Model Estimates Research Articles

A spatially explicit assessment of factors shaping attitudes toward African elephant conservation.

Conservation plans that explicitly account for the social landscape where people and wildlife co-occur can yield more effective and equitable conservation practices and outcomes. Yet, social data remain underutilized, often because social data are treated as aspatial or are analyzed with approaches that do not quantify uncertainty or address bias in self-reported data. We conducted a survey (questionnaires) of 177 households in a multiuse landscape in the Kenya-Tanzania borderlands. In a mixed-methods approach, we used Bayesian hierarchical models to quantify and map local attitudes toward African elephant (Loxodonta africana) conservation while accounting for response bias and then combined inference from attitude models with thematic analysis of open-ended responses and cointerpretation of results with local communities to gain deeper understanding of what explains attitudes of people living with wildlife. Model estimates showed that believing elephants have sociocultural value increased the probability of respondents holding positive attitudes toward elephant conservation in general (mean increase=0.31 [95% credible interval, CrI, 0.02-0.67]), but experiencing negative impacts from any wildlife species lowered the probability of respondents holding a positive attitude toward local elephant conservation (mean decrease=-0.20 [95% CrI -0.42 to 0.03]). Qualitative data revealed that safety and well-being concerns related to the perceived threats that elephants pose to human lives and livelihoods, and limited incentives to support conservation on community and private lands lowered positive local attitude probabilities and contributed to negative perceptions of human-elephant coexistence. Our spatially explicit modeling approach revealed fine-scale variation in drivers of conservation attitudes that can inform targeted conservation planning. Our results suggest that approaches focused on sustaining existing sociocultural values and relationships with wildlife, investing in well-being, and implementing species-agnostic approaches to wildlife impact mitigation could improve conservation outcomes in shared landscapes.

Energy Transfer by Mechanical Alloying and Electrocatalytic Performance of the As‐Sintered Self‐Supported High‐Entropy Alloy FeNiCoCuMo

Efficient, stable, and cost‐effective electrocatalysts for the oxygen evolution reaction (OER) are essential for clean energy generation through water splitting. This study presents a FeNiCoCuMo high‐entropy alloy (HEA) synthesized via mechanical alloying (MA) and sintering using hot‐pressing. The energy transfer from the milling process influences phase transformations, with the High‐Entropy Alloys Prediction Software (HEAPS) predicting the formation of an FCC phase. X‐ray diffraction (XRD) and scanning electron microscopy reveal an FCC phase after 150 h of milling, with no elemental segregation observed. The Burgio kinetic model estimates 448 kJ mol−1 is needed to achieve a 99% FCC phase. The crystallite size is 4 nm, with a lattice parameter of 0.371 nm. The as‐milled phases were preserved during hot‐pressing sintering. Electrodes (M1, M2, M3) fabricated from the HEA demonstrated high electrocatalytic efficiency, with an average overpotential of 380 mV and Tafel slope of 77 mV dec−1. At a current density of 10 mA cm−2, the electrodes maintained operation for up to 100 h. The synergy between constituent elements is key to the superior electrocatalytic performance of FeNiCoCuMo HEAs, demonstrating their potential as promising materials for OER electrocatalysis.

Wrapping input–output multipliers in confidence intervals

Input–output multipliers are typically calculated as point estimates of the Leontief quantity model. From the previous literature, they can also be estimated (and their confidence intervals) directly from establishments’/industries’ inputs and outputs data by running an appropriate econometric regression. However, previous contributions relied on analyses carried out for 1 single year, one country/region and a reduced number of degrees of freedom. This paper tests the robustness of the previous literature by using instead a time series of large-scale inter-country supply, use and input–output database with more than one country and more than 1 year, as well as panel data econometrics.

Economic Growth and Democratic Political System in Developing Countries

The relationship between a democratic political system and economic growth has been the focus of debate in recent years. This article is a contribution to the economic analysis of the relationship between democracy and economic growth. Using an econometric illustration of panel data from twenty developing countries, in a period from 2000 to 2021, Fixed-effect, random-effect and GMM model estimates show that a democratic political regime positively affects economic growth in developing countries. This result is explained by the importance of the role of democracy in implementing economic and social reforms, encouraging investment, opening up trade and protecting civil liberties and political powers.

