Statistical Model Research Articles

Novel method of image analysis that combines Radon and Fourier image transformations for the purpose of differentiating between malignant and benign colonic biopsies.

Colorectal cancer is the third most common type of cancer. It develops slowly as a polyp that can turn into a cancerous tumor. This study aimed to develop a decision-making algorithm of microscopic images using texture analysis that is orientation free, to be used for automated classification of normal and neoplastic (malignant or premalignant) colonic biopsies. Forty-nine colonic adenocarcinomas, 41 adenomas and a control group of adjacent normal colonic mucosa were included in the texture analysis. Radon transform followed by the Fast Fourier transform were applied to the images. Subsequently, the gray level co-occurrence matrix (GLCM) transform was applied allowing the extraction of four textural variables (homogeneity, contrast, correlation, and entropy). For classification and prediction of the diagnosis, a statistical multivariate regression model and a neural network (NNET) model were used and compared. The statistical model provided a sensitivity of 71.3% and a specificity of 50% (Area under the ROC curve: 0.67) for classifying the neoplastic and the normal images, respectively. The NNET model was superior to the statistical model and produced a sensitivity of 97.9% and specificity of 88% (Area under the ROC curve: 0.92). To our knowledge, this is the first study that used a combination of Radon, FFT, and GLCM transformations in order to overcome the tissue orientation problem in texture analysis of microscopic images of colonic biopsies. The NNET classifier trained by the extracted textural features proved to be superior to the statistical classifier, thus predicting colonic neoplasia with high accuracy. RESEARCH HIGHLIGHTS: We propose a novel decision-making algorithm of orientation invariant image texture analysis, fast and easily implemented for automated differentiation between benign and neoplastic epithelial tumors of the colon. This method can reduce the turnaround time allowing to prioritize the biopsies during their examination and diagnosis by the pathologist.

Channel access mechanism for maximizing throughput with fairness in wireless sensor networks

The spectrum scarcity problem of wireless sensor networks (WSNs) is improved through amalgamation of cognitive radio networks (CRNs) into WSNs. However, spectrum allocation to secondary users (SUs) is challenging in cognitive radio wireless sensor networks (CR-WSNs) as channel is already crowded and at same time should not induce interference to primary users (PUs). In designing efficient spectrum access model for CR-WSNs recent work have adopted machine-learning game theory (GT) and statistical model. However, the major limitation of existing spectrum access model they fail to assure access fairness with maximal throughput with minimal collision. This work presents a maximizing channel access fairness model to handle the research challenges. To boost CR-WSN performance, the throughput maximization using channel access fairness (TMCAF) employs shared and non-shared channel access designs. Experiment outcome shows throughput is improved and collision in network is reduced in comparison with state-of-art channel access models.

An in-silico approach towards multivariate acceptable ranges in biopharmaceutical manufacturing

Multivariate interactions between process parameters can heavily impact product quality and process performance in biopharmaceutical manufacturing processes. Thus, multivariate interactions should be identified and appropriately controlled. This article describes an in-silico approach to establish multivariate acceptable ranges; these ranges help to illustrate the combined impact of multiple input variables on product quality and process performance. Additionally, this article includes a case study for a monoclonal antibody polishing application.Proven acceptable ranges are set by changing only one input parameter at a time while keeping all others constant to understand the impact of process variability on product quality or process performance, but the impact of synergistic variables are not evaluated. Within multivariate acceptable ranges, any combination of input parameters of a unit operation yields the desired product quality and process performance. The layered approach applied in this article is based on risk assessment and statistical models to leverage prior knowledge and existing data. The risk assessment is specific for a manufacturing facility but is applicable to multiple products manufactured in the same facility. No additional wet-lab experiments are required for building the statistical models when development and process characterization are executed using a design of experiments approach, compared to a univariate evaluation of data. The established multivariate acceptable range justifies revised normal operating ranges to ensure process control. Further, the determination of multivariate acceptable ranges adds to overall process knowledge, ultimately supporting the implementation of a more effective control strategy.

