Correlated Time Series Research Articles

Fluorescent Photoelectric Detection of Peroxide Explosives Based on a Time Series Similarity Measurement Method.

Due to the characteristics of peroxide explosives, which are difficult to detect via conventional detection methods and have high explosive power, a fluorescent photoelectric detection system based on fluorescence detection technology was designed in this study to achieve the high-sensitivity detection of trace peroxide explosives in practical applications. Through actual measurement experiments and numerical simulation methods, the derivative dynamic time warping (DDTW) algorithm and the Spearman correlation coefficient were used to calculate the DDTW-Spearman distance to achieve time series correlation measurements. The detection sensitivity of triacetone triperoxide (TATP) and H2O2 was studied, and the detection of organic substances of acetone, acetylene, ethanol, ethyl acetate, and petroleum ether was carried out. The stability and specific detection ability of the fluorescent photoelectric detection system were determined. The research results showed that the fluorescence photoelectric detection system can effectively identify the detection data of TATP, H2O2, acetone, acetonitrile, ethanol, ethyl acetate, and petroleum ether. The detection limit of 0.01 mg/mL of TATP and 0.0046 mg/mL of H2O2 was less than 10 ppb. The time series similarity measurement method improves the analytical capabilities of fluorescence photoelectric detection technology.

Acute SARS-CoV-2 Infection and Incidence and Outcomes of Out-of-Hospital Cardiac Arrest

Little is known about how COVID-19 affects the incidence or outcomes of out-of-hospital cardiac arrest (OHCA), and it is possible that more generalized factors beyond SARS-CoV-2 infection are primarily responsible for changes in OHCA incidence and outcome. To assess whether COVID-19 is associated with OHCA incidence and outcomes. This retrospective cohort study was conducted in Seattle and King County, Washington. Participants included persons aged 18 years or older with nontraumatic OHCA attended by emergency medical services (EMS) between January 1, 2018, and December 31, 2021. Data analysis was performed from November 2022 to March 2023. Prepandemic (2018-2019) and pandemic (2020-2021) periods and SARS-CoV-2 infection. The primary outcomes were OHCA incidence and patient outcomes (ie, survival to hospital discharge). Mediation analysis was used to determine the percentage change in OHCA incidence and outcomes between prepandemic and pandemic periods that was attributable to acute SARS-CoV-2 infection vs conventional Utstein elements related to OHCA circumstances (ie, witness status and OHCA location) and resuscitation care (ie, bystander cardiopulmonary resuscitation, early defibrillation, and EMS response intervals). There were a total of 13 081 patients with OHCA (7102 dead upon EMS arrival and 5979 EMS treated). Among EMS-treated patients, the median (IQR) age was 64.0 (51.0-75.0) years, 3864 (64.6%) were male, and 1027 (17.2%) survived to hospital discharge. The total number of patients with OHCA increased by 19.0% (from 5963 in the prepandemic period to 7118 in the pandemic period), corresponding to an incidence increase from 168.8 to 195.3 events per 100 000 person-years. Of EMS-treated patients with OHCA during the pandemic period, 194 (6.2%) were acutely infected with SARS-CoV-2 compared with 7 of 191 EMS-attended but untreated patients with OHCA (3.7%). In time-series correlation analysis, there was a positive correlation between community SARS-CoV-2 incidence and overall OHCA incidence (r = 0.27; P = .01), as well as OHCA incidence with acute SARS-CoV-2 infection (r = 0.43; P < .001). The survival rate during the pandemic period was lower than that in the prepandemic period (483 patients [15.4%] vs 544 patients [19.2%]). During the pandemic, those with OHCA and acute SARS-CoV-2 infection had lower likelihood of survival compared with those without acute infection (12 patients [6.2%] vs 471 patients [16.0%]). SARS-CoV-2 infection itself accounted for 18.5% of the pandemic survival decline, whereas Utstein elements mediated 68.2% of the survival decline. In this cohort study of COVID-19 and OHCA, a substantial proportion of the higher OHCA incidence and lower survival during the pandemic was not directly due to SARS-CoV-2 infection but indirect factors that challenged OHCA prevention and treatment.