The economic impacts of living habitat changes in the Virginia Middle Peninsula, Chesapeake Bay

Living habitats support ecologically and commercially important species but are threatened by anthropogenic stressors, including climate change. The populations of two living habitats in the Chesapeake Bay, Eastern Oyster (Crassostrea virginica) and Eelgrass (Zostera marina), are depleted compared to historical levels, but recent Oyster restoration efforts have created new reef habitat and enhanced existing Oyster reefs. Other animals (e.g., commercially important fish and invertebrates) consume organisms that associate with these living habitats, suggesting the regional economies that rely on the commercial fishing industry may be impacted by changes in living habitat coverage. Ecosystem models were developed for two rivers that have undergone Oyster restoration in the Virginia Middle Peninsula, and simulations were conducted to estimate the potential influence that changes to living habitats may have on commercial fisheries harvests. Additionally, commercial fishers were interviewed to document expenditures needed to link ecological model estimates to an economic input-output model via IMPLAN that estimated the regional economic impacts of these changes. Increases to Oyster and Eelgrass populations are predicted to enhance commercial fisheries populations and in turn, harvests, while decreases to living habitats are predicted to have negative effect on fisheries harvests. The driving factor behind landed value trends is changes with the lucrative Blue Crab (Callinectes sapidus) fishery. The primary expenses of watermen are repairs/maintenance, fuel, and bait, but greater than 37 % of the annual revenue is retained as proprietor and crew income. Oyster reef restoration alone is predicted to increase the regional economic impact of the commercial fishing industry by $1.1 M yr-1 and support 12 more full-time jobs at the end of a 20-year simulation. Oyster restoration in combination with a continued decline of Eelgrass led to a negligible increase (∼$150 K yr-1) in the regional economic impact. Eelgrass restoration to the current management goal without the influence of Oyster restoration is anticipated to elevate the regional economic impact to $16.8 M yr-1 and 152 full-time jobs, and smaller increases to the Eelgrass population, relative to current levels, will still have a positive impact on the regional economy. Combined Oyster and Eelgrass restoration are predicted to enhance economic impacts to the greatest extent, while continued Eelgrass decline is likely to minimize these benefits.

Comparison between estimates of beta regression model using MSE

The multiple linear regression analysis shows the effects of explanatory variables on the dependent variable. Its function is to represent data to understand the shape and nature of the relationship between explanatory variables and the response variable. One of the main problems faced by linear regression is the nature of the data, as sometimes it does not follow a normal distribution. This has led to the emergence of different types of linear regression models such as the beta regression model, where the data of the dependent variable is less than one and confined within the period (0,1), and the data is relative and decimal. Non-traditional methods will be used to estimate beta regression parameters because some traditional methods do not provide suitable and accurate results when dealing with one of the main problems encountered in regression, which is multicollinearity. To address this issue, we used some methods include The Maximum Likelihood Estimation (MLE), ridge regression, modified Liu, and Ozkale and Kaciranlar method. Shrinking parameters are proposed for each method, and a comparison between the methods is conducted using the Mean Squared Error comparison criterion through a simulation experiment with two approaches, the simulation results showed that the ridge regression method is the best for the first and second methods of simulation. Paper type Research Paper

Uncertainty in model estimates of global groundwater depth

Abstract Knowing the depth at which groundwater can be found below the land surface is critical for understanding its potential accessibility by ecosystems and society. Uncertainty in global scale water table depth (WTD) limits our ability to assess groundwater’s role in a water cycle altered by changing climate, land cover, and human water use. Global groundwater models offer a top–down pathway to gain this knowledge, but their uncertainty is currently poorly quantified. Here, we investigate four global groundwater models and reveal steady-state WTD disagreements of more than 100 m for one-third of the global land area. We find that model estimates of land areas with shallow groundwater at <10 m depth vary from 10% to 71% (mean of 23%). This uncertainty directly translates into subsequent assessments, as land areas with potential groundwater accessibility for forests, population, and areas equipped for irrigation, differ substantially depending on the chosen model. We explore reasons for these differences and find that contrary to observations, 3 out of 4 models show deeper water tables in humid than in arid climates and greatly overestimate how strongly topographic slope controls WTD. These results highlight substantial uncertainty associated with any global-scale groundwater analysis, which should be considered and ultimately reduced.

