0.0002) reduction compared to counterfactual. For WC, the BSTA revealed a relative effect reduction compared to counterfactual of 67% (±3%, 95%CI:-60%, -72%; P = 0.0002). Conclusion Introduction of a CP prior to mesh implantation in open AWR led to a significant reduction in overall wound infections and wound complications. The use of a CP should be strongly considered.

Analysis of trends and abrupt changes in groundwater and meteorological droughts in the United Kingdom

In recent years, the escalating impact of climate change, coupled with growing concerns regarding groundwater drought, has underscored the critical importance of analyzing hydrological trends. This is a foundational step in advancing effective water resource planning and management. This study presents a regional investigation of groundwater and meteorological droughts in the UK, utilizing the Standardized Groundwater Index (SGI) and the Standardized Precipitation Index (SPI). The analysis encompasses drought duration and an evaluation of the correlation between the SGI and SPI. Subsequently, the seasonal Mann-Kendall (MK) test and the Bayesian Changepoint Detection and Time Series Decomposition (BEAST) algorithm were employed to assess overall trends for both SGI and SPI. Additionally, these methods were utilized to pinpoint trend and seasonal abrupt changes within the time series of both indices.The seasonal MK test revealed statistically significant trends in many SGI stations across the UK, spanning from Northern Scotland down to the South of England, encompassing both increasing and decreasing trends. Additionally, the MK test showed slightly positive trends at the low SPI time scale, though these were not statistically significant. In contrast, more consistent and significant increasing trends were observed throughout the entire UK, except for Northern Scotland, which exhibited negative trends. Despite indications of a progressively more humid UK, the BEAST analysis enabled the detection of decreasing abrupt changes in trends. These changes result in sudden transitions from wet to dry conditions, which can be challenging to identify using the MK test alone. However, BEAST revealed both positive and negative abrupt changes across the entire UK, as well as positive or negative variations in seasonality. This suggests a high degree of variability in seasonal patterns. In summary, this study provides a detailed assessment of groundwater and meteorological drought trends and abrupt changes, considering the impact of climate change on various regions of the UK.

A Combined Seasonal Mann–Kendall and Innovative Approach for the Trend Analysis of Streamflow Rate in Two Croatian Rivers

Climate change profoundly impacts hydrological systems, particularly in regions such as Croatia, which is renowned for its diverse geography and climatic variability. This study examined the effect of climate change on streamflow rates in two Croatian rivers: Bednja and Gornja Dobra. Using seasonal Mann–Kendall (MK) tests, overall streamflow trends were evaluated. Additionally, innovative polygon trend analysis (IPTA), innovative visualization for innovative trend analysis (IV-ITA), and Bayesian changepoint detection and time series decomposition (BEAST) algorithms were used to assess the trends’ magnitudes and transitions. The seasonal MK analysis identified significant decreasing trends, primarily during summer. The results of IPTA and IV-ITA revealed consistent decreasing trends throughout most months, with a notable increase in September, especially at high flow values. The rivers’ behavior differed between the first and second halves of the month. BEAST analysis detected abrupt changes, including earlier shifts (1951–1968) in the Bednja and more recent ones (2013–2015) in both the Bednja and, to a lesser extent, the Gornja Dobra rivers. This comprehensive approach enhances our understanding of long-term streamflow trends and short-term fluctuations induced by climate change.

Trends in botanical exploration in Nigeria forecast over 1000 yet undescribed vascular plant species.

