Modeling Perspective Research Articles

Can extreme climatic and bioclimatic indices reproduce soy and maize yields in Latin America? Part 1: an observational and modeling perspective

Can extreme climatic and bioclimatic indices reproduce soy and maize yields in Latin America? Part 1: an observational and modeling perspective

A Study of Methods for Predicting and Controlling Blood Glucose in Diabetic Patients

With the development of society and the improvement of people's living standard, diabetes has gradually become one of the world public health problems that endanger human life safety and affect the development of global economy. Type 1 diabetes is a chronic metabolic disorder that prevents the pancreas from producing insulin and requires lifelong treatment with daily insulin injections to prevent high blood sugar. The lack of insulin leads to the continuous high level of blood sugar in patients. The most important thing for patients is to monitor blood sugar changes and control blood sugar within the normal range. In this paper, a systematic literature search is carried out to study these two aspects, and key information such as the learning model adopted in the literature, the main results, the development of relevant technologies, and the limitations are summarized. From the perspective of glucose-insulin prediction model, due to the complex structure of physiological model, many parameters and difficult to identify, most of the modeling methods are data-driven. There is a great room for improvement in the research on how to mine and utilize existing models to effectively establish accurate glucose prediction models for different objects. From the perspective of control theory, after insulin injection, there is a certain delay in the reduction of blood glucose concentration, and the onset time is different depending on the injection site. The rapid development of deep learning and the increase in available data offer the possibility of addressing these challenges in the near future. When designing closed-loop glucose algorithms, consider using a variety of approaches to establish a personalized glucose control algorithm for each diabetic patient.

Current State of Advances in Quantification and Modeling of Hydrological Droughts

Hydrological droughts may be referred to as sustained and regionally extensive water shortages as reflected in streamflows that are noticeable and gauged worldwide. Hydrological droughts are largely analyzed using the truncation level approach to represent the desired flow condition such as the median, mean, or any other flow quantile of an annual, monthly, or weekly flow sequence. The quantification of hydrologic droughts is accomplished through indices, such as the standardized streamflow index (SSI) in tandem with the standardized precipitation index (SPI) commonly used in meteorological droughts. The runs of deficits in the SSI sequence below the truncation level are treated as drought episodes, and thus, the theory of runs forms an essential tool for analysis. The parameters of significance from the modeling perspective of hydrological droughts (or tantamount to streamflow droughts in this paper) are the longest duration and the largest magnitude over a desired return period of T-year (or month or week) of the streamflow sequences. It is to be stressed that the magnitude component of the hydrological drought is of paramount importance for the design and operation of water resource storage systems such as reservoirs. The time scales chosen for the hydrologic drought analysis range from daily to annual, but for most applications, a monthly scale is deemed appropriate. For modeling the aforesaid parameters, several methodologies are in vogue, i.e., the empirical fitting of the historical drought sequences through a known probability density function (pdf), extreme number theorem, Markov chain analysis, log-linear, copulas, entropy-based analyses, and machine learning (ML)-based methods such as artificial neural networks (ANN), wavelet transform (WT), support vector machines (SVM), adaptive neuro-fuzzy inference systems (ANFIS), and hybrid methods involving entropy, copulas, and machine learning-based methods. The forecasting of the hydrologic drought is rigorously conducted through machine learning-based methodologies. However, the traditional stochastic methods such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), copulas, and entropy-based methods are still popular. New techniques for flow simulation are based on copula and entropy-based concepts and machine learning methodologies such as ANN, WT, SVM, etc. The simulated flows could be used for deriving drought parameters in consonance with traditional Monte Carlo methods of data generation. Efforts are underway to use hydrologic drought models for reservoir sizing across rivers. The ML methods whilst combined in the hybrid form hold promise in drought forecasting for better management of existing water resources during the drought periods. Data mining and pre-processing techniques are expected to play a significant role in hydrologic drought modeling and forecasting in future.