Riverine macroplastic survey along the segments of Tullahan River in Metro Manila, Philippines

The Philippines is one of the world’s main contributors to global marine plastic pollution. However, field data remains scarce, so model estimates of riverine plastic pollution may be inaccurate. This paper aims to characterize the macrolitter observed along the Tullahan River, with focus on plastics, passing through the barangays of Quezon City and Valenzuela City in Metro Manila. The impact of solid waste management and land use activities on plastic flux were also investigated. For the floating litter, visual counting and float method were used to determine the plastic flux and river velocity, respectively. Riverbank litter was collected manually to characterize it based on plastic product and polymer type. Results show that the macroplastic flux was lowest on the site where residential houses are farthest from the stream. Waste characterization revealed that riverbank litter was primarily plastic – 30%–41% were residuals, and 5%–21% were recyclables. Both methods revealed that wrappers and thin PE plastics are the top contributors to riverine macroplastic pollution. Therefore, the entry of macroplastics into the river may be due to land use activities and weak enforcement of existing solid waste management (SWM) policies. The points discussed in the study can help in improving SWM and land use planning. The results can also increase the accuracy of model estimates.

Can Social Technologies Drive Purchases in E-Commerce Live Streaming? An Empirical Study of Broadcasters’ Cognitive and Affective Social Call-to-Actions

As an emerging social technology, live streaming has facilitated a synchronous and interactive selling setting for e-commerce sellers and consumers. Despite growing operations management (OM) in e-commerce live streaming (ELS), the prior literature has largely neglected the directive social operations of ELS broadcasters, which are crucial to a company's business operations and marketing strategies. To mitigate this gap, we investigated the effects of social call-to-actions (SCTAs), an ELS-specific directive social operation, on consumer purchase outcomes in ELS. To explicate the mechanisms driving the purchase effect of SCTAs, we draw upon the notions of cognitive and affective marketing appeals to categorize SCTAs into two types: cognitive and affective SCTAs. We measure broadcasters’ SCTAs from ELS speech-to-text data using text-mining techniques and adopt econometric model estimations via an instrumental variables identification approach to quantify the relationships between ELS broadcasters’ SCTAs and consumer purchases. Our results uncover a significant positive purchase effect of affective SCTAs but an insignificant effect of cognitive SCTAs on product-level purchase orders in ELS. Specifically, a one-unit increase in affective SCTA-related words per minute is related to increased purchase orders of a product by 0.43%, i.e., averaging a 1.10% growth in demand per minute. Additionally, we find that the effects of cognitive and affective SCTAs are contingent on product and broadcaster types. Our session-level mediation effect analysis verifies the underlying mechanisms that drive the purchase effects of broadcasters’ SCTAs. Specifically, we find that cognitive SCTAs impede social engagements and thereby purchases, whereas affective SCTAs can boost social engagements, leading to increased session traffic and ultimately product sales in ELS. Our study provides novel empirical findings and important practical implications for ELS platforms and broadcasters.