Taxonomists are primary actors of biodiversity assessment. At the same time, there is awareness by the taxonomic community at large that the field is going through a crisis, sometimes referred to as the 'taxonomic impediment'. Coupled with the ongoing biodiversity crisis, or 6th mass extinction, this biodiversity impedance puts at risk the target set in the Convention on Biological Diversity's (CBD) Global Biodiversity Framework vision 2050, which calls for urgent action to '… put biodiversity on a path to recovery by 2030 for the benefit of planet and people'. This risk is particularly pronounced in tropical African countries where taxonomic studies are done on an ad hoc basis. In this study, our aim is to investigate the historical trends in botanical exploration of vascular plants in Nigeria and forecast the near-term (50-year) description of presently unknown species, which we use to discuss scenarios of taxonomic effort that may be necessary for a comprehensive biodiversity assessment in the country. The study is based on a dataset from the World Checklist of Vascular Plants, containing all vascular plant species reported to occur in Nigeria. We fit nested Bayesian time series regressions to estimate the long-term trend in the rate of description of vascular plant species in Nigeria. From these models, we use an ensemble forecast to estimate the number of species descriptions by the year 2070, and then evaluate the description rates per taxonomist required to meet this estimate under different totals of active taxonomists. We find a striking difference in species description between Nigerian botanists and their foreign counterparts, with the former contributing relatively small numbers. Additionally, only a fraction of the authors involved in describing Nigeria's vascular plants are of indigenous origin. Our study reveals that the number of new species described annually exhibits a long-term increasing trend, with an average of 19.5 species described per year. However, after taking into account year-to-year variability and the number of taxonomists active in a given year, the long-term trend in species descriptions credibly declines over time. While the number of authors involved in describing species has generally increased over time, it has remained stable since the 1950s. Predictions for the number of new species descriptions by 2070 vary by model, with an ensemble prediction estimating 1140 species descriptions, but ranging from 1004 to 2239 between individual models. The study estimates that current levels of taxonomic activity should lead to a 20 % increase in known species of vascular plants in Nigeria over the next 50 years, which is still probably an underestimate of the true, unknown species richness. Urgent action is needed to address the taxonomic impediment so that local taxonomic studies in tropical African countries can achieve the CBD's Global Biodiversity Framework vision 2050. Here, we outline some key pathways to achieving this goal.

Relationships of State Alcohol Policy Environments With Homicides and Suicides

IntroductionAlcohol use is involved in a large proportion of homicides and suicides each year in the United States, but there is limited evidence on how policies targeting alcohol influence violence in the U.S. context. Extant studies generally focus on individual policies in isolation of each other. This study examines of the effects of changes in states’ alcohol policy restrictions on overall homicide and suicide rates and firearm-related homicide and suicide rates using a holistic measure of states’ alcohol policy environments. MethodsUsing a composite measure of state-level alcohol policies (Alcohol Policy Scale) and data from the National Vital Statistics System from 2002 to 2018, this study applied a Bayesian time series model to estimate the effects of alcohol policy changes on overall and firearm-involved homicide and suicide rates. The analysis was performed in 2023 and 2024. ResultsA one standard deviation change in the Alcohol Policy Scale was associated with a 6 percent decline in homicide rates both overall (IRR=0.94; 95-percent credibility interval = [0.89, 1.00]) and for firearm homicides specifically (IRR=0.94, 95-percent CI=[0.88, 1.01]). There was no clear association of alcohol policy with suicides. The model predicts that a nationwide increase in alcohol restrictions equivalent to a shift from the 25th to 75th percentile of the scale's distribution would result in almost 1200 fewer homicides annually. ConclusionsIncreases in the restrictiveness of state-level alcohol policies are associated with reductions in homicides. More restrictive alcohol policy environments may offer an opportunity to reduce homicides.

Analysis of SPI index trend variations in the United Kingdom - A cluster-based and bayesian ensemble algorithms approach

