Statistical Model Research Articles

Using seasonal oscillations in fin ray microchemistry to chemically age Lake Sturgeon

AbstractObjectiveNonlethally sampled pectoral fin rays are commonly used as aging structures for Lake Sturgeon Acipenser fulvescens, but they tend to underestimate the true age in older, slow‐growing individuals (age > 14 years). Current aging practices involve counting bands along the structure, which are construed as annuli. Oscillations of certain trace elements corresponding with annuli have been seen across various fish species, with patterns continuing to the marginal edge of hard structures. This study explored the aging of fin rays by using microchemistry patterns in Lake Sturgeon of known age (n = 94; ages 5–21) to determine the potential use of this method as an alternative or supplementary aging technique.MethodsElements were chosen for age determination analysis by examining the relationship between profile minima/maxima and visually interpreted annuli. Fish were assigned ages using three different methods: (1) traditional interpretation (counting annuli along the structure), (2) chemical interpretation (examination of seasonal variations in elemental profiles with visually identified annuli), and (3) a statistical model (multivariate multiple changepoint analysis with finite differencing using raw elemental profiles).ResultMean absolute differences between age estimates and known age were significantly higher for the traditional interpretation method than for the chemical interpretation method. The mean coefficient of variation in estimated age was 11.14% for the traditional interpretation method and 4.04% for the chemical interpretation method. The changepoint model was able to correctly classify age for 100% of the samples within ±1 year in one population (ages 5–8) but could not classify samples from the second population (ages 12–21).ConclusionOur results suggest that chemical aging techniques could provide more reliable age estimates for juvenile and subadult Lake Sturgeon when fin rays are the only aging option. Further work is required to determine the applicability of the model for assigning ages to older fish and for use with different populations and structures.

Prediction of hypertensive disorders in pregnant women in the «gray» risk zone following combined first-trimester screening

Introduction. In obstetrics, hypertensive disorders of pregnancy (HDP) including preeclampsia (РЕ) are one of the primary causes resulting in critical cases and maternal mortality. HDP prediction is a milestone that allows preventing complications as well as reducing the number of most common relevant complications of pregnancy. Existing algorithms that predict PE risk distribute the risks in such a way that a considerable number of patients fall into the category of false negative results, and, consequently, receive no timely prevention and proper follow-up. In particular, this cohort usually consists of patients with borderline high risks, who may be designated as a medium risk group or located in a “gray” zone. Aim: to develop a prognostic model for risk stratification in female patients with borderline to high developing PE risk based on combined first-trimester screening. Materials and Methods. A prospective comparative study included 1089 female patients who underwent a combined screening at 11–14 weeks of gestation. Group 1 consisted of female patients at high РЕ risk (1:100 and greater), while female patients at moderate risk (1:101–1:250) and low risk (below 1:250) were included into Group 2 and Group 3, respectively. All pregnant women underwent examination including assessed anamnestic, general clinical and laboratory data, mean blood pressure (BP), uterine artery pulsatility index, serum level of human chorionic gonadotropin beta-subunits (β-hCG), placental growth factor (PlGF), pregnancy-associated plasma protein-A (PAPP-A). Results. An impact of various factors on risk of developing hypertensive disorders in pregnancy was assessed by binary logistic regression by identifying most significant among them and generating a statistical prediction model – the prognostic index of hypertensive disorders in pregnancy. The latter included: obstetric history, body mass index, PlGF, mean ВР, and alanine aminotransferase level. The sensitivity and specificity comprised 91.2 and 53.6 %, respectively, and the method effectivenesswas 81.8 %. Conclusion. The method proposed for HDP prediction is a second-line approach that may be used in clinical practice to stratify patients with borderline high risk of developing PE.