Attention-based generative adversarial networks for aquaponics environment time series data imputation

Environmental parameter data collected by sensors for monitoring the environment of agricultural facility operations are usually incomplete due to external environmental disturbances and device failures. And the missing of collected data is completely at random. In practice, missing data could create biased estimations and make multivariate time series predictions of environmental parameters difficult, leading to imprecise environmental control. A multivariate time series imputation model based on generative adversarial networks and multi-head attention (ATTN-GAN) is proposed in this work to reducing the negative consequence of missing data. ATTN-GAN can capture the temporal and spatial correlation of time series, and has a good capacity to learn data distribution. In the downstream experiments, we used ATTN-GAN and baseline models for data imputation, and predicted the imputed data, respectively. For the imputation of missing data, over the 20%, 50% and 80% missing rate, ATTN-GAN had the lowest RMSE, 0.1593, 0.2012 and 0.2688 respectively. For water temperature prediction, data processed with ATTN-GAN over MLP, LSTM, DA-RNN prediction methods had the lowest MSE, 0.6816, 0.8375 and 0.3736 respectively. Those results revealed that ATTN-GAN outperformed all baseline models in terms of data imputation accuracy. The data processed by ATTN-GAN is the best for time series prediction.

IMERG Precipitation Improves the SMAP Level-4 Soil Moisture Product

Abstract The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface and root-zone soil moisture from April 2015 to the present with a mean latency of 2.5 days from the time of observation. The L4_SM algorithm assimilates SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observation-based precipitation. This paper describes and evaluates the use of satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products in the L4_SM algorithm beginning with L4_SM Version 6. Specifically, IMERG is used in two ways: (i) The L4_SM precipitation reference climatology is primarily based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project Version 2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. The use of IMERG precipitation improves the anomaly time series correlation coefficient of L4_SM surface soil moisture (versus independent satellite estimates) by 0.03 in the global average and by up to ∼0.3 in parts of South America, Africa, Australia, and East Asia, where the quality of the gauge-only precipitation product used in earlier L4_SM versions is poor. The improvements also reduce the time series standard deviation of the Tb observation-minus-forecast residuals from 5.5 K in L4_SM Version 5 to 5.1 K in Version 6. Significance Statement Soil moisture links the land surface water, energy, and carbon cycles. NASA Soil Moisture Active Passive (SMAP) satellite observations and observation-based precipitation data are merged into a numerical model of land surface water and energy balance processes to generate the global, 9-km resolution, 3-hourly Level-4 Soil Moisture (L4_SM) data product. The product is available with ∼2.5-day latency to support Earth science research and applications, such as flood prediction and drought monitoring. We show that a recent L4_SM algorithm update using satellite- and gauge-based precipitation inputs from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products improves the temporal variations in the estimated soil moisture, particularly in otherwise poorly instrumented regions in South America, Africa, Australia, and East Asia.

A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN

Due to the lack of historical data, accurate prediction is a great challenge for newly constructed wind farms (NWFs). How to guarantee satisfactory prediction accuracy with limited data has become a bottleneck issue in wind power prediction (WPP). To solve the few-shot learning problem in NWFs, a novel multi-gradient evolutionary deep learning neural network (EATDLNN) prediction model is proposed, which incorporates the time-series GAN (TimeGAN) and multivariate variational mode decomposition (MVMD) method. TimeGAN adds an auto-encoder network and a supervised loss function to GAN, which enables it to capture the dynamic time series correlation characteristics of wind data and generate high-quality samples. MVMD is then employed to simultaneously decompose nonstationary sequences. In the EATDLNN prediction model, an evolution-based multi-gradient training approach is proposed to train the DLNN by integrating three gradient descent methods (i.e., Adam, MBGD-Momentum, RMSprop). Compared with the traditional single-gradient training approach, the multi-gradient training approach provides different gradient descent paths and the gradient can adaptively choose to update towards the path with the smallest error based on the evolutionary framework, which can help the model jump out of the local optimum and enhance the generalization ability. Massive experiments prove the excellent performance of the proposed method, especially in one-step prediction, the root mean square errors reduce by 79.40%∼91.05% compared with other state-of-the-art methods.