Reliability framework for CLT floors in out-of-plane bending using Monte Carlo simulation

Sustainably sourced engineered wood products (EWP) such as cross-laminated timber (CLT) floor panels are viable alternatives to conventional concrete and steel, however their inherent natural variability increases perceived risks and limits wide-spread adoption. The reliability index (β) is the metric used by design standards to communicate allowable structural risk. Although not included directly in any structural capacity equations, it governs all the partial resistance factors and load amplification factors that inform those design code calculations. CLT floors have significant partial resistance factors due to the natural variability of the material that effectively penalise the calculated design strength of those products. By exploring their reliability there is the potential to allow these products to be designed more efficiently, leading to increased competitiveness among traditional materials and less waste. A novel statistical capacity prediction model that goes beyond a simple averaged-value strength assumption to include consideration of the variability between individual boards within a panel was validated against experimental data. A CLT database of 237 out-of-plane bending test results was collected for the first time to inform an appropriate model error factor. Monte Carlo simulation (MCS) approaches were adopted to predict the reliability levels of CLT panels by defining the material, geometrical and loading parameters as random variables and assigning each a mean, coefficient of variation and appropriate distribution. The target and calculated reliability levels were compared across both Eurocode 5 and the North American codes ANSI/APA PRG 320 and CSA O86 as the preeminent CLT design codes internationally. This study found that the calculated reliability levels met or exceeded both codes in most scenarios. The impacts of width, load ratio, material strength distribution, and CLT layups on the expected failure probabilities were explored with sensitivity analyses. The MCS framework was also applied to the calibration of an appropriate resistance factor for CLT design to inform the potential creation of an Australian CLT design code. A resistance factor of 0.85 was calibrated which falls between the equivalent European and North American code resistance factors.

Antimicrobial Effect of Response Surface Optimized Pyocyanin Produced by Pseudomonas aeruginosa

Pseudomonas aeruginosa is notable for its natural bluish-green pigment production, known as pyocyanin. Researchers have credited pyocyanin as an antimicrobial agent, in addition to its other biomedical applications. This study focused on isolating Pseudomonas aeruginosa from the local environment with the ability to synthesize pyocyanin and optimize its cultural conditions for improved yield. A total of thirty-one experimental combinations of process variables using the Central Composite Design (CCD) of Response Surface Methodology (RSM) was ran to find the best conditions for the organism to make pigments. Of the fermentation media evaluated, nutrient broth supplemented with maltose was the best fermentation medium for the organism’s pyocyanin production. A regression model described the interaction between the test variables and the pyocyanin yield with an R2 value of 87.52%, indicating that the model had a satisfactory fitness level. The determined optimal conditions include a maltose concentration of 18 g/L, a pH of 6.8, an inoculum size of 2.3 mL, and an agitation speed of 120 rpm. The optimized condition resulted in a 4.12-fold increase in pyocyanin yield compared to an unoptimized condition. Pyocyanin exhibits high antimicrobial activity compared to the conventional antibiotics assessed against E. coli and Candida albicans. Our study’s findings suggest that statistical models can improve pyocyanin synthesis by Pseudomonas aeruginosa. Furthermore, this pigment has the potential to replace conventional antibiotics in the treatment of microbial infections

Application of Scheffe's Simplex Lattice Model in concrete mixture design and performance enhancement

This comprehensive literature review delves into the application of Scheffe's Simplex Lattice Model for optimizing cement concrete mixtures, with a particular emphasis on its impact on material properties and sustainability. The review meticulously outlines the principles, historical context, and advantages of Scheffe's model, providing a nuanced understanding of its significance. Comparative analyses with traditional and alternative optimization techniques in concrete mix design illuminate the distinct advantages of statistical methods, especially Scheffe's model. The review critically examines the challenges and limitations associated with applying Scheffe's model, addressing issues related to the complexity of concrete mixtures and computational demands. Potential avenues for improvement are explored, suggesting refinements to handle non-linearity, incorporate advanced optimization algorithms, and streamline computational requirements. Additionally, the review highlights emerging trends in statistical modeling for concrete mixture optimization, such as the integration of machine learning and data-driven approaches, signaling the evolving landscape of concrete technology. In conclusion, the literature underscores Scheffe's Simplex Lattice Model as a valuable and versatile tool with far-reaching implications for the advancement of concrete mixture design methodologies. The call to action encourages ongoing research and development to refine the model, explore emerging trends, and address practical challenges, positioning Scheffe's model as a cornerstone in the pursuit of sustainable, resilient, and high-performance concrete materials. The study highlights Scheffe's Simplex Lattice Model as a robust statistical tool for optimizing cement concrete mixtures, demonstrating its efficacy in improving performance indicators like compressive strength and durability. This systematic approach offers a paradigm shift from empirical methods, fostering a nuanced understanding of concrete components' relationships and paving the way for tailored material properties and enhanced sustainability in construction. The call to action urges further research to refine the model, address challenges, and explore hybrid approaches, ultimately advancing concrete mixture design methodologies towards greater efficiency and precision.