Human Health Risk Assessment of Radionuclide Contamination in Drinking Water

This study investigates the human health risks of uranium, radium, radon, and other drinking water radionuclides and their mitigation strategies. It was implemented through literature review, field sampling, and analytical methods. Samples were taken from various sources, including groundwater, surface water, municipal supplies, and private wells. ICPI-MS and liquid scintillation counters were used for radiation measurements. Statistical analysis and risk assessment models were used to measure health risks and treatment effectiveness. Groundwater sources were the main sources of radionuclides, with private wells being the main sources. The elimination efficiencies of reverse osmosis were exceptional, reaching up to 99%. The elderly population (60+ years) were the most likely to have cancer, with the highest risks for bladder cancer, lung cancer, kidney cancer, and leukemia. The frequency of radionuclide contamination in drinking water sources varied, with the U.S. Environmental Protection Agency, Nigerian EPA, and Canada having the strictest schedules. The results emphasize the urgent need for monitoring programs, effective treatment technologies, and targeted risk management strategies to cope with radionuclide contamination. Government advice includes improving the regulatory system, developing advanced treatment methods, long-term epidemiological studies, public awareness, interdisciplinary collaboration, scientific exploration of alternative water sources, and prioritizing interventions for vulnerable populations.

Utilizing Artificial Neural Networks for Geometric Bone Model Reconstruction in Mandibular Prognathism Patients

Patient-specific 3D models of the human mandible are finding increasing utility in medical fields such as oral and maxillofacial surgery, orthodontics, dentistry, and forensic sciences. The efficient creation of personalized 3D bone models poses a key challenge in these applications. Existing solutions often rely on 3D statistical models of human bone, offering advantages in rapid bone geometry adaptation and flexibility by capturing a range of anatomical variations, but also a disadvantage in terms of reduced precision in representing specific shapes. Considering this, the proposed parametric model allows for precise manipulation using morphometric parameters acquired from medical images. This paper highlights the significance of employing the parametric model in the creation of a personalized bone model, exemplified through a case study targeting mandibular prognathism reconstruction. A personalized model is described as 3D point cloud determined through the utilization of series of parametric functions, determined by the application of geometrical morphometrics, morphology properties, and artificial neural networks in the input dataset of human mandible samples. With 95.05% of the personalized model’s surface area displaying deviations within −1.00–1.00 mm relative to the input polygonal model, and a maximum deviation of 2.52 mm, this research accentuates the benefits of the parametric approach, particularly in the preoperative planning of mandibular deformity surgeries.

Facial Skin Microbiome Composition and Functional Shift with Aging.

The change in the skin microbiome as individuals age is only partially known. To provide a better understanding of the impact of aging, whole-genome sequencing analysis was performed on facial skin swabs of 100 healthy female Caucasian volunteers grouped by age and wrinkle grade. Volunteers' metadata were collected through questionnaires and non-invasive biophysical measurements. A simple model and a biological statistical model were used to show the difference in skin microbiota composition between the two age groups. Taxonomic and non-metric multidimensional scaling analysis showed that the skin microbiome was more diverse in the older group (≥55 yo). There was also a significant decrease in Actinobacteria, namely in Cutibacterium acnes, and an increase in Corynebacterium kroppenstedtii. Some Streptococcus and Staphylococcus species belonging to the Firmicutes phylum and species belonging to the Proteobacteria phylum increased. In the 18-35 yo younger group, the microbiome was characterized by a significantly higher proportion of Cutibacterium acnes and Lactobacillus, most strikingly, Lactobacillus crispatus. The functional analysis using GO terms revealed that the young group has a higher significant expression of genes involved in biological and metabolic processes and in innate skin microbiome protection. The better comprehension of age-related impacts observed will later support the investigation of skin microbiome implications in antiaging protection.

How does artificial intelligence work in organisations? Algorithmic management, talent and dividuation processes

AbstractThis article analyses the forms of dividuation workers undergo when they are linked to technologies, such as algorithms or artificial intelligence. It examines functionalities and operations deployed by certain types of Talent Management software and apps—UKG, Tribepad, Afiniti, RetailNext and Textio. Specifically, it analyses how talented workers materialise in relation to the profiles and the statistical models generated by such artificial intelligence machines. It argues that these operate as a nooscope that allows the transindividual plane to be quantified through a process known as dividuation. Finally, by way of conclusion, the notion of the dividual is presented as the logic that characterises the human–machine relationship in the case of artificial intelligence and as the horizon of what Felix Guattari called “machinic capitalism”.

