Climate influence the human leptospirosis cases in Brazil, 2007-2019: a time series analysis

BACKGROUND

Human leptospirosis is responsible for great losses and deaths, especially in developing countries, which can be mitigated by knowing the correct health indicators and climate influence on the disease.

METHODS

Leptospirosis cases and deaths, population and precipitation were recovered from different databases (2007-2019). Annual incidence, mortality and case fatality rates (CFRs) of human leptospirosis and average precipitation were calculated for Brazil and its regions. Time series analysis using an moving average with external variable (ARMAX) model was used to analyse the monthly contribution and precipitation influence over leptospirosis cases for each Brazilian region and for the whole country. A forecast model to predict cases for 2020 was created for Brazil.

RESULTS

Human leptospirosis exhibited heterogeneous distribution among Brazilian regions, with most cases occurring during the rainy season and precipitation influenced the disease occurrence in all regions but the South. The forecast model predicted 3276.99 cases for 2020 (mean absolute percentage error 14.680 and root mean square error 53.013). Considering the annual average for the period, the leptospirosis incidence was 1913 cases per 100 000 inhabitants, mortality was 0.168 deaths per 100 000 inhabitants and the CFR was 8.83%.

CONCLUSIONS

The models built can be useful for planning leptospirosis surveillance and control actions for the whole country and its regions and, together with the health indicators, revealed no uniform epidemiological situation of leptospirosis in Brazil.

A Fishery Water Quality Monitoring and Prediction Evaluation System for Floating UAV Based on Time Series

In recent years, fishery has developed rapidly. For the vital interests of the majority of fishermen, this paper makes full use of Internet of Things and air–water amphibious UAV technology to provide an integrated system that can meet the requirements of fishery water quality monitoring and prediction evaluation. To monitor target water quality in real time, the water quality monitoring of the system is mainly completed by a six-rotor floating UAV that carries water quality sensors. The GPRS module is then used to realize remote data transmission. The prediction of water quality transmission data is mainly realized by the algorithm of time series comprehensive analysis. The evaluation rules are determined according to the water quality evaluation standards to evaluate the predicted water quality data. Finally, the feasibility of the system is proved through experiments. The results show that the system can effectively evaluate fishery water quality under different weather conditions. The prediction accuracy of the pH, dissolved oxygen content, and ammonia nitrogen content of fishery water quality can reach 99%, 98%, and 99% on sunny days, and reach 92%, 98%, and 91% on rainy days.

Nine Months of COVID-19 Pandemic in Europe: A Comparative Time Series Analysis of Cases and Fatalities in 35 Countries

(1) Background: to describe the dynamic of the pandemic across 35 European countries over a period of 9 months. (2) Methods: a three-phase time series model was fitted for 35 European countries, predicting deaths based on SARS-CoV-2 incidences. Hierarchical clustering resulted in three clusters of countries. A multiple regression model was developed predicting thresholds for COVID-19 incidences, coupled to death numbers. (3) Results: The model showed strongly connected deaths and incidences during the waves in spring and fall. The corrected case-fatality rates ranged from 2% to 20.7% in the first wave, and from 0.5% to 4.2% in the second wave. If the incidences stay below a threshold, predicted by the regression model (R2=85.0%), COVID-19 related deaths and incidences were not necessarily coupled. The clusters represented different regions in Europe, and the corrected case-fatality rates in each cluster flipped from high to low or vice versa. Severely and less severely affected countries flipped between the first and second wave. (4) Conclusions: COVID-19 incidences and related deaths were uncoupled during the summer but coupled during two waves. Once a country-specific threshold of infections is reached, death numbers will start to rise, allowing health care systems and countries to prepare.

Reductions in mortality resulting from COVID-19 quarantine measures in China

Background

To explore the impact of quarantine measures on the cause of death.

Methods

We use time series analysis with the data from death cause surveillance database of Suzhou from January 2017 to December 2019 to estimate the expected deaths from January to June 2020 and compare these expected deaths with the reported numbers of deaths.

Results

After the implementation of epidemic prevention measures in Suzhou in the first 3 months, overall number of all-cause deaths declined for 5.36, 7.54 and 7.02% compared with predicted numbers. The number of deaths from respiratory causes and traffic accidents declined shapely by 30.1 and 26.9%, totally. When quarantine measures were released (April–June), however, the observed numbers of total deaths exceeded the predicted deaths. People aged over 70 accounted for 91.6% of declined death number in respiratory causes and people aged over 60 accounted for 68.0% of declined death number in traffic accidents. Women over the age of 80 benefited the most from respiratory prevention (accounts for 41% of all reductions), whereas women aged over 60 benefited the most from traffic control (44%).