Exploring data quality and seasonal variations of N2O in wastewater treatment: a modeling perspective

Abstract In this work, operational data collected from four Danish wastewater treatment plants (WWTP) are assessed for quality issues and analyzed to investigate the feasibility of data-driven modeling for control purposes. All plants have permanent N2O sensors installed in the biological reactors, and N2O data are collected on the same terms as other operational data. We present and deploy a six-dimensional data quality assessment to the operational data evaluating (1) relevance, (2) accuracy, (3) completeness, (4) consistency, (5) comparability, and (6) accessibility. To increase the accuracy and completeness of the stored data, it is suggested that future initiatives are taken toward the collection and storing of metadata in WWTPs. Furthermore, seasonal variations and time-varying relationships between N2O, nitrogenous variables, and oxygen are investigated and compared across various case plants and process designs. Results show that the quality of the operational data varies substantially between plants. The investigation of time-varying interrelation between N2O and nitrogenous variables showed no clear pattern within or across different case plants. Furthermore, it is recommended that future research should consider adapting models so that more influence is linked to reliable measurements, contrary to assuming that all variables are of equal quality.

Examining avascular tumour growth dynamics: A variable-order non-local modelling perspective

This study investigates the growth of an avascular tumour described through the interchange of mass among its constituents and the production of inelastic distortions induced by growth itself. A key contribution of this research examines the role of non-local diffusion arising from the complex and heterogeneous tumour micro-environment. In our context, the non-local diffusion is enhanced by a variable-order fractional operator that incorporates crucial information about regions of limited nutrient availability within the tissue. Our research also focuses on the identification of an evolution law for the growth-induced inelastic distortions recast through the identification of non-conventional forces dual to suitable kinematic descriptors associated with the growth tensor. The establishment of such evolution law stems from examining the dissipation inequality and subsequently determining a posteriori connections between the inelastic distortions and the source/sink terms in the mass balance laws. To gain insights into the dynamics of tumour growth and its response to the proposed modelling framework, we first study how the variables governing the tissue evolution are affected by the introduction of the new growth law. Second, we investigate how regions of limited diffusion within the tumour, encoded into a fractional operator of variable-order, influence its growth.

Лингвокреативность как основа образования полисинтетических номинаций в англоязычном медиадискурсе

The paper is dedicated to the analysis of the phenomenon of linguistic creativity. By analyzing theoretical material related to linguistic creativity, the author emphasizes the close relationship between concepts such as word formation and linguistic creativity. The author also examines the creation of new linguistic creative units from the perspective of an anthropocentric model, where the subject is at the center. According to this model, new occasional (complex authorial) units possess a range of characteristics. The subject, utilizing complex nominative (polysynthetic) structures in the speech, such as word-phrases and word-sentences, as tools for constructing/perceiving reality and personal individualization, uses the creative potential of language. The research aim is to identify the linguistic creative nature of polysynthetic nominations in English language media discourse. The scientific novelty of this research lies in the development of an algorithm for linguistic (lexical) analysis aimed at a thorough scientific description of polysynthetic nominations and the identification of their integral and differential features. The scientific description revealed that a significant portion of the identified polysynthetic nominations in the English segment of the Internet is not documented in dictionaries. As a result, the linguistic creative nature of complex-syntactic (polysynthetic) nominations, which have become an integral part of the language system due to the active development of information and communication technologies, is uncovered.

Nanocomposite ceramic membranes as novel tools for remediation of textile dye waste water – A review of current applications, machine learning based modeling and future perspectives

Textile effluent treatment has gained significant attention due to the carcinogenic effects of the dye pollutants present and their enhanced resistance to degradation. Porous ceramic membranes have gained growing interest for dye remediation due to various unique characteristics, including resistance to fungal invasion, adverse chemical conditions, and high temperature. The current study explores applications of ceramic membrane technology, including membranes like MF, UF, and NF, for dye remediation from textile effluent. The best-performing nanocomposite membranes for eliminating azo dyes from textile effluent and efficiencies achieved are reported as Clay-alumina (99%), yttria-stabilized-zirconia (99%), nano TiO2-bentonite UF (95%), and tight TiO2 UF (100%). Ceramic nanocomposites with TiO2 and Al2O3 active layer NF (99%), Ceramic Hybrid hollow fiber LNF (99.3%). Loose nanofiltration (LNF) ceramic membranes have demonstrated outstanding targeted separation performance for the separation of dyes, making them affirm the effective recoveries and recurrent use of high-value-added constituents amongst all the membrane approaches examined. The potential of ceramic membranes for future research and development, including membrane production and mechanism, are discussed. In addition, the implementation of AI-based methods and Machine learning (ML) algorithms are discussed in relation to predicting membrane filtration performance and fouling tendencies and dye removal processes, maximizing opportunities in this field.