Are We Dosing Correctly? Population Pharmacokinetics of Cefepime in Critically Ill Pediatric Patients

Abstract Background Dosing guidelines of cefepime are well defined in healthy pediatric and adult populations but have not been extensively studied in critically ill pediatric patients. Patients who are critically ill may have significant alterations in antibiotic pharmacokinetics (PK) and pharmacodynamics (PD) due to many factors. Receipt of ECMO or CRRT may further alter PK/PD due to changes in clearance and volume of distribution and the depletion of plasma proteins. Since critically ill pediatric patients are likely to receive multiple courses of antimicrobials, optimization of drug dosing and delivery is critically important for individual patient outcomes. This study will test the hypothesis that the PK of cefepime is substantially altered in critically ill pediatric patients. Methods We prospectively enrolled patients in VUMC pediatric ICUs receiving cefepime, including those receiving ECMO or CRRT. Plasma samples were collected at opportune timepoints enriched for specific PK/PD metrics. Cefepime plasma concentrations were measured by LC-MS/MS. Weight, dose, interval, and administration time were extracted from the medical record. Monolix was used to fit population PK models using one and two-compartment models with a proportional error model. Covariate modeling was performed with a priori selected covariates: ECMO, CRRT, sex, race, ethnicity, gestational age, and creatinine clearance (CrCl). The final covariate model was used to perform probability of target attainment (PTA) analysis using Monte Carlo simulation. 6,000 subjects were simulated for each dosing regimen, with 3 weight categories (≤9 kg, 9-25 kg, and >25 kg) and 2 CrCl categories (≤90 mL/min and >90 mL/min). PTA was calculated over a grid of possible MIC values based on three targets, 65% fT>MIC, 100% fT>MIC, and 100% fT>4×MIC. Results Preliminary data from 84 pediatric participants receiving cefepime were analyzed. Of these, 9 received CRRT and 10 received ECMO. An average of 3 samples were collected per participant. A two compartment with proportional error model was selected as the base model. Population PK parameter estimates for the base model, base model with weight, and final covariate model were calculated. Inclusion of weight and CrCl significantly improved model fit. CRRT, ethnicity, and gestational age did significantly improve model fit, but their inclusion in the model did not provide evidence of being significantly better than the model with only weight and CrCl. The results from the PTA analysis are seen in Figure 1. We observed an overall ordering of PTA by dosing regimen across all three targets, with q8h 3-hour infusion having the highest PTA and q12h 30-minute infusion having the lowest PTA. Increased CrCl led to reduced PTA and as weight increased, PTA also increased. Conclusion This study will be among the first and largest to characterize cefepime PK in the critically ill pediatric population. Clinicians should incorporate local antibiograms with these population PK models to determine optimal dosing in critically ill pediatric patients. The use of extended infusions may be beneficial for PK/PD target achievement, particularly for resistant pathogens. Since clinical covariates affect PK parameters, this population may also benefit from therapeutic drug monitoring. Figure 1. Comparison of PTA among four dosing regimens of cefepime at targets of 65% fT>MIC, 100% fT>MIC, and 100% fT>4×MIC.

Multiple objectives optimization of injection-moulding process for dashboard using soft computing and particle swarm optimization

This research focuses on utilizing injection moulding to assess defects in plastic products, including sink marks, shrinkage, and warpages. Process parameters, such as pure cooling time, mould temperature, melt temperature, and pressure holding time, are carefully selected for investigation. A full factorial design of experiments is employed to identify optimal settings. These parameters significantly affect the physical and mechanical properties of the final product. Soft computing methods, such as finite element (FE), help mitigate behaviour by considering different input parameters. A CAD model of a dashboard component integrates into an FE simulation to quantify shrinkage, warpage, and sink marks. Four chosen parameters of the injection moulding machine undergo comprehensive experimental design. Decision tree, multilayer perceptron, long short-term memory, and gated recurrent units models are explored for injection moulding process modelling. The best model estimates defects. Multiple objectives particle swarm optimisation extracts optimal process parameters. The proposed method is implemented in MATLAB, providing 18 optimal solutions based on the extracted Pareto-Front.