Predictive Modelling on Competitor Analysis Performance by using Generalised Linear Models and Machine Learning Approach

Competitive analysis in digital and technology is trending in the business field. However, the field of digital and technology in the business world is vast and challenging to analyse. The purpose of this research is first to identify the success factor which represent the company performance. Second, is to identify the significant services provided by the company to their business user. Then, based on the first and second objectives, a predictive modelling is developed to produce the best solution to their business user. The research is implementing a case study from Telecommunication Company and using data science life cycle methodology. The statistical modelling that is used to develop the competitor’s analysis model is generalised linear model (GLM) which integrated with machine learning approach. Furthermore, the synthetic data set is created by using Gamma Distribution, Gaussian Distribution and Poisson Distribution due to some data from the case study is confidential. The synthetic data set is based on existing real data which are from Telecommunication Company sentiment analysis data, were used to investigate the performance of the proposed model. The machine learning technique is used to get the accuracy of the significant GLM which has been developed. The accuracy is tested by using the error rates of the machine learning technique which are Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared. This research discovered that the business solution to the significant service for the business user and discovered the best statistical model to be used for the business solution. The results show that the Gumbel distribution is the best fit model for the synthetic dataset where the values of RMSE is 1.0574, MAE is 0.9168 and R-squared is 0.3994, and the significant success factor that has been identified by using the GLM is advertising success factor. The model developed can be improved with another type of data set and different sizes of data. Hence, further studies and real-world data are required for better validation.

Consistent efficacy and safety of sublingual immunotherapy tablets across allergens and geographic regions.

The clinical development program of the SQ grass, ragweed, tree, and house dust mite (HDM) sublingual immunotherapy (SLIT)-tablets for allergicrhinitis/conjunctivitis (AR/C) included clinical trials conducted in North America,Europe, and Japan. Data from these trials were analyzed to assess efficacy, immunologic mechanisms, and safety outcomes acrossallergens and geographic regions. Thirteen phase III, double-blind, placebo controlled trials in the subjects with AR/C were conducted in NorthAmerica, Europe (including Russia), and Japan (N = 7763 analyzed). Trials were generally similar with respect to medicalpractice, target population, eligibility criteria, and efficacy and safety monitoring. Data were analyzed for the approved dosesin North America and Europe. Four statistical models were used to enhance comparison of the efficacy end points among the trials. The SLIT-tablets demonstrated consistent efficacy across allergens and regions, regardless of the statistical analysis used. Relative improvement in the primary efficacy end point compared with placebo by using the predefined protocol analysis ranged from 17.9% to 32.8%, 17.5% to 19.3%, 20.6% to 38.3%, and 39.6% with the grass, HDM, ragweed, and tree SLIT-tablets, respectively. The kinetics of specific immunoglobulin E (IgE) and IgG4 responses were similar among the allergensand regions. Local application-site reactions were the most common adverse events for all allergens and in all regions. Mosttreatment-related adverse events for all allergens and in all regions were mild in severity. The rate of systemic allergic reactions was similar across regions (0%-0.54%). Confirmatory phase III trials for SLIT-tablets in the treatment of AR/C showed consistent efficacy, immunologic,and safety outcomes across allergens and geographic regions.

Progress in Theoretical Modelling of Macroscopic and Microscopic Dynamics of Bolted Joints in Complex Equipment

Bolt connection structure is a common form of connecting large and complex equipment. Its object contact surfaces under normal and tangential loads will appear in the form of slip and adhesion, which affects the service life of mechanical equipment. Bolted connection structures cause changes in stiffness and damping, which have great impacts on the dynamic characteristics. Experimental studies and numerical simulations have difficulty predicting the overall performance of bolts in a timely manner, hence cannot ensure the reliability and safety of complex equipment. In order to improve the overall performance of complex equipment, it is necessary to study the contact theory model of bolt connection structures. Based on the relationship between friction force and velocity in the classical friction model, the mathematical expressions of restoring force and tangential displacement in the kinetic theory model are deduced to predict the stiffness degradation of the bolted structure and to characterise the kinetic properties and laws of the bolted structure. From the perspective of theoretical calculation, it makes up for the situation in which it is difficult to measure the performance of bolts due to the existence of spanning scale and provides theoretical support for the reliability of connecting complex equipment. This paper summarises and analyses the contact theory model of bolt connection structures, ranging from macroscopic to microscopic; describes the static friction model, kinetic friction model, statistical summation contact model, fractal contact model; and analyses the influencing factors of the microscopic contact mechanism. The advantages and disadvantages of the kinetic theoretical models are described, the manifestation of friction and the relationship between tangential force–displacement are discussed, and the key research directions of the kinetic theoretical models of bolted structures in the future are elucidated.