Study regionUnited Kingdom (UK). Study focusA regional investigation of the Standard Precipitation Index (SPI) trends and abrupt changes in the UK has been carried out. The K-means algorithm was employed to partition the study area into six homogeneous regions, each distinguished by specific SPI characteristics. Subsequently, the seasonal Mann-Kendall (MK) test and the Bayesian Changepoint Detection and Time Series Decomposition (BEAST) algorithm were used to evaluate the overall trends for each cluster and SPI time scale, as well as to identify abrupt changes in trend and seasonality along the SPI time series, respectively. New hydrological insights for the regionThe seasonal MK test revealed statistically significant increasing SPI trends for all clusters, except for the southeastern area of the UK, where decreasing, but not statistically significant, SPI trends were observed. Moreover, despite a scenario suggesting an increasingly humid UK, the BEAST analysis allowed the detection of decreasing abrupt changes in trends, resulting in sudden changes from wet to dry conditions, that cannot be identified using the MK test. Alongside these, the BEAST analysis has also revealed positive abrupt changes in trends across all UK, as well as positive or negative variations in seasonality, which are followed by longer or shorter wet or dry periods, respectively. Overall, the study approach provides a detailed picture of the SPI trends and abrupt changes, in light of the impact of climate change on the different areas of the UK.

Multilayer factors associated with excess all-cause mortality during the omicron and non-omicron waves of the COVID-19 pandemic: time series analysis in 29 countries.

The COVID-19 pandemic has resulted in significant excess mortality globally. However, the differences in excess mortality between the Omicron and non-Omicron waves, as well as the contribution of local epidemiological characteristics, population immunity, and social factors to excess mortality, remain poorly understood. This study aims to solve the above problems. Weekly all-cause death data and covariates from 29 countries for the period 2015-2022 were collected and used. The Bayesian Structured Time Series Model predicted expected weekly deaths, stratified by gender and age groups for the period 2020-2022. The quantile-based g-computation approach accounted for the effects of factors on the excess all-cause mortality rate. Sensitivity analyses were conducted using alternative Omicron proportion thresholds. From the first week of 2021 to the 30th week of 2022, the estimated cumulative number of excess deaths due to COVID-19 globally was nearly 1.39million. The estimated weekly excess all-cause mortality rate in the 29 countries was approximately 2.17 per 100,000 (95% CI: 1.47 to 2.86). Weekly all-cause excess mortality rates were significantly higher in both male and female groups and all age groups during the non-Omicron wave, except for those younger than 15 years (P < 0.001). Sensitivity analysis confirmed the stability of the results. Positive associations with all-cause excess mortality were found for the constituent ratio of non-Omicron in all variants, new cases per million, positive rate, cardiovascular death rate, people fully vaccinated per hundred, extreme poverty, hospital patients per million humans, people vaccinated per hundred, and stringency index. Conversely, other factors demonstrated negative associations with all-cause excess mortality from the first week of 2021 to the 30th week of 2022. Our findings indicate that the COVID-19 Omicron wave was associated with lower excess mortality compared to the non-Omicron wave. This study's analysis of the factors influencing excess deaths suggests that effective strategies to mitigate all-cause mortality include improving economic conditions, promoting widespread vaccination, and enhancing overall population health. Implementing these measures could significantly reduce the burden of COVID-19, facilitate coexistence with the virus, and potentially contribute to its elimination.

Abstract TP93: A Bayesian Hierarchical Time Series Model to Forecast Patient Recovery Outcomes After an Acute Stroke When Activity Monitor Data is Incomplete

Background: Activity monitor data is a useful tool in clinical trials to monitor patient recovery after stroke. These data offer important information on activity, sedentary and sleep behaviors throughout recovery. However, not all participants wear activity monitors, or their activity data may be incomplete. The purpose of this study was to develop a model-based approach to obtain individual patient-level longitudinal forecast estimates of activity and sleep behaviors for both those with and without complete activity data. Methods: Participants wore an ActivPal (steps/day) and Actigraph (total sleep/day) monitors for 3-7 days at 10-60-90 days post stroke. We developed a Bayesian hierarchical time series model to forecast daily steps and sleep time by participant. We included a logistic growth curve in which we use predictors comprising age, sex, race, marital status, smoking, BMI, cognition, gait speed, depression, self-reported sleep, and patients’ previous time trends to forecast patient specific future time trends during the first 90 days following stroke. Results: Data were collected on a total sample of 114 participants. Out of the 114 participants, 73 participants had recorded sleep data, and 76 participants had step data reported, which are not mutually exclusive groups. Out of 114 total participants, 41 participants did not have any sleep or step data recorded. We obtained patient-specific time trends for both patients with and without monitor data. To assess model predictive performance, we compared predicted estimates to observed data using a validation study and found that on average, there was a difference of 0.52 daily steps (counts). Additionally, on average, there was a difference of 0.46 between observed and estimated daily sleep count (minutes). Discussion: The contribution of our study is a model-based approach to accurately predict patient-specific longitudinal step and sleep behaviors based on predictor variables and previous time trends for patients in the subacute stroke recovery phase. Our approach may be extended to other applications using activity monitor data. A limitation of this study is the increased uncertainty surrounding forecast estimates for periods/days without data, which will be addressed in future work.