Time‐lapse inversion of self‐potential data through particle filtering

AbstractIn environmental sciences, comprehending the movement of subsurface contaminants is crucial for formulating effective remediation measures. The self‐potential (SP) method has become a common tool for delineating landfill contamination plumes. Contaminant diffusion or migration represents dynamic processes, with corresponding SP responses evolving over time. However, conventional SP interpretation approaches have predominantly relied on static single‐frame inversion, overlooking the temporal correlation in time‐series SP data and resulting in cumulative errors. To tackle this challenge, we introduce a novel method for time‐lapse inversion of SP data leveraging particle filtering. This approach recursively refines the priori state model through posteriori observations to achieve precise estimations of dynamic models. Specifically, a spherical polarization model is deployed to establish the state equations of underground contaminant diffusion and transport models, whereas the observation model is derived through forward modeling. The proposed method is validated using two synthetic examples and one lab‐measured dataset. The findings demonstrate the efficacy of the time‐lapse inversion algorithm in precisely estimating dynamic models, outperforming static single‐frame inversion based on the particle swarm optimization algorithm. The posteriori distribution of particles approximates a bell‐shaped distribution, with the true state closely positioned near the peak probability. Therefore, we affirm that conducting time‐lapse inversion of time‐series SP data through particle filtering is an effective and dependable approach for accurately estimating dynamic model states.

Natural sinks and sources of CO<sub>2</sub> and CH<sub>4</sub> in the atmosphere of Russian regions and their contribution to climate change in the 21st century evaluated with CMIP6 model ensemble

The natural fluxes of CO2 and CH4 into the atmosphere from the territory of Russia in the 21st century have been analyzed using the results of calculations with the ensemble of global climate models of the international project CMIP6. Estimates of natural CO2 fluxes in Russian regions differ greatly for different models. Their values for the beginning of the 21st century range from –1 to 1 GtC/yr. In the 21st century the differences in model estimates of fluxes grow and at the end of the 21st century in the scenario with the largest anthropogenic impacts SSP5-8.5 range from –2.5 to 2.5 GtC/year. Estimates of natural methane emissions to the atmosphere from the territory of Russia also differ greatly for different models. Modern methane emissions are estimated in the range from 10 to 35 MtCH4/yr, with an increase in the 21st century of up to 300%. Ensemble model calculations show general trends for changes in natural greenhouse gas fluxes. Most CMIP6 ensemble models are characterized by a maximum of CO2 uptake by terrestrial ecosystems and its further reduction by the end of the 21st century, while natural methane emissions to the atmosphere for all models and scenarios of anthropogenic impacts grow throughout the 21st century. The cumulative temperature potential of natural CO2 fluxes on the territory of Russia in the 21st century is estimated, depending on the scenario of anthropogenic impacts, from –0.3 to 0.1 K, and the warming-accelerating impact of natural CH4 emissions is estimated in the range of 0.03-0.09 K.

Association of crop commercialization and rural households' multidimensional poverty using targeted maximum likelihood estimation

Recent evidence shows the highest prevalence of multidimensional poverty in the world. To alleviate poverty, Ethiopia is investing in stimulating agricultural commercialization for poverty reduction, driving sustainable economic growth, and creating jobs. Therefore, this study investigated the causal relationship between crop commercialization and multidimensional poverty across Ethiopian rural households using the Ethiopia Socioeconomic Survey 2018/19 data, which included 1129 crop commercialized and 1585 completely subsistence rural households. Moreover, this study also examined the causal effect of crop commercialization on the multidimensional poverty mediated by mobile phone ownership status. The study used the double robust method of the augmented inverse probability of treatment weighted (AIPTW) and targeted maximum likelihood estimation (TMLE) methods. The result showed that crop commercialization has an association with multidimensional poverty. Specifically, the TMLE with a default super learner model estimates were the most precise and suggest that the prevalence of risk of being multidimensionally poor among rural households who were crop commercialized is 12.2 % lower than for those who were completely subsistence households. Thus, it is important to have more robust policies like crop commercialization to target rural multidimensional poverty reduction. Moreover, the controlled direct effect estimation analyses showed that crop-commercialized rural households reduced the adjusted risk of multidimensional poverty by; 12 % for those who owned mobile phones and 7.8 % for those who did not. To ensure sustainable livelihoods for rural multidimensional poor households, it is necessary to have a multidisciplinary policy intervention to promote crop commercialization through the spread of mobile phones in rural areas.

Does the European Union Start-Up Aid Help Young Farmers to Innovate and to Join Networks?