Effects of Various Heavy Metal Exposures on Insulin Resistance in Non-diabetic Populations: Interpretability Analysis from Machine Learning Modeling Perspective.

Increasing and compelling evidence has been proved that heavy metal exposure is involved in the development of insulin resistance (IR). We trained an interpretable predictive machine learning (ML) model for IR in the non-diabetic populations based on levels of heavy metal exposure. A total of 4354 participants from the NHANES (2003-2020) with complete information were randomly divided into a training set and a test set. Twelve ML algorithms, including random forest (RF), XGBoost (XGB), logistic regression (LR), GaussianNB (GNB), ridge regression (RR), support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), AdaBoost (AB), Gradient Boosting Decision Tree (GBDT), Voting Classifier (VC), and K-Nearest Neighbour (KNN), were constructed for IR prediction using the training set. Among these models, the RF algorithm had the best predictive performance, showing an accuracy of 80.14%, an AUC of 0.856, and an F1 score of 0.74 in the test set. We embedded three interpretable methods, the permutation feature importance analysis, partial dependence plot (PDP), and Shapley additive explanations (SHAP) in RF model for model interpretation. Urinary Ba, urinary Mo, blood Pb, and blood Cd levels were identified as the main influencers of IR. Within a specific range, urinary Ba (0.56-3.56µg/L) and urinary Mo (1.06-20.25µg/L) levels exhibited the most pronounced upwards trend with the risk of IR, while blood Pb (0.05-2.81µg/dL) and blood Cd (0.24-0.65µg/L) levels showed a declining trend with IR. The findings on the synergistic effects demonstrated that controlling urinary Ba levels might be more crucial for the management of IR. The SHAP decision plot offered personalized care for IR based on heavy metal control. In conclusion, by utilizing interpretable ML approaches, we emphasize the predictive value of heavy metals for IR, especially Ba, Mo, Pb, and Cd.

Polymorphs of Copper Zinc Tin Sulfide: Optoelectronic Properties and Detection Using Raman

Despite its growing popularity as a solar material, there is no comprehensive reference to aid with determining various polymorphs of copper zinc tin sulfide (CZTS) in the lab. Understanding the diverse electronic, optical and vibrational properties of these polymorphs is crucial to further enhance the conversion efficiency of CZTS solar cells. Here, findings are presented that suggest the effective hole mass in a monoclinic polymorph, that is likely to form along with the more commonly reported tetragonal variations, is about 10 times heavier than that of its tetragonal counterparts, thereby affecting key attributes such as carrier transport and concentration. Among tetragonal polymorphs, the so‐called primitive mixed CuAu (PMCA) arrangement is likely to undergo a direct‐indirect gap transition due to internal strains. Optical absorption between different polymorphs could also vary by up to 90% in the visible spectrum. Finally, accurate Raman footprint of each polymorph is predicted to help establish a definitive method for their detection. From a modeling perspective, Becke‐Lee‐Yang‐Parr with spin‐orbit coupling is shown to be a superior yet 100X faster alternative to hybrid functionals when applied to studying electronic structure of CZTS.

Investigation of the Electrokinetic Properties of HIV-Based Virus-Like Particles.

The antigen density on the surface of HIV-based virus-like particles (VLPs) plays a crucial role in the improvement of HIV vaccine potency. HIV VLPs consist of a dense protein core, which is surrounded by a lipid bilayer and whose surface is usually decorated with antigenic glycoproteins. The successful downstream processing of these particles is challenging, and the high-resolution and cost-efficient purification of HIV-based VLPs has not yet been achieved. Chromatography, one of the major unit operations involved in HIV VLP purification strategies, is usually carried out by means of ion exchangers or ion-exchange membranes. Understanding the electrokinetic behavior of HIV-based VLPs may help to improve the adjustment and efficiency of the corresponding chromatographic processes. In this study, we investigated the electrokinetics and aggregation of both undecorated and decorated VLPs and interpreted the data from the perspective of the soft particle model developed by Ohshima (OSPM), which fails to fully predict the behavior of the studied VLPs. Post-Ohshima literature, and particularly the soft multilayer particle model developed by Langlet et al., provides an alternative theoretical framework to overcome the limits of the OSPM. We finally hypothesized that the electrophoretic mobility of HIV-based VLPs is controlled by an electrohydrodynamic interplay between envelope glycoproteins, lipid bilayer, and Gag envelope.