Measurement of 144Sm(p, γ) cross-section at Gamow energies

Abstract The cross-section measurement of 144Sm(p, γ)145Eu (T1/2 = 5.93(4) days) reaction has been performed at proton energies around 2.6-6.8 MeV using the activation technique. Molecular deposition technique was used to prepare the 144Sm targets having thickness between 100-350 μg/cm2 on Al backing. The astrophysical S-factor of this reaction has been measured for the first time at Ecm p=2.57±0.13 MeV, S-factor=2.542±1.152(×1010) MeV-b. Cross-section data were compared with the previously measured experimental data (2.8-7.5 MeV) from literature and the theoretical predictions obtained using Hauser-Feshbach statistical model. Obtained results were utilised to predict the reaction rates for 144Sm (p, γ) and 145Eu (γ, p) reactions.

Feasibility of Wind Energy Utilization for Sustainable Power Generation in Ilorin, Kwara State, Nigeria's North-Central Region

The escalating energy demands across Nigeria, especially in remote rural areas, have outpaced the capacity of the national electricity grid, necessitating the development of independent and sustainable energy sources. Among the renewable options, wind energy stands out as a promising solution. This study focuses on assessing the potential of wind energy in Ilorin, located in Kwara State, within Nigeria's north-central region. Utilizing data collected from 2007 to 2021 by the Nigerian Meteorological Agency, the research examines monthly average wind speeds at two specific coordinates in Ilorin, considering variations in air density. The study utilizes a 15-year set of monthly average wind velocities obtained from the Nigerian Meteorological Agency (NiMet) Headquarters in Abuja, measured at a height of 10 meters above ground level. By employing the 2-coefficient Weibull statistical model and extrapolation principles across different altitudes ranging from 150 to 900 meters above ground level, the study reveals distinct seasonal patterns of wind speeds ranging from 1.1 to 5.1 m/s in Ilorin. Furthermore, wind power density values ranging from 6.7 to 39.20 W/m2 are identified, with optimal wind attributes observed at altitudes exceeding 900 meters. These findings provide valuable insights for assessing the feasibility of wind energy utilization and designing efficient systems in Nigeria's north-central regions, aiding in the sustainable energy transition.

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 paper, we introduce a statistically principled latent space approach for modeling signed networks and accommodating the well-known balance theory, i.e., “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 towards 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.

A Scapular Statistical Shape Model can Reliably Predict Premorbid Glenoid Morphology in Conditions of Severe Glenoid Bone Loss.

Knowledge of premorbid glenoid parameters at the time of shoulder arthroplasty, such as inclination, version, joint line position, height, and width, can assist with implant selection, implant positioning, metal augment sizing and/or bone graft dimensions. The objective of this study was to validate a scapular statistical shape model (SSM) in predicting patient-specific glenoid morphology in scapulae with clinically relevant glenoid erosion patterns. Computer tomography scans of 30 healthy scapulae were obtained and used as the control group. Each scapula was then virtually eroded to create seven erosion patterns (Walch A1, A2, B2, B3, D, Favard E2, and E3). This resulted in 210 uniquely eroded glenoid models, forming the eroded glenoid group. A scapular SSM, created from a different database of 85 healthy scapulae, was then applied to each eroded scapula to predict the premorbid glenoid morphology. The premorbid glenoid inclination, version, height, width, radius of best fit sphere, and glenoid joint line position were automatically calculated for each of the 210 eroded glenoids. The mean values for all outcome variables were compared across all erosion types between the healthy, eroded, and SSM predicted groups using a two-way repeated-measures analysis of variance. The SSM was able to predict the mean premorbid glenoid parameters of the eroded glenoids with a mean absolute difference of 3±2° for inclination, 3±2° for version, 2±1mm for glenoid height, 2±1mm for glenoid width, 5±4mm for radius of best fit sphere, and 1±1mm for glenoid joint line. The mean SSM predicted values for inclination, version, height, width, and radius were not significantly different than the control group (P>0.05). A statistical shape model has been developed that can reliably predict premorbid glenoid morphology and glenoid indices in patients with common glenoid erosion patterns. This technology can serve as a useful template to visually represent the premorbid healthy glenoid in patients with severe glenoid bony erosions. Knowledge of the premorbid glenoid preoperatively can assist with implant selection, positioning, and sizing.

White men can't jump, but do they still get picked first? Race and player selection in the NBA draft, 1980-2021.