A model for galaxy-galaxy strong lensing statistics in surveys

A model for galaxy-galaxy strong lensing statistics in surveys

Underestimation of extremes in sea level surge reconstruction

Statistical models are an alternative to numerical models for reconstructing storm surges at a low computational cost. These models directly link surges to metocean variables, i.e., predictors such as atmospheric pressure, wind and waves. Such reconstructions usually underestimate extreme surges. Here, we explore how to reduce biases on extremes using two methods—multiple linear regressions and neural networks—for surge reconstructions. Models with different configurations are tested at 14 long-term tide gauges in the North-East Atlantic. We found that (1) using the wind stress rather than the wind speed as predictor reduces the bias on extremes. (2) Adding the significant wave height as a predictor can reduce biases on extremes at a few locations tested. (3) Building on these statistical models, we show that atmospheric reanalyses likely underestimate extremes over the 19th century. Finally, it is demonstrated that neural networks can effectively predict extreme surges without wind information, but considering the atmospheric pressure input extracted over a sufficiently large area around a given station. This last point may offer new insights into air-sea interaction studies and wind stress parametrization.

A scalable big data approach for remotely tracking rangeland conditions

Rangelands, covering half of the global land area, are critically degraded by unsustainable use and climate change. Despite their extensive presence, global assessments of rangeland condition and sustainability are limited. Here we introduce a novel analytical approach that combines satellite big data and statistical modeling to quantify the likelihood of changes in rangeland conditions. These probabilities are then used to assess the effectiveness of management interventions targeting rangeland sustainability. This approach holds global potential, as demonstrated in Mongolia, where the shift to a capitalist economy has led to increased livestock numbers and grazing intensity. From 1986 to 2020, heavy grazing caused a marked decline in Mongolia’s rangeland condition. Our evaluation of diverse management strategies, corroborated by local ground observations, further substantiates our approach. Leveraging globally available yet locally detailed satellite data, our proposed condition tracking approach provides a rapid, cost-effective tool for sustainable rangeland management.

Population-Level Balance in Signed Networks

Statistical network models are useful for understanding the underlying formation mechanism and characteristics of complex networks. However, statistical models for signed networks have been largely unexplored. In signed networks, there exist both positive (e.g., like, trust) and negative (e.g., dislike, distrust) edges, which are commonly seen in real-world scenarios. The positive and negative edges in signed networks lead to unique structural patterns, which pose challenges for statistical modeling. In this article, we introduce a statistically principled latent space approach for modeling signed networks and accommodating the well-known balance theory, that is, “the enemy of my enemy is my friend” and “the friend of my friend is my friend.” The proposed approach treats both edges and their signs as random variables, and characterizes the balance theory with a novel and natural notion of population-level balance. This approach guides us towards building a class of balanced inner-product models, and toward developing scalable algorithms via projected gradient descent to estimate the latent variables. We also establish non-asymptotic error rates for the estimates, which are further verified through simulation studies. In addition, we apply the proposed approach to an international relation network, which provides an informative and interpretable model-based visualization of countries during World War II. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

SNP profile for quantitative trait nucleotide in populations with small effective size and its impact on mapping and genomic predictions.

Increasing SNP density by incorporating sequence information only marginally increases prediction accuracies of breeding values in livestock. To find out why, we used statistical models and simulations to investigate the shape of distribution of estimated SNP effects (a profile) around Quantitative Trait Nucleotides (QTN) in populations with a small effective population size (Ne). A QTN profile created by averaging SNP effects around each QTN was similar to the shape of expected pairwise linkage disequilibrium (PLD) based on Ne and genetic distance between SNP, with a distinct peak for the QTN. Populations with smaller Ne showed lower but wider QTN profiles. However, adding more genotyped individuals with phenotypes dragged the profile closer to the QTN. The QTN profile was higher and narrower for populations with larger compared to smaller Ne. Assuming the PLD curve for the QTN profile, 80% of the additive genetic variance explained by each QTN was contained in ± 1/Ne Morgan interval around the QTN, corresponding to 2 Mb in cattle, and 5 Mb in pigs and chickens. With such large intervals, identifying QTN is difficult even if all of them are in the data and the assumed genetic architecture is simplistic. Additional complexity in QTN detection arises from confounding of QTN profiles with signals due to relationships, overlapping profiles with closely-spaced QTN, and spurious signals. However, small Ne allows for accurate predictions with large data even without QTN identification because QTN are accounted for by QTN profiles if SNP density is sufficient to saturate the segments.