Exploring Trans Effect Concept in Pt(II) Complexes through the Quantum Theory of Atoms in Molecules and Chemical Bond Overlap Model Perspectives

AbstractThis study utilizes Density Functional Theory (DFT) alongside the Chemical Bond Overlap (OP) Model and Quantum Theory of Atoms in Molecules (QTAIM) to reinterpret the well‐established trans effect in square‐planar Pt(II) complexes. Investigating ligand exchange mechanisms in trans‐[()T] (T = , , , , CO, ) via transition state localization and intrinsic reaction coordinate calculations, overlap descriptors (OP/TOP) such as density, repulsion, and polarizability are computed for Pt−T and Pt−L bonds for reactants and transition states. Through OP/TOP and QTAIM, key descriptors correlating are identified with the trans‐directing effectiveness of ligands, revealing higher electron density donation and more electron‐rich bonds in stronger trans‐directing ligands. This combined methodology offers insights into ligand trans‐directing character, enhancing understanding of their reactivity and bonding behavior.

Are vehicle tires major contributors to microplastic emissions into the China seas? A simple model perspective

Microplastics pose a substantial threat to our environment. Given China's large population and rapid economic growth, it is urgent to estimate the annual emissions of microplastics into its marine environment. The microplastics show a significant variation in their source emissions as well as in their physical and chemical properties, leading to differences in their transport and fate in aquatic environments. To account for these variations, we developed a process-oriented model that considers microplastics from different sources and the inter-provincial variation in their retention rate to assess annual microplastic emissions into the China seas. On a national scale, of the microplastics emitted, 36.05 % are from household laundry activities, 27.26 % are from the wear and tear of vehicle tires, and 24.04 % are from the abrasion of plastic household items. After emission, 60.21 % are removed by wastewater treatment plants. The overall proportion of microplastics that end up in the marine environment highly depends on the specific riverine retention rate of microplastics from vehicle tires. Including the high settling rate of these microplastics, this proportion drops from 9.96 % to 3.29 %, rendering vehicle tires a minor contributor to microplastic emissions into the China seas compared to other sources. Moreover, when using the density-dependent approach and considering the east/west dimension of each province, the microplastic emissions from vehicle tires into the China seas decrease from 71 % to 5.27 %. This underscores the urgent need for global and regional models to account for the detailed riverine transport process of microplastics from vehicle tires in order to enhance the accuracy of their emission estimates into coastal waters.

A systematic integrative review of specialized nurses' role to establish a culture of patient safety: A modelling perspective.

To understand specialized nurses' role in the culture of patient safety and their ability to promote and enforce it within healthcare. A systematic integrative review using the approach of Whittemore and Knafl. Systematic literature search for qualitative, quantitative and mixed-methods studies, followed by data evaluation, quality assessment, analysis and research synthesis with a narrative perspective. Findings were contextualized within a 'framework for understanding the development of patient safety culture'. Searches were conducted in PubMed [including MEDLINE], Scopus, CINAHL, Web of Science and EMBASE from Jan 2013 until Sep 2023. Sixteen studies published in English from six different countries were selected and used for research synthesis. Diverse enabling factors and enacting behaviours influencing specialized nurses' roles to promote patient safety culture were identified, mainly focusing on nurses' workload, professional experiences and organizational commitment. Patient safety outcomes focused on medication management, infection prevention, surveillance process in critical care, oversight on quality and safety of nurses' practice, patient care management, continuity of care, adherence to the treatment plan and implementation of a specialized therapeutic procedure. Specialized nurses can make a significant contribution to promote patient safety culture and support organizational initiatives to prevent adverse events. Stronger participation and leadership of specialized nurses in initiatives to improve patient safety culture requires appropriate investments and support by policy makers and managers in terms of resources and training. There is a gap in existing literature on the contribution that specialized nurses can make in promoting patient safety culture. Review results highlight the importance of interprofessional collaboration and teamwork by involving specialized nurses. They inform healthcare policy makers about recognizing their roles and competencies in patient safety culture. Preferred reporting items for systematic reviews and meta-analysis. No patient or public contribution.