Predictive model of pump energy consumption based on image feature extraction and instance-based transfer learning

The energy consumption of pumps for water supply accounts for a high proportion of the total global energy consumption. The analysis of the pump's energy consumption factors and the construction of prediction models play a crucial role in carbon emission reductions throughout the water supply process. However, difficulties in accurately constructing input features under complex variables and the scarcity and anomalies of monitoring data during system operation pose great challenges to accurate energy consumption prediction. In order to address this issue, a water pump energy consumption prediction method based on image feature extraction and instance transfer is proposed, using the water pump systems installed in two residential communities as the research objects. Firstly, a novel image-based correlation determination method for feature engineering was introduced and validated on a sufficient dataset. Subsequently, in the context of poor data transfer learning, a water pump energy consumption prediction model, named Two-stage TrAdaBoost.R2 (TSTR), was established by incorporating the image correlation determination method and fine-tuning the model parameters using the particle swarm optimization algorithm to enhance prediction accuracy. The experimental results demonstrated the effectiveness of the proposed image-based correlation determination method, showing a 5.49%–44.33% improvement in the coefficient of variation of root mean square error (CV-RMSE) prediction accuracy compared to classical time-series correlation determination methods. Furthermore, in the poor data transfer prediction experiment, the TSTR-based model achieved 0.90% and 20.22% higher CV-RMSE prediction accuracy compared to two comparative models. These findings confirmed the effectiveness of instance transfer learning methods in poor data scenarios and provided valuable insights for selecting input feature construction methods under complex variables.

Modified DFA for a robust discrimination between short-term and long-range correlations in short time series

Since the standard detrended fluctuation analysis (s-DFA) method depends on asymptotic power-law scaling assumption, it has been questioned on its ability in characterizing the correlation structure at the small scales, let alone discriminating the correlation structures of short time series. A modified DFA (m-DFA) with a novel defined fluctuation function is employed in this study to discriminate and characterize the distinct (short-termed or long-ranged) correlation structures in short time series. Detailed results show that the m-DFA is able not only to distinguish the short-termed, or long-ranged correlation, but also to well quantify the correlation strengths in both output from classical models and real-world series (for example, DTR variability over China) of data length as short as 2000. Moreover, m-DFA can work effectively in time series with strong correlation of even shorter length as 500. The correlation structures inferred by m-DFA in short intervals contribute greatly to better understanding of local and evolutionary correlation features of an underlying process, not limited to only global one from s-DFA.

Some empirical results using block bootstrap in estimating the coefficients of a periodic autoregressive model

Abstract The bootstrap proposed by Efron (1979) resulted a useful method in estimating the distribution of an estimator or a test statistic by resampling the data in the case of independent and identically distributed observations. Although it was not as effective in the case of dependent data as in the case of independent and identically distributed data, an adaptation was obtained using the block bootstrap. The block bootstrap consists in dividing the data into blocks of observations and then resampling these blocks with replacement. When resampling periodic data, we must take in consideration the periodicity present. Periodically correlated time series and in particular those related with PAR processes have been object of many recent studies due to numerous applications in real data problems. The aim of this paper is to use a block bootstrap procedure proposed, Block Bootstrap of the Residuals, in the case of PAR (Periodic Autoregressive) models. The results obtained in the case of estimating the coefficients in a PAR model studied are very good and are characterized by small values of Bias, Mean Squared Error and Standard Deviation. Also the bootstrap estimations obtained are closer to the true values than the usual classic point estimations.