Hydro‐thermal modelling of the potential impacts of reservoirs on water temperature and incubation time of Atlantic salmon and brook trout in the Tobique River, Canada

AbstractWater temperature is recognised as a crucial variable in lotic ecosystems affecting the metabolism of aquatic organisms, with extremely high‐temperature events increasing the risk of mortality of various species. Impounding rivers to regulate flow and generate electricity is one of the causes of altered thermal regimes in river systems. This paper presents the results of a study to simulate the impact of reservoirs on downstream temperatures of the Tobique River, New Brunswick, Canada. CEQUEAU, a hydrological and water temperature model, was used to simulate and assess the flow and water temperature of the Tobique River from 1997 to 2020. It was coupled to a statistical model to generate water temperature at different depths in the reservoir. Results show that the CEQUEAU model was successfully calibrated for water temperature with an overall root mean square error of 1.7°C. Based on the results, when water is drawn from the lower parts of the reservoir, the mean water temperature downstream of the dam increases in winter and spring by ~1°C. The results of this paper provide insight into how dams can affect the incubation time of cold‐water species in eastern Canada. We calculated the hatching degree days of Atlantic salmon and Brook trout to evaluate the impact of dams on fry emergence. Results indicated that the hatching period of Atlantic salmon and Brook trout downstream of reservoirs may shorten under the influence of dams.

Fixation or Revision for Periprosthetic Fractures: Epidemiology, New Trends, and Projections in the United States.

Periprosthetic fractures can be devastating complications after total joint arthroplasty (TJA). The management of periprosthetic fractures is complex, spanning expertise in arthroplasty and trauma. The purpose of this study was to examine and project trends in the operative treatment of periprosthetic fractures in the United States. A large, public and private payer database was queried to capture all International Classification of Diseases, Tenth Revision (ICD-10) diagnosis codes for periprosthetic femoral and tibial fractures. Statistical models were created to assess trends in treatment for periprosthetic fractures and to predict future surgical rates. An alpha value of 0.05 was used to assess significance. A Bonferroni correction was applied where applicable to account for multiple comparisons. In this study, from 2016 to 2021, 121,298 patients underwent surgical treatment for periprosthetic fractures. There was a significant increase in the total number of periprosthetic fractures. The incidence of periprosthetic hip fractures rose by 38% and that for periprosthetic knee fractures rose by 73%. The number of periprosthetic fractures is predicted to rise 212% from 2016 to 2032. There was a relative increase in open reduction and internal fixation (ORIF) compared with revision arthroplasty for both periprosthetic hip fractures and periprosthetic knee fractures. Periprosthetic fractures are anticipated to impose a substantial health-care burden in the coming decades. Periprosthetic knee fractures are predominantly treated with ORIF rather than revision total knee arthroplasty (TKA), whereas periprosthetic hip fractures are predominantly treated with revision total hip arthroplasty (THA) rather than ORIF. Both periprosthetic knee fractures and periprosthetic hip fractures demonstrated increasing trends in this study. The proportion of periprosthetic hip fractures treated with ORIF relative to revision THA has been increasing. Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Forecasting and Comparison of Nonlinear Statistical Growth Models for Fish Seed Production in Eastern Coastal States of India

The study was carried out to compare the goodness of fit of three non–linear growth models viz., Monomolecular, Logistic and Gompertz growth models and analysing the instability index among the collected fish seed Production data from the eastern costal states of Indian for the period from 1994–1995 to 2019–2020. The parameters of each model were estimated using Levenberg–Marquardt (LM) iterative method. The ‘independence’ and ‘normality’ of error terms were examined by using ‘Run–test’ and ‘Shapiro–Wilk test’. Among the three models tested, monomolecular model was found as best model to describe variation in fish seed production in all Eastern Costal States of India. The values of CDI for fish seed production were 146.94 %, 79.30 % and 63.41% respectively which revealed that fish seed production had high instability in Odisha, Tamil Nadu and Andhra Pradesh while low instability (14.93 % and 13.13%) in West Bengal and India.