Conclusions

Overall, the whole population benefited from the epidemic prevention measures especially elderly females. This study is a useful supplement to encourage the government to develop regular preventive measures under the era of normalized epidemic.

Mathematical model, forecast and analysis on the spread of COVID-19

Pandemic COVID-19 which has infected more than 35,027,546 people and death more than 1,034,837 people in 235 countries as on October 05, 2020 has created a chaos across the globe. In this paper, we develop a compartmental epidemic model to understand the spreading behaviour of the disease in human population with a special case of Bhilwara, a desert town in India where successful control measures TTT (tracking, testing and treatment) was adopted to curb the disease in the very early phase of the spread of the disease in India. Local and global asymptotic stability is established for endemic equilibrium. Extensive numerical simulations with real parametric values are performed to validate the analytical results. Trend analysis of fatality rate, infection rate, and impact of lockdown is performed for USA, European countries, Russia, Iran, China, Japan, S. Korea with a comparative assessment by India. Kruskal - Wallis test is performed to test the null hypothesis for infected cases during the four lockdown phases in India. It has been observed that there is a significant difference at both 95% and 99% confidence interval in the infected cases, recovered cases and the case fatality rate during all the four phases of the lockdown.

Maternal effects and the outcome of interspecific competition

Maternal environmental effects create lagged population responses to past environments. Although they are ubiquitous and vary in expression across taxa, it remains unclear if and how their presence alters competitive interactions in ecological communities.Here, we use a discrete-time competition model to simulate how maternal effects alter competitive dynamics in fluctuating and constant environments. Further, we explore how omitting maternal effects alter estimates of known model parameters from observational time series data.Our simulations demonstrate that (i) maternal effects change competitive outcomes, regardless of whether competitors otherwise interact neutrally or exhibit non-neutral competitive differences, (ii) the consequences of maternal effects for competitive outcomes are mediated by the temporal structure of environmental variation, (iii) even in constant conditions, competitive outcomes are influenced by species' maternal effects strategies, and (iv) in observational time series data, omitting maternal effects reduces variation explained by models and biases parameter estimates, including competition coefficients.Our findings demonstrate that the ecological consequences of maternal effects hinge on the competitive environment. Evolutionary biologists have long recognized that maternal effects can be an important but often overlooked strategy buffering populations from environmental change. We suggest that maternal effects are similarly critical to ecology and call for research into maternal effects as drivers of dynamics in populations and communities.

Count Data Time Series Modelling in Julia-The CountTimeSeries.jl Package and Applications

A new software package for the Julia language, CountTimeSeries.jl, is under review, which provides likelihood based methods for integer-valued time series. The package’s functionalities are showcased in a simulation study on finite sample properties of Maximum Likelihood (ML) estimation and three real-life data applications. First, the number of newly infected COVID-19 patients is predicted. Then, previous findings on the need for overdispersion and zero inflation are reviewed in an application on animal submissions in New Zealand. Further, information criteria are used for model selection to investigate patterns in corporate insolvencies in Rhineland-Palatinate. Theoretical background and implementation details are described, and complete code for all applications is provided online. The CountTimeSeries package is available at the general Julia package registry.

Forecasting the Tuberculosis Incidence Using a Novel Ensemble Empirical Mode Decomposition-Based Data-Driven Hybrid Model in Tibet, China

Objective

The purpose of this study is to develop a novel data-driven hybrid model by fusing ensemble empirical mode decomposition (EEMD), seasonal autoregressive integrated moving average (SARIMA), with nonlinear autoregressive artificial neural network (NARNN), called EEMD-ARIMA-NARNN model, to assess and forecast the epidemic patterns of TB in Tibet.

Methods

The TB incidence from January 2006 to December 2017 was obtained, and then the time series was partitioned into training subsamples (from January 2006 to December 2016) and testing subsamples (from January to December 2017). Among them, the training set was used to develop the EEMD-SARIMA-NARNN combined model, whereas the testing set was used to validate the forecasting performance of the model. Whilst the forecasting accuracy level of this novel method was compared with the basic SARIMA model, basic NARNN model, error-trend-seasonal (ETS) model, and traditional SARIMA-NARNN mixture model.