Despite excelling at recruiting Black players, studies have repeatedly produced evidence of racial discrimination in the National Basketball Association (NBA). Through this study, we re-examine the topic of racial discrimination within the NBA through an analysis of the Association's annual entry draft. Using a novel dataset, we statistically model the relationship between player race and draft pick number using pooled data from 1980 to 2021. Overall, we find only limited evidence of racial discrimination. These findings are generally robust to sub-sample analyses, alternative specifications of our race variable, and alternate statistical modeling techniques. However, analyses performed on sub-samples of draft picks that participated in the NBA combine-and for whom we have measurements of player athleticism-produce some evidence of racial discrimination. Through such models we estimate that Black players are picked roughly three picks later in the draft. We consider the implications of these findings for contemporary theorizing about racial discrimination in the NBA and more mainstream labor markets.

Misstatements, misperceptions, and mistakes in controlling for covariates in observational research.

We discuss 12 misperceptions, misstatements, or mistakes concerning the use of covariates in observational or nonrandomized research. Additionally, we offer advice to help investigators, editors, reviewers, and readers make more informed decisions about conducting and interpreting research where the influence of covariates may be at issue. We primarily address misperceptions in the context of statistical management of the covariates through various forms of modeling, although we also emphasize design and model or variable selection. Other approaches to addressing the effects of covariates, including matching, have logical extensions from what we discuss here but are not dwelled upon heavily. The misperceptions, misstatements, or mistakes we discuss include accurate representation of covariates, effects of measurement error, overreliance on covariate categorization, underestimation of power loss when controlling for covariates, misinterpretation of significance in statistical models, and misconceptions about confounding variables, selecting on a collider, and p value interpretations in covariate-inclusive analyses. This condensed overview serves to correct common errors and improve research quality in general and in nutrition research specifically.

Improving and Predicting the Surface Roughness and the Machining Accuracy in Ultrasonic Vibration-Assisted Milling

AbstractThe present research work amis to develop a controlled vibratory system for improving the performance of slotting process based on the vibration-assisted milling technique. In addition, the establishment of a statistical model for predicting the process efficiency was a key aim target.In the current investigation, an ultrasonic vibratory device was designed to provide vibration oscillations in the cutting feed direction to the 7075 aluminum alloy workpiece side at 34.7 kHz frequency and 10 μm amplitude. A LabView code has been developed to control the input vibration parameters. A full factorial design of experiments with 4 factors and 2 levels was conducted. An in-depth statistical analysis is then implemented to study the effect of machining and vibration parameters on the process performance. Then, a statistical model for predicting the process response in terms of cutting force, surface roughness, and machining accuracy has been established.Some experiments showed a significant reduction in cutting force up to 50% in the feed direction. Additionally, an improvement in workpiece surface roughness was recorded. Regarding the machined surface accuracy, introducing ultrasonic vibrations significantly reduces the produced slot width error compared to Conventional Milling (CM).The developed statistical model shows a very well agreement with the experimental results. Therefore, the developed VAM technique is suitable for manufacturing parts that require high geometrical and dimensional accuracy.

Climate Change Risks for the Mediterranean Agri-Food Sector: The Case of Greece

The study assesses the direct effects of climate change by 2060, including extreme events, on the productivity of regional crop farming and livestock in Greece, and the broader socio-economic effects on the agri-food and other sectors. Different approaches (i.e., agronomic models, statistical regression models, and equations linking thermal stress to livestock output) were combined to estimate the effects on productivity from changes in the average values of climatic parameters, and subsequently the direct economic effects from this long-term climate change. Recorded damages from extreme events together with climatic thresholds per event and crop were combined to estimate the direct economic effects of these extremes. The broader socio-economic effects were then estimated through input–output analysis. Under average levels of future extreme events, the total direct economic losses for Greek agriculture due to climate change will be significant, from EUR 437 million/year to EUR 1 billion/year. These losses approximately double when indirect effects on other sectors using agricultural products as inputs (e.g., food and beverage, hotels, and restaurants) are considered, and escalate further under a tenfold impact of extreme events. Losses in the GDP and employment are moderate at the national level, but significant in regions where the contribution of agriculture is high.

Generalizability of polygenic prediction models: how is theR2defined on test data?