Experimental and theoretical investigation of water sorption on lemon leaves: New perspectives with statistical physical modeling

A deeper analysis of the water sorption isotherms on lemon leaves (40, 50, and 60 °C) was carried out using statistical physical modeling to obtain information about the sorption mechanism and better understand the process. For this, a theoretical model was developed based on the grand canonical potential to describe the experimental data, and the isotherms were analyzed using the finite multilayer sorption model. The experimental sorption isotherms obtained for lemon leaves are Type II. The model used to analyze the process showed excellent predictive capacity, with coefficient of determination values above 0.97 and root mean square error below 0.08. The interpretation of the estimated parameters indicated that the water molecules are parallel to the surface of the lemon leaves, which allows the formation of finite layers of water. The system is directly affected by temperature, where increasing temperature decreases the density of receptor sites. The process occurs through physisorption since the calculated sorption energies are less than 50 kJ mol−1. The thermodynamic analysis of the process shows that water sorption on leaves is exothermic and spontaneous. This work contributes to a better understanding the drying process through the results obtained.

SpliceAPP: an interactive web server to predict splicing errors arising from human mutations

BackgroundSplicing variants are a major class of pathogenic mutations, with their severity equivalent to nonsense mutations. However, redundant and degenerate splicing signals hinder functional assessments of sequence variations within introns, particularly at branch sites. We have established a massively parallel splicing assay to assess the impact on splicing of 11,191 disease-relevant variants. Based on the experimental results, we then applied regression-based methods to identify factors determining splicing decisions and their respective weights.ResultsOur statistical modeling is highly sensitive, accurately annotating the splicing defects of near-exon intronic variants, outperforming state-of-the-art predictive tools. We have incorporated the algorithm and branchpoint information into a web-based tool, SpliceAPP, to provide an interactive application. This user-friendly website allows users to upload any genetic variants with genome coordinates (e.g., chr15 74,687,208 A G), and the tool will output predictions for splicing error scores and evaluate the impact on nearby splice sites. Additionally, users can query branch site information within the region of interest.ConclusionsIn summary, SpliceAPP represents a pioneering approach to screening pathogenic intronic variants, contributing to the development of precision medicine. It also facilitates the annotation of splicing motifs. SpliceAPP is freely accessible using the link https://bc.imb.sinica.edu.tw/SpliceAPP. Source code can be downloaded at https://github.com/hsinnan75/SpliceAPP.

Carbon emissions trading price forecasts by multi-perspective fusion

<p>The precise prediction of carbon emissions trading prices is the foundation for the stable and sustainable development of the carbon financial market. In recent years, influenced by a combination of factors such as the pandemic, trading regulations, and policies, carbon prices have exhibited strong random volatility and clear non-stationary characteristics. Traditional single-perspective prediction methods based on conventional statistical models are increasingly inadequate due to the homogenization of features and are struggling to adapt to China's regional carbon emissions trading market. Therefore, this paper proposes a multi-perspective fusion-based prediction method tailored to the Chinese market. It leverages carbon emissions trading information from key cities as relevant features to predict the price changes in individual cities. Inspired by the development of artificial intelligence, this paper implements various time series models based on deep neural networks. The effectiveness of the multi-perspective approach is validated through multiple metrics. It provides scientific decision-making tools for domestic carbon emissions trading investors, making a significant contribution to strengthening carbon market risk management and promoting the establishment and rational development of a unified carbon market in China.</p>