Results

By comparing the accuracy level of the forecasting measurements including root-mean-square error, mean absolute deviation, mean error rate, mean absolute percentage error, and root-mean-square percentage error, it was shown that the EEMD-SARIMA-NARNN combined method produced lower error rates than the others. The descriptive statistics suggested that TB was a seasonal disease, peaking in late winter and early spring and a trough in autumn and early winter, and the TB epidemic indicated a drastic increase by a factor of 1.7 from 2006 to 2017 in Tibet, with average annual percentage change of 5.8 (95% confidence intervals: 3.5–8.1).

Conclusion

This novel data-driven hybrid method can better consider both linear and nonlinear components in the TB incidence than the others used in this study, which is of great help to estimate and forecast the future epidemic trends of TB in Tibet. Besides, under present trends, strict precautionary measures are required to reduce the spread of TB in Tibet.

High coverage COVID-19 mRNA vaccination rapidly controls SARS-CoV-2 transmission in Long-Term Care Facilities

Residents of Long-Term Care Facilities (LTCFs) represent a major share of COVID-19 deaths worldwide. Measuring the vaccine effectiveness among the most vulnerable in these settings is essential to monitor and improve mitigation strategies. We evaluated the early effect of the administration of BNT162b2 mRNA vaccines to individuals older than 64 years residing in LTCFs in Catalonia, a region of Spain. We monitored all the SARS-CoV-2 documented infections and deaths among LTCFs residents from February 6th to March 28th, 2021, the subsequent time period after which 70% of them were fully vaccinated. We developed a modeling framework based on the relation between community and LTFCs transmission during the pre-vaccination period (July -December 2020) and compared the true observations with the counterfactual model predictions. As a measure of vaccine effectiveness, we computed the total reduction in SARS-CoV-2 documented infections and deaths among residents of LTCFs over time, as well as the reduction on the detected transmission for all the LTCFs. We estimated that once more than 70% of the LTCFs population were fully vaccinated, 74% (58%-81%, 90% CI) of COVID-19 deaths and 75% (36%-86%, 90% CI) of all expected documented infections among LTCFs residents were prevented. Further, detectable transmission among LTCFs residents was reduced up to 90% (76-93%, 90%CI) relative to that expected given transmission in the community. Our findings provide evidence that high-coverage vaccination is the most effective intervention to prevent SARS-CoV-2 transmission and death among LTCFs residents. Conditional on key factors such as vaccine roll out, escape and coverage --across age groups--, widespread vaccination could be a feasible avenue to control the COVID-19 pandemic.

Use of Composites in Analysis of Individual Time Series: Implications for Person-Specific Dynamic Parameters

Psychology has seen an increase in the study of intra-individual processes through time series. Although timely and relevant, modeling variance from unique sources in time series may lead to model non-convergence, making it tempting to reduce the number of parameters in the model. The purpose of this paper was to investigate use of composite scores in analysis of individual time series. To meet this goal, we compared the dynamic factor analysis (DFA) model using multiple indicators with the autoregressive (AR) and autoregressive + white noise (AR + WN) models using composites. We conducted a Monte Carlo study in which a DFA(1,1) model was used to generate the data with varying conditions of size of factor loadings, number of indicators, size of AR parameters, and time series length. We also conducted analysis of empirical data from six individuals' daily self-reports of mood. Findings indicated that the DFA(1,1) and AR(1) + WN models performed comparably in their recovery of AR parameters, while the AR(1) model underestimated the parameter under nearly all conditions. However, variability of estimates may make the AR(1) + WN model less viable for researchers conducting individual-level analyses when the true data-generating mechanism is the DFA(1, 1) model.