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%.

Biogeomorphology from space: Analyzing the dynamic interactions between hydromorphology and vegetation along the Naryn River in Kyrgyzstan based on dense satellite time series

The structure and development of river corridors are controlled by an interplay of hydrological, geomorphological and ecological processes over a range of spatial and temporal scales. The majority of existing studies on fluvial biogeomorphology focus either on conceptual development or on investigations on the scales of single geomorphic units or study reaches. With this study, we extend the view on biogeomorphology beyond the reach scale using satellite time series. We take the Naryn River in Kyrgyzstan as an example for demonstrating our approach to biogeomorphological analysis of river corridors. The Naryn is still in a natural state on an entire flow length of more than 600 km with full longitudinal and lateral connectivity. Along the central part of the catchment, the Naryn is a highly dynamic braided river system. This makes this river ideal to study large scale biogeomorphological dynamics. In our study, we follow the well-established concept of biogeomorphological succession. We mapped the different succession stages in the field and used the results to derive spectral-temporal indices characterizing the different biogeomorphological succession stages. Monthly time series of the modified difference water index as well as the normalized difference vegetation index derived from the Landsat archive have been used to compute statistical trends and changepoints by means of a Bayesian time series decomposition algorithm. The results are then evaluated regarding biogeomorphological succession and disturbance events. The results show that dense satellite time series are well suited to derive indicators of biogeomorphological interactions on large spatial scale. The temporally continuous nature of this kind of observations allows an observation of processes and an interpretation for instance against the background of the theory of adaptive cycles and panarchy. In conclusion, the suggested approach has the potential to give new insights in the structure and functioning of entire river corridors or networks.

Ensemble transport smoothing. Part I: Unified framework

Smoothers are algorithms for Bayesian time series re-analysis. Most operational smoothers rely either on affine Kalman-type transformations or on sequential importance sampling. These strategies occupy opposite ends of a spectrum that trades computational efficiency and scalability for statistical generality and consistency: non-Gaussianity renders affine Kalman updates inconsistent with the true Bayesian solution, while the ensemble size required for successful importance sampling can be prohibitive. This paper revisits the smoothing problem from the perspective of measure transport, which offers the prospect of consistent prior-to-posterior transformations for Bayesian inference. We leverage this capacity by proposing a general ensemble framework for transport-based smoothing. Within this framework, we derive a comprehensive set of smoothing recursions based on nonlinear transport maps and detail how they exploit the structure of state-space models in fully non-Gaussian settings. We also describe how many standard Kalman-type smoothing algorithms emerge as special cases of our framework. A companion paper [35] explores the implementation of nonlinear ensemble transport smoothers in greater depth.

An Electronic Medical Record Intervention to Increase Pharmacologic Prophylaxis for Venous Thromboembolism in Emergency Department Observation Patients