Ramadhan effect towards Nifty Shariah indices in India

Purpose This paper aims to analyse the weekday effect on the Nifty Shariah indices as per the Islamic calendar. The study is intended to know about the return and volatility of these indices during Ramadhan and non-Ramadhan days. Design/methodology/approach The study focuses on analysing the Nifty Shariah indices and Sensex daily returns collected from NSE India and BSE India, respectively, during the period of 1 August 2016 to 31 July 2022. Descriptive statistics are used to analyse the data, while the Ordinary Least Square method is used to determine the impact of weekdays on the Nifty Shariah indices. Additionally, the study applies the GARCH statistical model to examine the influence of Ramadhan on the returns and volatility of the Nifty Shariah indices. Findings All of the Nifty Shariah indices produced positive returns during the overall sample period. According to the study, Tuesday index returns outperform other weekdays. The GARCH model indicated that the coefficient values for the Nifty 50 Shariah and Nifty 500 Shariah indices were negative. Ramadhan has a strong negative effect on volatility, according to this study. Originality/value The outcomes of the research are beneficial for investors aiming to exploit daily or weekly price fluctuations, rather than pursuing extended investment periods. Furthermore, fund managers can employ these findings to shape trading strategies, and academics can examine the performance of Shariah indices in the Indian context. This enables devout investors to make significant financial choices, thus advancing ethical values in society and upholding standards of both public and private morality.

Insights into nearshore sandbar dynamics through process-based numerical and logistic regression modeling

Process-based nearshore morphodynamic models are commonly used tools by coastal engineers and planners to predict the nearshore morphology change of sandy beaches across various spatiotemporal scales. Accurate modeling of the morphological response on medium and long time scales is imperative for quantitative assessments of coastal infrastructure over a project’s intended life-span. However, most previous modeling applications have focused on single/sub-seasonal storm events and are often limited to an assessment of the subaerial beach (i.e. berm and dune). This not only leaves uncertainty concerning the quality of morphology predictions on extended (> weeks) time scales, but also the capacity of process-based models to emulate realistic nearshore sandbar dynamics and the corresponding exchange of sediment between the nearshore-beach system. To shed light on these meso-scale dynamics, CSHORE, a 1D phase-averaged, process-based nearshore morphodynamic model, was applied on an annual scale to a multi-barred, dissipative beach in Oysterville, WA, USA. Thousands of unique sediment transport and hydrodynamic parameter combinations were executed during model calibration. A large portion of these simulations displayed physically realistic sandbar dynamics, including the growth, decay, and migration of intertidal and subtidal sandbars. To explore the model mechanisms enabling realistic bar behavior, the binary and multinomial logistic regression model were used to quantify the relationship between model parameter selection and the probability of various categorical bar configurations occurring in the final predicted profile. The results indicate the most sensitive parameters associated with barred morphology, in this study, and support the use of separate sediment transport parameters for low and high wave energy conditions. The co-utilization of numerical and statistical modeling outlined in this publication is generalizable to future exploratory modeling and/or calibration routines concerned with categorical outcomes.

Characteristics of Nontyphoid Salmonella Isolated from Human, Environmental, Animal, and Food Samples in Burkina Faso: A Systematic Review and Meta-Analysis.

Salmonella is one of the world's leading causes of zoonotic and foodborne illnesses. Recently, antimicrobial resistance (AMR) has become one of the most critical challenges to public health and food safety. Herein, we employed a meta-analysis to determine the pooled prevalence and spatiotemporal distribution of serovars and antimicrobial resistance in NTS in Burkina Faso. To find eligible articles, a comprehensive literature search of PubMed, African Journals Online, ScienceDirect, Google Scholar, and the gray literature (university libraries) in Burkina was conducted for the period from 2008 to 2020. Studies meeting the inclusion criteria were selected and assessed for risk of bias. To assess the temporal and spatial relationships between serotypes and resistant strains from humans, animals, food, and the environment, a random-effects statistical model meta-analysis was carried out using the Comprehensive Meta-Analysis Version 3.0 program. The NTS prevalence rates were 4.6% (95% CI: 3-7) and 20.1% (95% CI: 6.6-47.4) in humans and animals, respectively, and 16.8% (95% CI: 10.5-25.8) and 15.6% (95% CI: 8.2-27.5) in food and the environment, respectively. Most NTS serovars were S. Derby, reported both in food and animals, and S. Typhimurium, reported in humans, while S. Croft II, S. Jodpur II, and S. Kentucky were the most prevalent in the environment. NTS isolates were highly resistant to erythromycin, amoxicillin, cefixime, and cephalothin, with a pooled prevalence of multidrug resistance of 29% (95% CI: 14.5-49.5). The results of this review show a high diversity of Salmonella serotypes, as well as high antibiotic resistance in Salmonella isolates from animal, human, food, and environmental samples in Burkina, calling for a consolidated "One Health" approach to better understand the drivers of pathogen emergence, spread, and antimicrobial resistance, as well as the formulation of intervention measures needed to limit the risk associated with the disease.