Improving the crop classification performance by unlabeled remote sensing data

Accurate and timely crop classification results play a crucial role in providing data support for agricultural policy-making and crop yield estimation. However, the current development of crop classification faces a bottleneck in improving classification performance due to limited labeled samples and saturated classification algorithms. In this study, we propose a novel method to improve crop classification performance by leveraging unlabeled remote sensing data (URSD). Importantly, our method does not necessitate a large number of labeled samples or significant modifications to the classification algorithm. Instead, it relies on a unique self-supervised training approach and a substantial amount of URSD. Specifically, we develop a self-supervised classification framework based on a Multilayer Perceptron (MLP) and introduce a self-supervised training approach that takes into account both temporal and spectral factors. Additionally, we construct a historical sample classification model based on crop growth knowledge, emphasizing the correlation of local time series. We evaluate the proposed method using four study areas in China. The analysis of pre-training data types reveals that our method not only improves the classification performance of current year samples but also demonstrates noticeable improvement in classifying historical samples. The classification method analysis demonstrates the ability of our proposed self-supervised learning training approach to accumulate more prior knowledge. Overall, these results highlight the advantages of our method in terms of classification efficiency and performance improvement.

Use of social media data, online reviews and wikipedia page views to measure visitation patterns of outdoor attractions

This study explores the suitability of activity counts extracted from social media platforms (Twitter, Flickr), review portals (TripAdvisor, Google Maps) and Wikipedia article views to model official visitor counts at selected outdoor attractions in Florida (U.S.) and Carinthia (Austria). It applies correlation analysis, multiple regression, and time series analysis to identify which of these user-generated content (UGC) sources and their combinations best match official monthly visitor count patterns for an analysis period of three years (2019–2021). With travel activities being severely hampered during 2020 due to the COVID-19 pandemic, the analysis also aims to analyze to which extent reduced visitor counts are reflected in the respective UGC sources. Results show that the number of Google Maps reviews combined with Wikipedia pageviews best explain the variability of monthly official visitor counts in Ordinary Least Squares (OLS) regression in both study areas. While the comparison analysis was conducted for monthly counts, data from some UGC sources can reflect shorter term activity fluctuations. Time series analysis detected a seasonality of 12 months for Wikipedia pageviews, Google Maps reviews, and official visitor counts for Austria due a clearly distinct summer season. As opposed to this, for Florida, due to a climate that facilitates all-year round park visitations, periodograms yielded different seasonality frequencies for UGC sources and official visitor counts. A short-term drop in visitor counts due to COVID-19 was evident in Florida both in UGC sources and official visitor counts in spring 2020, whereas for Austria attractions reduced activity is only somewhat reflected in UGC sources but not in actual visitor counts due to attractions still being closed in April. Management implicationsThis research explores multiple UGC sources for modeling official visitor counts at outdoor attractions. A combination of several sources has the advantage that it can help mitigate known limitations of UGC from individual sources, such as sparsity of geodata, data retrieval restrictions, sociodemographic bias, and varying popularity across regions. This research also revealed a better model fit of combined UGC data with official visitor counts than using any of the UGC sources alone. Data-rich UGC sources offer daily or weekly activity counts that provide a more refined temporal resolution than typical official visitor counts which are often limited to monthly data aggregations.

Automatic Calculation of Graphic Area Change Rate for Icing Overhead Power Line Insulators Based on Bounding Box Automatic Matching and GrabCut Contour Automatic Segmentation

Image monitoring of icing insulators is valuable for ice-disaster risk management of overhead power lines. This paper proposes an automatic calculation method of graphic area change rate for icing insulators to address laborious manual viewing, computational complexity, poor generalization, weak interpretability, and label data demand of existing approaches. The method automatically identifies insulators in monitoring images by the trained YOLOv5 model, matches the ice-free and test images taken by the same terminal at the same angle utilizing intersection over union of identified bounding boxes, and segments them using an improved GrabCut algorithm. In the segmentation maps, insulator string contours before and after icing are automatically detected to compute the graphic area change rate. The graphic area change rate of insulators in 815 images monitored by 22 terminals from December 2017 to February 2018 is automatically calculated, which is separately data-fused with the tension monitoring value and equivalent ice thickness collected closest to the image acquisition time to obtain the 22 terminals' time series correlations. The correlations approximately follow normal distributions with mathematical expectations of 0.70 and 0.68 and reach 0.95 and 0.94 for high-quality image samples, respectively, verifying the effectiveness. The method enables real-time evaluation of insulators' icing degrees.