The endurance and vitality of European agriculture are threatened by the aging of farmers, together with the lack of generational change. The small share of young farmers also impacts on the innovative capacity and competitiveness of the sector. The Common Agricultural Policy of the European Union started addressing the issue long ago by providing financial aid to help young farmers to start up. Over time, the aid increased and packages of measures were set to reinforce the aid and to promote investments, innovations, and networks at the farm level. While the literature focuses on analyzing the effectiveness of the start-up aid in fostering new entries, this paper assumes an original perspective as it seeks to assess whether this aid has actually promoted innovations and networks in the beneficiary farms. The analysis relies on sample data collected in 2021 in Italy and Poland via interviews of 500 farmers under 40 who had entered the sector both with and without the aid. A probit model estimates whether the beneficiaries have a higher probability to innovate and network. Then, the contribution of the aid to the intensity of the adoption of innovations and networks is estimated via an Average Treatment Effect on the Treated model (ATT). Results suggest that the start-up aid increased the adoption of innovations and networks. The ATT indicates that this positive effect holds even after correcting for self-selection bias. By adopting an original perspective, our analysis suggests that the start-up aid for young farmers goes beyond rejuvenating agriculture by fostering innovation at the farm level and by promoting networking, thus enhancing agricultural change. However, farmer behaviors in the two countries are different, suggesting quite complex patterns for the impact of this measure.

Realized Random Graphs, with an Application to the Interbank Network

Abstract We introduce a new inferential methodology for dynamic network models driven by latent state variables. The main idea is to obtain a noisy representation of the state variables dynamics by computing a sequence of cross-sectional estimates of the network model at each point in time. The dynamic modeling of these cross-sectional estimates, that we name realized random graphs, transforms the original nonlinear state-space network model into a linear time-series model that can be easily estimated. Under the assumption of a mixed-membership blockmodel structure, the model parameters and the unobservable state variables can be consistently estimated when both the size of the network and the time-series length are large. By allowing for an extremely rich parameterization of the model in high dimensions, the proposed methodology describes the heterogeneous topology of real-world networks. We corroborate our findings by using this novel framework to estimate and forecast the dynamic common factors driving the evolution of the Italian electronic market of interbank deposits, and we show that the interbank lending rate is a key factor determining the network topology.

Quantifying brain health in acute ischemic stroke through effective reserve.

To quantify brain health using a measure of reserve that incorporates pre-existing pathology. We analyzed two retrospective ischemic stroke cohorts (GASROS and SALVO) with neuroimaging and 90-day modified Rankin Scores (mRS) available. White matter hyperintensity (WMHv), brain, and intracranial volumes were automatically extracted, and brain parenchymal fraction (BPF) calculated. The latent variable effective reserve (eR) was modeled using age, WMHv, and BPF or brain volume in GASROS. Models were compared using Bayes Information Criterion (BIC). The best model's eR estimates were categorized into quartiles and evaluated in SALVO. GASROS included 476 (median age: 65.8; 65.3% male) and SALVO included 43 (median age: 69.2; 62.8% male) patients. Inverse associations between eR and mRS was seen in both models, with brain volume outperforming BPF (path coefficients: -0.67, -0.48, respectively; p < 0.001; |ΔBIC| = 362). Quartile-based eR stratification in SALVO showed a similar inverse trend, with worse outcomes in the low reserve group (mRS≤2 - highest vs lowest quartile: 85/90% vs 59/45% for GASROS/SALVO). Expanding the concept of eR, highlights its clinical translational potential. The strong link between higher eR and better outcomes underscores its value as a protective brain health metric.