Detecting Task-Dependent Functional Connectivity in Group Iterative Multiple Model Estimation with Person-Specific Hemodynamic Response Functions

Introduction: Group iterative multiple model estimation (GIMME) has proven to be a reliable data-driven method to arrive at functional connectivity maps that represent associations between brain regions across time in groups and individuals. However, to date, GIMME has not been able to model time-varying task-related effects. This article introduces HRF-GIMME, an extension of GIMME that enables the modeling of the direct and modulatory effects of a task on functional magnetic resonance imaging data collected by using event-related designs. Critically, hemodynamic response function (HRF)-GIMME incorporates person-specific modeling of the HRF to accommodate known variability in onset delay and shape. Methods: After an introduction of the technical aspects of HRF-GIMME, the performance of HRF-GIMME is evaluated via both a simulation study and application to empirical data. The simulation study assesses the sensitivity and specificity of HRF-GIMME by using data simulated from one slow and two rapid event-related designs, and HRF-GIMME is then applied to two empirical data sets from similar designs to evaluate performance in recovering known neural circuitry. Results: HRF-GIMME showed high sensitivity and specificity across all simulated conditions, and it performed well in the recovery of expected relations between convolved task vectors and brain regions in both simulated and empirical data, particularly for the slow event-related design. Conclusion: Results from simulated and empirical data indicate that HRF-GIMME is a powerful new tool for obtaining directed functional connectivity maps of intrinsic and task-related connections that is able to uncover what is common across the sample as well as crucial individual-level path connections and estimates. Impact statement Group iterative multiple model estimation (GIMME) is a reliable method for creating functional connectivity maps of the connections between brain regions across time, and it is able to detect what is common across the sample and what is shared between subsets of participants, as well as individual-level path estimates. However, historically, GIMME does not model task-related effects. The novel HRF-GIMME algorithm enables the modeling of direct and modulatory task effects through individual-level estimation of the hemodynamic response function (HRF), presenting a powerful new tool for assessing task effects on functional connectivity networks in functional magnetic resonance imaging data.

Correlation between Acinetobacter baumannii Resistance and Hospital Use of Meropenem, Cefepime, and Ciprofloxacin: Time Series Analysis and Dynamic Regression Models

Acinetobacter baumannii is one of the most difficult-to-treat pathogens worldwide, due to developed resistance. The aim of this study was to evaluate the use of widely prescribed antimicrobials and the respective resistance rates of A. baumannii, and to explore the relationship between antimicrobial use and the emergence of A. baumannii resistance in a tertiary care hospital. Monthly data on A. baumannii susceptibility rates and antimicrobial use, between January 2014 and December 2017, were analyzed using time series analysis (Autoregressive Integrated Moving Average (ARIMA) models) and dynamic regression models. Temporal correlations between meropenem, cefepime, and ciprofloxacin use and the corresponding rates of A. baumannii resistance were documented. The results of ARIMA models showed statistically significant correlation between meropenem use and the detection rate of meropenem-resistant A. baumannii with a lag of two months (p = 0.024). A positive association, with one month lag, was identified between cefepime use and cefepime-resistant A. baumannii (p = 0.028), as well as between ciprofloxacin use and its resistance (p < 0.001). The dynamic regression models offered explanation of variance for the resistance rates (R² > 0.60). The magnitude of the effect on resistance for each antimicrobial agent differed significantly.

On Time Scales of Intrinsic Oscillations in the Climate System

Proxy temperature data records featuring local time series, regional averages from areas all around the globe, as well as global averages, are analyzed using the Slow Feature Analysis (SFA) method. As explained in the paper, SFA is much more effective than the traditional Fourier analysis in identifying slow-varying (low-frequency) signals in data sets of a limited length. We find the existence of a striking gap from ~1000 to about ~20,000 years, which separates intrinsic climatic oscillations with periods ranging from ~60 years to ~1000 years, from the longer time-scale periodicities (20,000 year+) involving external forcing associated with Milankovitch cycles. The absence of natural oscillations with periods within the gap is consistent with cumulative evidence based on past data analyses, as well as with earlier theoretical and modeling studies.

Short-Term Associations of Ambient Fine Particulate Matter (PM2.5) with All-Cause Hospital Admissions and Total Charges in 12 Japanese Cities

The short-term association between ambient particulate matter ≤2.5 microns in diameter (PM_2.5) and hospital admissions is not fully understood. Studies of this association with hospital admission costs are also scarce, especially in entire hospitalized populations. We examined the association between ambient PM_2.5 and all-cause hospital admissions, the corresponding total charges, and the total charges per patient by analyzing the hospital admission data of 2 years from 628 hospitals in 12 cities in Japan. We used generalized additive models with quasi-Poisson regression for hospital admissions and generalized additive models with log-linear regression for total charges and total charges per patient. We first estimated city-specific results and the combined results by random-effect models. A total of 2,017,750 hospital admissions were identified. A 10 µg/m³ increase in the 2 day moving average was associated with a 0.56% (95% CI: 0.14–0.99%) increase in all-cause hospital admissions and a 1.17% (95% CI: 0.44–1.90%) increase in total charges, and a 10 µg/m³ increase in the prior 2 days was associated with a 0.75% (95% CI: 0.34–1.16%) increase in total charges per patient. Short-term exposure to ambient PM_2.5 was associated with increased all-cause hospital admissions, total charges, and total charges per patient.