Exploring the Multifractality in the Precious Metal Market

This study proposes a novel approach to investigating the multifractality of time series using the multifractal cross-correlation detrended moving average analysis (MF-X-DMA). The study demonstrates the behavioral differences of MF-X-DMA in coherent and non-coherent time periods. Due to the lack of a mechanism to capture the dynamical cross-correlation in time series, correlated time series with multifractal structure present a barrier for analysis. The study shows that when the wavelet coherence method is applied to time series, co-movement between time series can be easily captured in certain time intervals, providing an efficient way to find time intervals to apply MF-X-DMA. The study applies the wavelet coherence method to the daily spot prices of gold and platinum from January 1987. It shows that the wavelet coherence method is an excellent engine to extract designated time series in certain frequency and time intervals, eliminating the need for windowing or shuffling methods. Additionally, the study observes a long-term power law cross-correlation using detrended cross-correlation analysis coefficients of inversed series for both low-correlated and high-correlated series. Finally, the findings indicate that MF-X-DMA leads to superior results compared to MF-DFA when provided with highly correlated data.

Detection of Jamming Attacks via Source Separation and Causal Inference

Jamming attacks to hinder communication capabilities are becoming a critical aspect of wireless networks. A challenging issue is the detection of reactive jammers that perform spectrum sensing and attack the network only when legitimate communication is in progress. In this scenario, we introduce a novel framework for reactive jamming detection using a patrol of radio-frequency (RF) sensors external to the network to be protected. The solution relies on two key components: i) a novel underdetermined blind source separation (UBSS) method that, starting from the signal mixtures observed by the RF patrollers, is capable of separating the jamming temporal profile from the network nodes’ transmission profiles; ii) a new jamming detection based on causal inference called all-versus-one transfer entropy (AvOTE). The framework is then applied to a case study where the victim network is a Long Range (LoRa)-based internet of things (IoT) system with star topology. The solution outperforms a state-of-the-art method and an approach that attempts to find the causal relationship via time series correlation, exhibiting very good performance in the presence of shadowing. Indeed, a detection probability of 90% is achieved with a false alarm probability of 6% in the presence of nuisances such as collisions and severe shadowing.

Multifractal Features and Dynamical Thresholds of Temperature Extremes in Bangladesh

Multifractal detrended fluctuation analysis (DFA) can extract multi-scaling behavior and measure long-range correlations in climatic time series. In this study, with the help of multifractal DFA, we investigated the scaling behavior of daily minimum/maximum temperatures during the years 1989–2019 from 34 meteorological stations in Bangladesh. We revealed spatial patterns, topographic impacts and global warming impacts of long-range correlations embedded in small and large fluctuations in temperature time series. Meanwhile, we developed a multifractal DFA-based algorithm to dynamically determine thresholds to discriminate extreme and non-extreme events in climate systems and applied it to analyze the frequency and trends of temperature extremes in Bangladesh. Compared with widely-used percentile thresholds, the extreme climate events captured in our algorithm are more reliable since they are determined dynamically by the climate system itself.