The role of venous thromboembolism (VTE) prophylaxis among patients receiving emergency department (ED) observation unit care is unclear. We investigated an electronic health record-based clinical decision support tool aimed at increasing pharmacologic VTE prophylaxis use among at-risk patients placed in ED observation units. We conducted an interrupted time-series study of an Epic-based best practice advisory implemented in May 2019 at a health care system comprising 2 academic medical centers and 4 community hospitals with dedicated ED observation units. The best practice advisory alerted staff at 24 hours to conduct a risk assessment and linked to a VTE prophylaxis order set. We used an interrupted time series, Bayesian structured time series, and a multivariable mixed-effect regression model to estimate the intervention effect. Prior to the best practice advisory implementation, there were 8,895 ED observation unit patients with a length of stay more than or equal to 24 hours, and 0.9% received pharmacologic VTE prophylaxis. Afterward, there were 12,664 ED observation unit patients with a length of stay more than or equal to 24 hours, and 4.8% received pharmacologic VTE prophylaxis. The interrupted time series and causal impact analysis showed a statistically significant increase in VTE prophylaxis (eg, absolute percent difference 3.8%, 95% confidence interval 3.5 to 4.1). In a multivariable model, only the intervention was significantly associated with receiving VTE prophylaxis (odds ratio 4.56, 95% confidence interval 2.22 to 9.37). An electronic health record-based alert helped to prompt staff caring for ED observation unit patients at risk for VTE with prolonged visits to order recommended pharmacologic prophylaxis. The best risk assessment model to use and the true incidence of VTE events in this population are unclear.

The impact of EV71 vaccination program on hand, foot and mouth disease in Zhejiang Province, China: A negative control study

ObjectiveTo estimate the potential causal impact of Enterovirus A71 (EV71) vaccination program on the reduction of EV71-infected hand, foot, and mouth disease (HFMD) in Zhejiang Province. MethodsWe utilized the longitudinal surveillance dataset of HFMD and EV71 vaccination in Zhejiang Province during 2010–2019. We estimated vaccine efficacy using a Bayesian structured time series (BSTS) model, and employed a negative control outcome (NCO) model to detect unmeasured confounding and reveal potential causal association. ResultsWe estimated that 20,132 EV71 cases (95% CI: 16,733, 23,532) were prevented by vaccination program during 2017–2019, corresponding to a reduction of 29% (95% CI: 24%, 34%). The effectiveness of vaccination increased annually, with reductions of 11% (95% CI: 6%, 16%) in 2017 and 66% (95% CI: 61%, 71%) in 2019. Children under 5 years old obtained greater benefits compared to those over 5 years. Cities with higher vaccination coverage experienced a sharper EV71 reduction compared to those with lower coverage. The NCO model detected no confounding factors in the association between vaccination and EV71 cases reduction. ConclusionsThis study suggested a potential causal effect of the EV71 vaccination, highlighting the importance of achieving higher vaccine coverage to control the HFMD.

Bayesian Time Series Modeling of Necrotizing Fasciitis Count in Mahasarakham and Roi-Et Hospitals

Bayesian Time Series Modeling of Necrotizing Fasciitis Count in Mahasarakham and Roi-Et Hospitals

Impact of Supplementary Immunization Activities using Novel Oral Polio Vaccine Type 2 during a Large outbreak of Circulating Vaccine-Derived Poliovirus in Nigeria.

Novel oral poliovirus vaccine (OPV) type 2 (nOPV2) has been made available for outbreak response under an emergency use listing authorization based on supportive clinical trial data. Since 2021 more than 350 million doses of nOPV2 were used for control of a large outbreak of circulating vaccine-derived poliovirus type 2 (cVDPV2) in Nigeria. Using a bayesian time-series susceptible-infectious-recovered model, we evaluate the field effectiveness of nOPV2 immunization campaigns in Nigeria compared with campaigns using monovalent OPV type 2 (mOPV2). We found that both nOPV2 and mOPV2 campaigns were highly effective in reducing transmission of cVDPV2, on average reducing the susceptible population by 42% (95% confidence interval, 28-54%) and 38% (20-51%) per campaign, respectively, which were indistinguishable from each other in this analysis (relative effect, 1.1 [.7-1.9]). Impact was found to vary across areas and between immunization campaigns. These results are consistent with the comparable individual immunogenicity of nOPV2 and mOPV2 found in clinical trials but also suggest that outbreak response campaigns may have small impacts in some areas requiring more campaigns than are suggested in current outbreak response procedures.