Planning for complex inferior vena cava filter retrievals: the implementation and effectiveness of 3D printed models

BackgroundInferior vena cava filter (IVC) retrieval is most often routine but can be challenging with high morbidity in complex cases, especially those with an extended dwelling time. While risk of morbidity in complex retrievals is decreased with advanced filter retrieval techniques, deciding when and which to use these requires detailed pre-procedural planning. The purpose of our study was to evaluate patient-specific 3D printed anatomic IVC filter models for aiding complex IVC filter retrievals.MethodsAll IVC filter retrieval patients between June 2021 and September 2022 at one academic medical hospital were prospectively screened. Nine met criteria for complex retrieval, and their CT images were used to 3D print patient-specific IVC and filter models. Models were used in pre-procedural planning and clinical utility was assessed using the Anatomic Model Utility Likert Questionnaire and estimations of the procedural and fluoroscopy time saved.ResultsThe usage of 3D printed models in pre-procedural planning had high clinical utility based on the Likert questionnaire (Anatomic Model Utility Points 366.7 ± 103.1). Using a model significantly increased confidence in planning (p = 0.03) and modified the treatment plan in seven cases. It also led to cost-efficient use of resources in the procedure suite with estimated reduction in procedure and fluoroscopy time of 29.0 [20.3] (p = 0.003) and 10.2 [6.7] (p = 0.002) minutes, respectively.Conclusion3D printed anatomic models for patients who require complex IVC filter retrieval demonstrated Likert-based high clinical utility and led to estimated reductions of procedural and fluoroscopy time.

Post-pandemic consumption of outpatient antibiotics in Switzerland up to pre-pandemic levels, 2018–2023: An interrupted time series analysis

BackgroundThe COVID-19 pandemic has been a challenge for health-care systems and antibiotic stewards as uncertainty regarding treatment and bacterial coinfections raised concern. MethodsThis retrospective observational study examined the association of the pandemic on outpatient antibiotic sales and prescriptions in Switzerland using interrupted time series (ITS) analyses. Data from IQVIA™ and the Sentinella Network were used to analyze antibiotic consumption and prescription patterns over a 72-month period from January 2018 to December 2023, divided into pre-pandemic, pandemic, and post-pandemic periods. ResultsAntibiotic consumption decreased during the pandemic and returned to pre-pandemic levels in the post-pandemic period. A decrease in level was most pronounced in the French-speaking region (−2.82 defined daily doses per 1,000 inhabitants per day (DID) per month, 95 %CI [−4.34, −1.30], p < 0.001) and the Italian-speaking region (−2.80 DID per month, 95 %CI [−4.78, −0.82], p < 0.01), followed by the German-speaking region (−1.72 DID per month, 95 %CI [−2.71, −0.74], p < 0.01). Similarly, in the ITS, the relative change of model estimates in antibiotic prescriptions by GPs and pediatricians for upper respiratory tract infections, was of −36.0 % and −50.3 % resp. in the pandemic period and +10.1 % and −2.6 % in the post-pandemic period compared with the pre-pandemic period. ConclusionsA decrease of antibiotic prescriptions was observed in GPs and pediatricians during the COVID-19 pandemic, followed by a return to pre-pandemic levels. The patterns in antibiotic prescriptions aligned with the epidemiology of respiratory infections and demonstrated a pronounced association with the implementation and subsequent removal of non-pharmaceutical interventions.

A category incremental continuous learning model for imbalance arrhythmia detection

Abstract The development of efficient arrhythmia detection systems is crucial for physiological measurements and computer-aided diagnosis. Existing systems rely mainly on offline learning methods and lack the ability to assimilate new data or recognize emerging categories. To address these challenges, this study introduces an arrhythmia detection model that is resistant to data imbalance and has continuous learning capabilities, specifically for incremental learning of new ECG data categories. The system incorporates constraints derived from the new class data and implements a dynamic mechanism for updating connection weights, facilitating the incremental continual learning of classes. Confronted with the problem of models forgetting the original data and overfitting with the added data in continuous learning, we introduce a data balancing method by regularization to balance the model's memory and learning of the two types of data. Facing the data imbalance problem in continuous learning, we introduce a posteriori probability weighting strategy. This strategy assigns greater importance to high-value samples based on the model's posterior residual kernel density estimates. Comprehensive testing of the model using various datasets from the MIT-BIH database indicates superior performance in incremental learning tasks. The results reveal that the model not only excels in class incremental learning but also ensures effective balancing across different data classes.