Dynamic Time-Warping Correction for Shifts in Ultrahigh Resolving Power Ion Mobility Spectrometry and Structures for Lossless Ion Manipulations

Detection of arrival time shifts between ion mobility spectrometry (IMS) separations can limit achievable resolving power (Rp), particularly when multiple separations are summed or averaged, as commonly practiced in IMS. Such variations can be apparent in higher Rp measurements and are particularly evident in long path length traveling wave structures for lossless ion manipulations (SLIM) IMS due to their typically much longer separation times. Here, we explore data processing approaches employing single value alignment (SVA) and nonlinear dynamic time warping (DTW) to correct for variations between IMS separations, such as due to pressure fluctuations, to enable more effective spectrum summation for improving Rp and detection of low-intensity species. For multipass SLIM IMS separations, where narrow mobility range measurements have arrival times that can extend to several seconds, the SVA approach effectively corrected for such variations and significantly improved Rp for summed separations. However, SVA was much less effective for broad mobility range separations, such as obtained with multilevel SLIM IMS. Changes in ions' arrival times were observed to be correlated with small pressure changes, with approximately 0.6% relative arrival time shifts being common, sufficient to result in a loss of Rp for summed separations. Comparison of the approaches showed that DTW alignment performed similarly to SVA when used over a narrow mobility range but was significantly better (providing narrower peaks and higher signal intensities) for wide mobility range data. We found that the DTW approach increased Rp by as much as 115% for measurements in which 50 IMS separations over 2 s were summed. We conclude that DTW is superior to SVA for ultra-high-resolution broad mobility range SLIM IMS separations and leads to a large improvement in effective Rp, correcting for ion arrival time shifts regardless of the cause, as well as improving the detectability of low-abundance species. Our tool is publicly available for use with universal ion mobility format (.UIMF) and text (.txt) files.

Predicting the Dynamics of the COVID-19 Pandemic in the United States Using Graph Theory-Based Neural Networks

The COVID-19 pandemic has had unprecedented social and economic consequences in the United States. Therefore, accurately predicting the dynamics of the pandemic can be very beneficial. Two main elements required for developing reliable predictions include: (1) a predictive model and (2) an indicator of the current condition and status of the pandemic. As a pandemic indicator, we used the effective reproduction number (Rt), which is defined as the number of new infections transmitted by a single contagious individual in a population that may no longer be fully susceptible. To bring the pandemic under control, Rt must be less than one. To eliminate the pandemic, Rt should be close to zero. Therefore, this value may serve as a strong indicator of the current status of the pandemic. For a predictive model, we used graph neural networks (GNNs), a method that combines graphical analysis with the structure of neural networks. We developed two types of GNN models, including: (1) graph-theory-based neural networks (GTNN) and (2) neighborhood-based neural networks (NGNN). The nodes in both graphs indicated individual states in the United States. While the GTNN model's edges document functional connectivity between states, those in the NGNN model link neighboring states to one another. We trained both models with Rt numbers collected over the previous four days and asked them to predict the following day for all states in the United States. The performance of these models was evaluated with the datasets that included Rt values reflecting conditions from 22 January through 26 November 2020 (before the start of COVID-19 vaccination in the United States). To determine the efficiency, we compared the results of two models with each other and with those generated by a baseline Long short-term memory (LSTM) model. The results indicated that the GTNN model outperformed both the NGNN and LSTM models for predicting Rt.