Between news and history: identifying networked topics of collective attention on Wikipedia

The digital information landscape has introduced a new dimension to understanding how we collectively react to new information and preserve it at the societal level. This, together with the emergence of platforms such as Wikipedia, has challenged traditional views on the relationship between current events and historical accounts of events, with an ever-shrinking divide between “news” and “history”. Wikipedia’s place as the Internet’s primary reference work thus poses the question of how it represents both traditional encyclopaedic knowledge and evolving important news stories. In other words, how is information on and attention towards current events integrated into the existing topical structures of Wikipedia? To address this, we develop a temporal community detection approach towards topic detection that takes into account both short term dynamics of attention as well as long term article network structures. We apply this method to a dataset of one year of current events on Wikipedia to identify clusters of Wikipedia articles related to news events, distinct from those that would be found solely from page view time series correlations or static network structure. We are able to resolve the topics that more strongly reflect unfolding current events vs more established knowledge by the relative importance of collective attention dynamics vs link structures. We also offer important developments by identifying and describing the emergent topics on Wikipedia. This work provides a means of distinguishing how these information and attention clusters are related to Wikipedia’s twin faces of encyclopaedic knowledge and current events—crucial to understanding the production and consumption of knowledge in the digital age.

Network effects and store-of-value features in the cryptocurrency market

It is important to determine the network effects and store-of-value feature of cryptocurrencies due to the argument that it could be considered as a new ‘asset class’. Current studies on cryptocurrencies' network effects mainly focused on using Metcalfe's Law to evaluate the relationship between cryptocurrency prices and the squared number of active wallets addresses. In terms of cryptocurrencies' store-of-value features, previous studies primarily compared daily volatility of limited number of popular cryptocurrencies to Gold. Extant studies are also based on out-of-date data. This research extends the literature by using up-to-date daily data of a sample of the top 100 cryptocurrencies covering 2010–2023 to explore the network effects and the store of value characteristics of a wide range of cryptocurrencies. Firstly, we used nonlinear regression models to examine the relationship between cryptocurrency prices and active wallets addresses, the number of transactions and circulations. Secondly, to deepen our understanding of the store-of-value features of cryptocurrencies, we used a combination of GARCH models and time series analysis to explore the volatility in the daily returns of the sampled cryptocurrencies. Findings indicate that at least one of the network factors (i.e., active wallets addresses, the number of transactions, and number of circulation supply) have a significant effect on cryptocurrency prices. The study also finds that stable coins have comparable daily volatility as Gold, while only mature cryptocurrencies, such as PAXG, Bitcoin, Ethereum, BNB and LINK, demonstrate strong correlation with Gold. Bitcoin also showed a high positive time-series correlation with 24 of the 42 cryptocurrencies. Findings from this study provide important insights to investors, market analysts, regulators and other stakeholders on the marketisation and the store of value potentials of cryptocurrencies.

Graph Spatio-Temporal networks for manufacturing sales forecast and prevention policies in pandemic era

Worldwide manufacturing industries are significantly affected by COVID-19 pandemic because of their production characteristics with low-cost country sourcing, globalization, and inventory level. To analyze the correlated time series, spatial–temporal model becomes more attractive, and the graph convolution network (GCN) is also commonly used to provide more information to the nodes and its neighbors in the graph. Recently, attention-adjusted graph spatio-temporal network (AGSTN) was proposed to address the problem of pre-defined graph in GCN by combining multi-graph convolution and attention adjustment to learn spatial and temporal correlations over time. However, AGSTN may show potential problem with limited small non-sensor data; particularly, convergence issue. This study proposes several variants of AGSTN and applies them to non-sensor data. We suggest data augmentation and regularization techniques such as edge selection, time series decomposition, prevention policies to improve AGSTN. An empirical study of worldwide manufacturing industries in pandemic era was conducted to validate the proposed variants. The results show that the proposed variants significantly improve the prediction performance at least around 20% on mean squared error (MSE) and convergence problem.

Dynamic Prediction of Longitudinal Settlement of Existing Tunnel Using ConvRes-DLinear Model with Integration of Undercrossing Construction Process Information

Undercrossing construction can cause severe structural deformation of the above existing tunnel in operation. The induced longitudinal differential settlement between the segments can pose a huge risk to running subways, hence it is of great importance to monitor and predict the settlement. Within this study, a Wireless Sensor Network (WSN) system was implemented to obtain hourly monitoring data of settlement from the very beginning of undercrossing to post construction period. An improved direct multi-step (DMS) forecasting model called ConvRes-DLinear is proposed, which fuses monitoring data with time and process encoding bias to deeply extract and learn temporal correlation of time series. A residual LSTM model is also constructed to compare the accuracy of the improved DLinear model. The training and testing experiment on the monitoring data of longitudinal settlement obtained by WSN system shows that the ConvRes-DLinear model with time and process encoding bias performs surprisingly well with a minimum prediction error. The features of the proposed model are discussed to make the results explainable. The monitoring system and time series forecasting model proposed in this study have a guiding significance for the monitoring and prediction of longitudinal differential settlement of tunnels under environmental disturbance.