Data-driven smoothing approaches for interest modeling in recommendation systems

In recommendation systems, users often click on some items that are distinct from historically clicked items. This verifies the existence of interest gaps between the historical interests reflected by historical behaviors and current interests. The gaps pose key challenges for interest modeling in recommendation systems, i.e., learn user current interests from historical behaviors. To bridge interest gaps, various solutions have been proposed from a model perspective, and mainly focus on capturing interest diversity and interest evolution process (the changes in user interests over time). Different from existing solutions, our key idea is smoothing the historical interests from a data perspective, so that interest gaps can be smoothed and bridged. To realize this idea, we propose two types of data-driven smoothing approaches based on the principle of collaborative filtering, i.e., click-centric smoothing and user-centric smoothing. The approaches can be easily applied to a number of existing solutions and enhance their performances. To ensure that the smoothed interests completely and accurately cover user current interests (coverage and accuracy), the click-centric smoothing leverages the relevant clicks of a click to smooth the interests on the click, and the user-centric smoothing leverages the relevant clicks of a user click sequence to smooth the interests learned from the click sequence. The “smooth” is to enhance the coverage, and “relevant” is to enhance the accuracy. To search the relevant clicks, we develop an iterative Bayesian inference over a click-flow graph, i.e., a new implementation of collaborative filtering. To further enhance the accuracy, we propose new constrained attention networks to generate high-quality smoothing factors from these relevant clicks. Both offline and online experiments show the data-driven smoothing approaches significantly outperform the baselines. The user-centric smoothing has been deployed on the e-commerce platform of Meituan and achieved 1.51% elevation of Click-through & Conversion Rate and 0.53% elevation of Click-through Rate.

Реализация поэтического дискурса в рекреативной фазе перевода (на примере перевода стихотворения Сергея Гонцова «В красном тереме» на английский язык)

The study aims to determine the ways of implementing some factors of discursivity, namely cultural-historical and psychological factors, in the process of poetic translation based on Sergei Gontsov’s poem “In a red tower-room”. The paper examines the views on poetic translation as a meaning-extracting and meaning-forming process. The study emphasizes the expediency of highlighting the recreational phase, during which the translator first becomes the recipient and decoder of the author’s idea, then encodes the mastered material by means of another language. Special attention is paid to the notions of discourse and a communicative block, the possibility of correlating them is considered. Using Gontsov’s poem “In a red tower-room” as an example, a sample of dividing the poem into communicative blocks is given, along with predictions of the reader’s questions for each block and ways of implementing these discursive factors when translating the poem into English. The scientific novelty lies in the fact that translations of S. A. Gontsov’s poems have not been published to date, the paper is the first to present an English-language version of this poem and offer an analysis of the stylistic devices used in the rendering of the original from the perspective of the semio-discursive model of translation. As a result, it was possible to identify the means by which cultural-historical and psychological discursive factors are expressed in the original poem and the means by which they are projected in the English text.

Work stress, work-family conflict, and psychological distress among resort employees: a JD-R model and spillover theory perspectives.

The hospitality industry is well-known for its challenging and high-pressure work settings. In this context, employees commonly face a multitude of stressors originating from their roles and job responsibilities, which can significantly impact their psychological wellbeing. Hence, based on the job demands-resources (JD-R) model and the spillover theory, this study aims to empirically explore the direct and indirect effect of work stress (assessed by role overload, ambiguity, and conflict) on psychological distress among frontline employees in 3- and 4-star Egyptian resorts while considering the mediating influence of work-family conflict (WFC). Four hypotheses were put to the test through the application of the PLS-SEM 4.0 version (4.0.9.9). Based on the findings from 563 frontline employees who participated in this research, the study supports the four hypotheses affirming that work-related stressors significantly contributed to employees' psychological distress. Further, the findings highlighted that these stressors significantly spill over into employees' family lives, generating conflicts between work and family roles. In addition, the results emphasized the significance of WFC as a contributing factor to employees' psychological distress. Finally, the study concluded that WFC partially mediates the link between work stress and employees' psychological distress. Based on these findings, some theoretical and practical implications for hospitality scholars, resort management, and policymakers were suggested to enhance the employees' wellbeing and mitigate psychological distress in this vital sector.