Associations between Meteorological Factors and Reported Mumps Cases from 1999 to 2020 in Japan

The present study investigated associations between epidemiological mumps patterns and meteorological factors in Japan. We used mumps surveillance data and meteorological data from all 47 prefectures of Japan from 1999 to 2020. A time-series analysis incorporating spectral analysis and the least-squares method was adopted. In all power spectral densities for the 47 prefectures, spectral lines were observed at frequency positions corresponding to 1-year and 6-month cycles. Optimum least-squares fitting (LSF) curves calculated with the 1-year and 6-month cycles explained the underlying variation in the mumps data. The LSF curves reproduced bimodal and unimodal cycles that are clearly observed in northern and southern Japan, respectively. In investigating factors associated with the seasonality of mumps epidemics, we defined the contribution ratios of a 1-year cycle (Q₁) and 6-month cycle (Q₂) as the contributions of amplitudes of 1-year and 6-month cycles, respectively, to the entire amplitude of the time series data. Q₁ and Q₂ were significantly correlated with annual mean temperature. The vaccine coverage rate of a measles-mumps-rubella vaccine might not have affected the 1-year and 6-month modes of the time series data. The results of the study suggest an association between mean temperature and mumps epidemics in Japan.

CODEX, a neural network approach to explore signaling dynamics landscapes

Current studies of cell signaling dynamics that use live cell fluorescent biosensors routinely yield thousands of single-cell, heterogeneous, multi-dimensional trajectories. Typically, the extraction of relevant information from time series data relies on predefined, human-interpretable features. Without a priori knowledge of the system, the predefined features may fail to cover the entire spectrum of dynamics. Here we present CODEX, a data-driven approach based on convolutional neural networks (CNNs) that identifies patterns in time series. It does not require a priori information about the biological system and the insights into the data are built through explanations of the CNNs' predictions. CODEX provides several views of the data: visualization of all the single-cell trajectories in a low-dimensional space, identification of prototypic trajectories, and extraction of distinctive motifs. We demonstrate how CODEX can provide new insights into ERK and Akt signaling in response to various growth factors, and we recapitulate findings in p53 and TGFβ-SMAD2 signaling.

Primary coronary intervention in ST-elevation myocardial infarction

Összefoglaló. Bevezetés: Heveny myocardialis infarctusban a szívizommentés sikere, a beteg életkilátása nagymértékben függ a panasz kezdete és az elzáródott koszorúér rekanalizálása között eltelt ischaemiás időtől. Jelenleg az ér nyitása optimális esetben minden betegnél koszorúér-intervencióval történik. Célkitűzés: Annak vizsgálata, hogy öt év alatt mennyit változtak az ischaemiás idő összetevői, és miben változott az elzáródott ér nyitásának módszere ST-elevációs myocardialis infarctus (STEMI) miatt végzett primer coronariaintervencióban. Módszer: 2014. 01. 01. és 2018. 12. 31. között 1663, STEMI miatt koszorúér-intervencióval kezelt betegnél (1173 férfi és 490 nő) vizsgáltuk évenkénti bontásban a panasztól a koszorúér nyitásáig eltelt idő összetevőit és a 30 napos halálozást. Eredmények: Öt év alatt a panasztól az első egészségügyi kontaktusig medián 2:53 vs. 2:10 óra (p = 0,0132), ettől az intervenciós centrumba történt felvételig medián 1:17 vs. 1:03 óra (p = 0,009), a felvételtől a ballon nyitásáig medián 0:31 vs. 0:29 óra (p = ns) telt el. A panasztól a ballon nyitásáig eltelt idő (medián 5:29 vs. 4:07 óra, p = 0,0001) rövidült, döntően 2014 és 2015 között. A gyógyszerkibocsátó stent beültetése 15%-ról 96%-ra nőtt. A vizsgált években a légzés/keringés támogatás aránya 8,2-10,6-13,9-7,6-8,4, a 30 napos halálozásé 4,1-6,8-11,1-7,4-5,7% volt; a két érték korrelációt mutat (p = 0,827). Következtetés: Öt év alatt a panasztól az első egészségügyi kontaktusig és a kórházi beszállításig eltelt idő rövidült, de az Európai Kardiológiai Társaság ajánlásához képest hosszú; a kórházi felvételtől a ballon nyitásáig eltelt idő megfelelő. A négy órán belüli reperfúzió a betegek közel felében valósult meg. Az intervenciós centrumba való gyorsabb bekerülés javíthatna az eredményen. Orv Hetil. 2021; 162(13): 497-503.

SUMMARY

INTRODUCTION

In acute myocardial infarction, the heart muscle salvage, the patient's life expectancy is highly dependent on the elapsed ischaemic time from the onset of complaint to target vessel recanalisation. Nowadays, target vessel recanalisation is performed with coronary intervention in all patients in optimal case.

OBJECTIVE

To examine how the components of ischemic time and the opening procedure of the occluded coronary have changed over five years in primary intervention done in acute ST-elevation myocardial infarction (STEMI).