Polygenic risk scores (PRS) quantify an individual's genetic predisposition for different traits and are expected to play an increasingly important role in personalized medicine. A crucial challenge in clinical practice is the generalizability and transferability of PRS models to populations with different ancestries. When assessing the generalizability of PRS models for continuous traits, the is a commonly used measure to evaluate prediction accuracy. While the is a well-defined goodness-of-fit measure for statistical linear models, there exist different definitions for its application on test data, which complicates interpretation and comparison of results. Based on large-scale genotype data from the UK Biobank, we compare three definitions of the on test data for evaluating the generalizability of PRS models to different populations. Polygenic models for several phenotypes, including height, BMI and lipoproteinA, are derived based on training data with European ancestry using state-of-the-art regression methods and are evaluated on various test populations with different ancestries. Our analysis shows that the choice of the definition can lead to considerably different results on test data, making the comparison of values from the literature problematic. While the definition as the squared correlation between predicted and observed phenotypes solely addresses the discriminative performance and always yields values between 0 and 1, definitions of the based on the mean squared prediction error(MSPE) with reference to intercept-only models assess both discrimination and calibration. These MSPE-based definitions can yield negative values indicating miscalibrated predictions for out-of-target populations. We argue that the choice of the most appropriate definition depends on the aim of PRS analysis - whether it primarily serves for risk stratification or also for individual phenotype prediction. Moreover, both correlation-based and MSPE-based definitions of can provide valuable complementary information. Awareness of the different definitions of the on test data is necessary to facilitate the reporting and interpretation of results on PRS generalizability. It is recommended to explicitly state which definition was used when reporting values on test data. Further research is warranted to develop and evaluate well-calibrated polygenic models for diverse populations.

Interrelations Between Surgical Outcome and Bone, Spring and Surgical Parameters in Scaphocephalic Skulls Treated with Spring‐Assisted Cranioplasty

AbstractSagittal synostosis is the most common type of craniosynostosis encompassing around 50% of craniosynostosis conditions. It can be corrected using spring‐assisted cranioplasty in which springs are compressed at both ends and inserted into the skull to expand it. Although it has advantages over open surgical techniques the surgical outcome may remain suboptimal due to the interaction of skull, spring and surgical parameters. Therefore, optimizing the surgical outcome in the correction of sagittal synostosis with spring‐assisted cranioplasty requires understanding the interaction of multiple factors. In this study, the interrelations between the surgical outcome and skull, spring and surgical parameters in sagittal synostosis treated with spring‐assisted cranioplasty are investigated. Two different statistical shape models are used to simulate skull correction with spring‐assisted cranioplasty. Forty samples for each parameter are generated randomly and spring‐assisted cranioplasty are simulated in finite element models using the combinations of the parameters. Linear regression analyzes revealed that the elastic modulus of the bones and bone thickness have significant effects on the post‐operative cranial index in sagittal synostosis after spring‐assisted cranioplasty. Therefore, quantifying the mechanical and geometric properties of a skull and planning the surgery accordingly may ultimately improve surgical outcomes in sagittal synostosis corrected with spring‐assisted cranioplasty.

Landscape drives zoonotic malaria prevalence in non-human primates.

Zoonotic disease dynamics in wildlife hosts are rarely quantified at macroecological scales due to the lack of systematic surveys. Non-human primates (NHPs) host Plasmodium knowlesi, a zoonotic malaria of public health concern and the main barrier to malaria elimination in Southeast Asia. Understanding of regional P. knowlesi infection dynamics in wildlife is limited. Here, we systematically assemble reports of NHP P. knowlesi and investigate geographic determinants of prevalence in reservoir species. Meta-analysis of 6322 NHPs from 148 sites reveals that prevalence is heterogeneous across Southeast Asia, with low overall prevalence and high estimates for Malaysian Borneo. We find that regions exhibiting higher prevalence in NHPs overlap with human infection hotspots. In wildlife and humans, parasite transmission is linked to land conversion and fragmentation. By assembling remote sensing data and fitting statistical models to prevalence at multiple spatial scales, we identify novel relationships between P. knowlesi in NHPs and forest fragmentation. This suggests that higher prevalence may be contingent on habitat complexity, which would begin to explain observed geographic variation in parasite burden. These findings address critical gaps in understanding regional P. knowlesi epidemiology and indicate that prevalence in simian reservoirs may be a key spatial driver of human spillover risk.