The Roles of Sales Technologies for Salespeople: Techno Demands and Resources Model Perspective

The purpose of this study is to examine the effects of a salesperson’s techno-demands and techno-resources created by new sales-related information technology on salespersons’ attitudinal and behavioral outcomes such as job burnout, job satisfaction, turnover intention, and sales performance. In order to test the proposed framework, data were collected from 305 salespeople in Korea. The results of a partial least squared structural equation modeling (PLS-SEM) analysis showed that techno-demands have a significant positive effect on salespeople’s job burnout and techno-resources have a significant positive effect on salespeople’s job satisfaction. Salespeople’s job burnout has a significant positive effect on salespeople’s turnover intention, whereas salespeople’s job satisfaction has a significant positive effect on salespeople’s sales performance. Finally, salespeople’s job satisfaction has a negative effect on turnover intention. Theoretically, this study develops a new comprehensive framework of the techno demands–resources model and is empirically tested in the context of salespeople. Managerially, the findings offer important insights to practitioners to leverage techno-resources to accelerate the sales technologies for sales activities.

From use cases to business cases: I-GReta use cases portfolio analysis from innovation management and digital entrepreneurship models perspectives

This study provides a detailed exploration of how innovation management and digital entrepreneurship models can help transform technical use cases in smart grid contexts into viable business cases, thereby bridging the gap between technical potential and market application in the field of energy informatics. It focuses on the I-GReta project Use Cases (UCs). The study employs methodologies like Use Case Analysis, Portfolio Mapping of Innovation Level, Innovation Readiness Level, and the Tech Solution Business Model Canvas (TSBMC) to analyse and transition from technical use cases to viable business cases. This approach aligns technological solutions with market demands and regulatory frameworks, leveraging digital entrepreneurship models to navigate market challenges and foster energy management, sustainability, and digitalization.

Kaempferol Improves Cardiolipin and ATP in Hepatic Cells: A Cellular Model Perspective in the Context of Metabolic Dysfunction-Associated Steatotic Liver Disease.

Targeting mitochondrial function is a promising approach to prevent metabolic dysfunction-associated steatotic liver disease (MASLD). Cardiolipin (CL) is a unique lipid comprising four fatty acyl chains localized in the mitochondrial inner membrane. CL is a crucial phospholipid in mitochondrial function, and MASLD exhibits CL-related anomalies. Kaempferol (KMP), a natural flavonoid, has hepatoprotective and mitochondrial function-improving effects; however, its influence on CL metabolism in fatty liver conditions is unknown. In this study, we investigated the effects of KMP on mitochondrial function, focusing on CL metabolism in a fatty liver cell model (linoleic-acid-loaded C3A cell). KMP promoted mitochondrial respiratory functions such as ATP production, basal respiration, and proton leak. KMP also increased the gene expression levels of CPT1A and PPARGC1A, which are involved in mitochondrial β-oxidation. Comprehensive quantification of CL species and related molecules via liquid chromatography/mass spectrometry showed that KMP increased not only total CL content but also CL72:8, which strongly favors ATP production. Furthermore, KMP improved the monolysocardiolipin (MLCL)/CL ratio, an indicator of mitochondrial function. Our results suggest that KMP promotes energy production in a fatty liver cell model, associated with improvement in mitochondrial CL profile, and can serve as a potential nutrition factor in preventing MASLD.

Within-host bayesian joint modeling of longitudinal and time-to-event data of Leishmania infection.

The host immune system plays a significant role in managing and clearing pathogen material during an infection, but this complex process presents numerous challenges from a modeling perspective. There are many mathematical and statistical models for these kinds of processes that take into account a wide range of events that happen within the host. In this work, we present a Bayesian joint model of longitudinal and time-to-event data of Leishmania infection that considers the interplay between key drivers of the disease process: pathogen load, antibody level, and disease. The longitudinal model also considers approximate inflammatory and regulatory immune factors. In addition to measuring antibody levels produced by the immune system, we adapt data from CD4+ and CD8+ T cell proliferation, and expression of interleukin 10, interferon-gamma, and programmed cell death 1 as inflammatory or regulatory factors mediating the disease process. The model is developed using data collected from a cohort of dogs naturally exposed to Leishmania infantum. The cohort was chosen to start with healthy infected animals, and this is the majority of the data. The model also characterizes the relationship features of the longitudinal outcomes and time-to-death due to progressive Leishmania infection. In addition to describing the mechanisms causing disease progression and impacting the risk of death, we also present the model's ability to predict individual trajectories of Canine Leishmaniosis (CanL) progression. The within-host model structure we present here provides a way forward to address vital research questions regarding the understanding of the progression of complex chronic diseases such as Visceral Leishmaniasis, a parasitic disease causing significant morbidity worldwide.