METHOD

Authors studied data of 1663 (1173 male and 480 female) STEMI patients in annual breakdowns treated with coronary intervention between 01. 01. 2014 and 31. 12. 2018, time from complaint to coronary artery opening, details of intervention and 30 days mortality rate.

RESULTS

During the five years, time intervals were as follows: from onset of complaint to first medical contact: median 2:53 vs. 2:10 hours (p = 0.0132), from this to admission in the interventional centre: median 1:17 vs. 1:03 hours (p = 0.009), from hospital admission to balloon opening: median 0:31 vs. 0:29 hours (p = ns). In total, the complaint to balloon opening time (median 5:29 vs. 4:07 hours, p = 0.0001) diminished, decisively from 2014 to 2015. Ratio of drug-eluting stent implantation increased from 15% to 96%. In the investigated years, the need of respiratory and/or circulatory device support ratio was 8.2-10.6-13.9-7.6-8.4, 30-day mortality rate between 4.1-6.8-11.1-7.4-5.7%; these two values showed a correlation (p = 0.827).

CONCLUSION

The time from complaint to first medical contact and transfer to hospital against the significant decrease is still longer than the recommendation of the European Society of Cardiology. The time from hospital admission to balloon opening is adequate. Reperfusion within four hours was achieved in half of the patients in total. Faster hospitalization may improve results. Orv Hetil. 2021; 162(13): 497-503.

Can Japan Achieve Zero Transmission of HIV? Time Series Analysis Using Bayesian Local Linear Trend Model

BACKGROUND

The number of newly diagnosed human immunodeficiency virus (HIV) infections and acquired immune deficiency syndrome (AIDS) patients in Japan appears to be decreasing. However, whether these new infections cease to occur in the future in Japan, similar to abroad, is unclear. To evaluate the feasibility of this achievement, we conducted a time series analysis using Bayesian local linear trend model to evaluate the possibility of zero new infection of HIV/AIDS in Japan.

METHODS

We used quarterly data on HIV/AIDS from the first quarter, 2001 to the second quarter, 2020. Bayesian analyses were conducted using Markov chain Monte Carlo (MCMC) method, and a local linear trend model was constructed for number of newly diagnosed HIV infection without AIDS diagnosis, AIDS cases, and their aggregate. Predictions for the following 60 quarters until the second quarter of 2035 were also made for all models.

RESULTS

The mean aggregate cases of HIV/AIDS patients became 0 by the fourth quarter of 2031 (90% credible interval 0-535). For HIV infections alone, mean cases became 0 by the second quarter of 2030 (90%CrI 0-472). For AIDS alone mean cases were 9 at the second quarter of 2035 (90%CrI 0-231).

CONCLUSION

Our local linear trend model suggested that number of HIV/AIDS cases in Japan could decrease to zero by the first quarter of 2031, if the trend of the infections followed the local linear trend model, yet with rather wide credible interval. Achieving zero new transmission of HIV in Japan is a realistic goal but measures to make it faster may be needed.

ICU Patients' Antibiotic Exposure and Triazole-Resistance in Invasive Candidiasis: Parallel Analysis of Aggregated and Individual Data

Background: The relationship between antibiotic use and the incidence of triazole-resistant phenotypes of invasive candidiasis (IC) in critically ill patients is unclear. Different methodologies on determining this relationship may yield different results.

Methods: A retrospective multicenter observational analysis was conducted to investigate exposure to antibiotics and the incidence of non-duplicate clinical isolates of Candida spp. resistant to fluconazole, voriconazole, or both during November 2013 to April 2018, using two different methodologies: group-level (time-series analysis) and individual-patient-level (regression analysis and propensity-score adjusting).

Results: Of 393 identified Candida spp. from 388 critically ill patients, there were three phenotypes of IC identified: fluconazole-resistance (FR, 63, 16.0%); voriconazole-resistance (VR, 46, 11.7%); and cross-resistance between fluconazole and voriconazole (CR, 32, 8.1%). Exposure to several antibacterial agents with activity against the anaerobic gastrointestinal flora, especially third-generation cefalosporins (mainly cefoperazone/sulbactam and ceftriaxone), but not triazoles, have an immediate effect (time lag = 0) on subsequent ICU-acquired triazole-resistant IC in the group-level (p < 0.05). When the same patient database was analyzed at the individual-patient-level, we found that exposure to many antifungal agents was significantly associated with triazole-resistance (fluconazole [adjusted odds ratio (aOR) = 2.73] or caspofungin [aOR = 11.32] on FR, voriconazole [aOR = 2.87] on CR). Compared to the mono-triazole-resistant phenotype, CR IC has worse clinical outcomes (14-days mortality) and a higher level of resistance.