Temporal association between the age-specific incidence of Guillain-Barré syndrome and SARS-CoV-2 vaccination in Republic of Korea: a nationwide time-series correlation study.

The incidence of Guillain-Barré syndrome (GBS) changed significantly during the coronavirus disease 2019 (COVID-19) pandemic. Emerging reports suggest that viral vector-based vaccines may be associated with an elevated risk of GBS. In this nationwide time-series correlation study, we examined the age-specific incidence of GBS from January 2011 to August 2022, as well as data on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccinations and infections from February 2021 to August 2022. We compared the forecasted estimates of age-specific GBS incidence, using the pre-SARS-CoV-2 period as a benchmark, with the actual incidence observed during the post-vaccination period of the pandemic. Furthermore, we assessed the temporal association between GBS, SARS-CoV-2 vaccinations, and COVID-19 for different age groups. In the age group of 60 and older, the rate ratio was significantly elevated during June-August and November 2021. A significant, strong positive association was observed between viral vector-based vaccines and GBS incidence trends in this age group (r=0.52, p=0.022). For the 30 to 59 years age group, the rate ratio was notably high in September 2021. A statistically significant, strong positive association was found between mRNA-based vaccines and GBS incidence in this age group (r=0.61, p=0.006). Viral vector-based SARS-CoV-2 vaccines were found to be temporally associated with an increased risk of GBS, particularly in older adults. To minimize age-specific and biological mechanism-specific adverse events, future vaccination campaigns should adopt a more personalized approach, such as recommending homologous mRNA-based SARS-CoV-2 vaccines for older adults to reduce the heightened risk of GBS.

Periodic Characteristics of Hepatitis Virus Infections From 2013 to 2020 and Their Association With Meteorological Factors in Guangdong, China: Surveillance Study

Background In the past few decades, liver disease has gradually become one of the major causes of death and illness worldwide. Hepatitis is one of the most common liver diseases in China. There have been intermittent and epidemic outbreaks of hepatitis worldwide, with a tendency toward cyclical recurrences. This periodicity poses challenges to epidemic prevention and control. Objective In this study, we aimed to investigate the relationship between the periodic characteristics of the hepatitis epidemic and local meteorological elements in Guangdong, China, which is a representative province with the largest population and gross domestic product in China. Methods Time series data sets from January 2013 to December 2020 for 4 notifiable infectious diseases caused by hepatitis viruses (ie, hepatitis A, B, C, and E viruses) and monthly data of meteorological elements (ie, temperature, precipitation, and humidity) were used in this study. Power spectrum analysis was conducted on time series data, and correlation and regression analyses were performed to assess the relationship between the epidemics and meteorological elements. Results The 4 hepatitis epidemics showed clear periodic phenomena in the 8-year data set in connection with meteorological elements. Based on the correlation analysis, temperature demonstrated the strongest correlation with hepatitis A, B, and C epidemics, while humidity was most significantly associated with the hepatitis E epidemic. Regression analysis revealed a positive and significant coefficient between temperature and hepatitis A, B, and C epidemics in Guangdong, while humidity had a strong and significant association with the hepatitis E epidemic, and its relationship with temperature was relatively weak. Conclusions These findings provide a better understanding of the mechanisms underlying different hepatitis epidemics and their connection to meteorological factors. This understanding can help guide local governments in predicting and preparing for future epidemics based on weather patterns and potentially aid in the development of effective prevention measures and policies.