Conclusion: Group-level and individual-patient-level analyses of antibiotic-use-versus-resistance relations yielded distinct but valuable results. Antibacterials with antianaerobic activity and antifungals might have “indirect” and “direct” effect on triazole-resistant IC, respectively.

Incidence and Death Rates from COVID-19 Are Not Always Coupled: An Analysis of Temporal Data on Local, Federal, and National Levels

SARS-CoV-2 has caused a deadly pandemic worldwide, placing a burden on local health care systems and economies. Infection rates with SARS-CoV-2 and the related mortality of COVID-19 are not equal among countries or even neighboring regions. Based on data from official German health authorities since the beginning of the pandemic, we developed a case-fatality prediction model that correctly predicts COVID-19-related death rates based on local geographical developments of infection rates in Germany, Bavaria, and a local community district city within Upper Bavaria. Our data point towards the proposal that local individual infection thresholds, when reached, could lead to increasing mortality. Restrictive measures to minimize the spread of the virus could be applied locally based on the risk of reaching the individual threshold. Being able to predict the necessity for increasing hospitalization of COVID-19 patients could help local health care authorities to prepare for increasing patient numbers.

Network Analysis of Cross-Correlations on Forex Market during Crises. Globalisation on Forex Market

Within the paper, the problem of globalisation during financial crises is analysed. The research is based on the Forex exchange rates. In the analysis, the power law classification scheme (PLCS) is used. The study shows that during crises cross-correlations increase resulting in significant growth of cliques, and also the ranks of nodes on the converging time series network are growing. This suggests that the crises expose the globalisation processes, which can be verified by the proposed analysis.

Testing Jump-Diffusion in Epileptic Brain Dynamics: Impact of Daily Rhythms

Stochastic approaches to complex dynamical systems have recently provided broader insights into spatial-temporal aspects of epileptic brain dynamics. Stochastic qualifiers based on higher-order Kramers-Moyal coefficients derived directly from time series data indicate improved differentiability between physiological and pathophysiological brain dynamics. It remains unclear, however, to what extent stochastic qualifiers of brain dynamics are affected by other endogenous and/or exogenous influencing factors. Addressing this issue, we investigate multi-day, multi-channel electroencephalographic recordings from a subject with epilepsy. We apply a recently proposed criterion to differentiate between Langevin-type and jump-diffusion processes and observe the type of process most qualified to describe brain dynamics to change with time. Stochastic qualifiers of brain dynamics are strongly affected by endogenous and exogenous rhythms acting on various time scales—ranging from hours to days. Such influences would need to be taken into account when constructing evolution equations for the epileptic brain or other complex dynamical systems subject to external forcings.

Application of long-term collected data for conservation: Spatio-temporal patterns of mortality in Japanese serow

Monitoring the mortality of wildlife provides basic demographic information to support management plan preparation. The utility of mortality records for conservation measures was investigated in the Japanese serow, focusing on temporal trends and spatial distribution. Using the mortality records of Japanese serow from 2006 to 2018 in Gifu prefecture, cause-specific mortality was categorized into five groups (disease, accident, vehicle collision, parapoxvirus infection, and unknown), and the sex ratios were examined. A state space model was used to analyze the time series for the monthly mortalities, and kernel estimation was used for the spatial distribution of the parapoxvirus infection. Land cover type around the detection point was also reported. Disease, accident, and vehicle collision mortality were similar, and 30% of mortality was of anthropogenic origin. The number of mortality records for males was higher, and the larger home range of males could account for this. The state space model showed moderate increases in monthly mortalities over time and a seasonal variation with the highest level in spring and lowest in winter. Land cover analysis demonstrated a temporal increase in the proportion of human settlement areas, suggesting the change of the Japanese serow habitat. The proximity of Japanese serow to human settlements contributed to increase in mortality records. The point pattern analysis indicated spatial clustering for parapoxvirus infection in the area where an epidemic had occurred in the past. Several measures are recommended; however, mortality records can help develop improved conservation plan.