Membrane fouling analysis of air-gap membrane distillation (AGMD) for recovery of water and removal of antibiotics from a model wastewater containing antibiotics and humic acid.

The study investigates the efficiency of air-gap membrane distillation (AGMD) in water recovery and antibiotics removal from wastewater, focusing on high-concentration scenarios. Experimental findings reveal enhanced membrane performance with increasing the feed temperature, resulting in vapor permeate fluxes of up to 5 kg/m2.h at higher temperatures. Despite experiencing flux reduction caused by fouling from humic acid (HA) in the feed antibiotics solution, the antibiotics consistently maintain near-complete rejection rates (>99%) over 48 hours. The foulant on the membrane surface was illustrated by SEM imaging. To know the temperature polarization and the fouling resistance, mathematical modeling was used, and it validates experimental results, elucidating temperature polarization effects and mass transfer coefficients. An increase in feed flow rates reduced thermal boundary layers, enhancing heat flux. Higher temperatures reduced HA fouling resistance. Therefore, AGMD proves effective in water recovery and antibiotics removal, with mathematical models aiding fouling understanding for future research and detailed computational fluid dynamics (CFD) models.

Drivergene.net: A Cytoscape app for the identification of driver nodes of large-scale complex networks and case studies in discovery of drug target genes.

There are no tools to identify driver nodes of large-scale networks in approach of competition-based controllability. This study proposed a novel method for this computation of large-scale networks. It implemented the method in a new Cytoscape plug-in app called Drivergene.net. Experiments of the software on large-scale biomolecular networks have shown outstanding speed and computing power. Interestingly, 86.67% of the top 10 driver nodes found on these networks are anticancer drug target genes that reside mostly at the innermost K-cores of the networks. Finally, compared method with those of five other researchers and confirmed that the proposed method outperforms the other methods on identification of anticancer drug target genes. Taken together, Drivergene.net is a reliable tool that efficiently detects not only drug target genes from biomolecular networks but also driver nodes of large-scale complex networks. Drivergene.net with a user manual and example datasets are available https://github.com/tinhpd/Drivergene.git.

Robustness and exploration between the interplay of the nonlinear co-dynamics HIV/AIDS and pneumonia model via fractional differential operators and a probabilistic approach.

In this article, we considered a nonlinear compartmental mathematical model that assesses the effect of treatment on the dynamics of HIV/AIDS and pneumonia (H/A-P) co-infection in a human population at different infection stages. Understanding the complexities of co-dynamics is now critically necessary as a consequence. The aim of this research is to construct a co-infection model of H/A-P in the context of fractional calculus operators, white noise and probability density functions, employing a rigorous biological investigation. By exhibiting that the system possesses non-negative and bounded global outcomes, it is shown that the approach is both mathematically and biologically practicable. The required conditions are derived, guaranteeing the eradication of the infection. Furthermore, adequate prerequisites are established, and the configuration is tested for the existence of an ergodic stationary distribution. For discovering the system's long-term behavior, a deterministic-probabilistic technique for modeling is designed and operated in MATLAB. By employing an extensive review, we hope that the previously mentioned approach improves and leads to mitigating the two diseases and their co-infections by examining a variety of behavioral trends, such as transitions to unpredictable procedures. In addition, the piecewise differential strategies are being outlined as having promising potential for scholars in a range of contexts because they empower them to include particular characteristics across multiple time frame phases. Such formulas can be strengthened via classical techniques, power law, exponential decay, generalized Mittag-Leffler kernels, probability density functions and random procedures. Furthermore, we get an accurate description of the probability density function encircling a quasi-equilibrium point if the effect of H/A-P minimizes the propagation of the co-dynamics. Consequently, scholars can obtain better outcomes when analyzing facts using random perturbations by implementing these strategies for challenging issues. Random perturbations in H/A-P co-infection are crucial in controlling the spread of an epidemic whenever the suggested circulation is steady and the amount of infection eliminated is closely correlated with the random perturbation level.

Relationship of various COVID-19 antibody titer with individual characteristics and prediction of future epidemic trend in Xiamen City, China.

Background: Reinfection of coronavirus disease 2019 (COVID-19) has raised concerns about how reliable immunity from infection and vaccination is. With mass testing for the virus halted, understanding the current prevalence of COVID-19 is crucial. This study investigated 1,191 public health workers at the Xiamen Center for Disease Control, focusing on changes in antibody titers and their relationship with individual characteristics. Methods: The study began by describing the epidemiological characteristics of the study participants. Multilinear regression (MLR) models were employed to explore the associations between individual attributes and antibody titers. Additionally, group-based trajectory models (GBTMs) were utilized to identify trajectories in antibody titer changes. To predict and simulate future epidemic trends and examine the correlation of antibody decay with epidemics, a high-dimensional transmission dynamics model was constructed. Results: Analysis of epidemiological characteristics revealed significant differences in vaccination status between infected and non-infected groups (χ2=376.706, P<0.05). However, the distribution of antibody titers among the infected and vaccinated populations was not significantly different. The MLR model identified age as a common factor affecting titers of immunoglobulin G (IgG), immunoglobulin M (IgM), and neutralizing antibody (NAb), while other factors showed varying impacts. History of pulmonary disease and hospitalization influenced IgG titer, and factors such as gender, smoking, family history of pulmonary diseases, and hospitalization impacted NAb titers. Age was the sole determinant of IgM titers in this study. GBTM analysis indicated a "gradual decline type" trajectory for IgG (95.65%), while IgM and NAb titers remained stable over the study period. The high-dimensional transmission dynamics model predicted and simulated peak epidemic periods in Xiamen City, which correlated with IgG decay. Age-group-specific simulations revealed a higher incidence and infection rate among individuals aged 30-39 years during both the second and third peaks, followed by those aged 40-49, 50-59, 18-29, and 70-79 years. Conclusions: Our study shows that antibody titer could be influenced by age, previous pulmonary diseases as well as smoking. Furthermore, the decline in IgG titers is consistent with epidemic trends. These findings emphasize the need for further exploration of these factors and the development of optimized self-protection countermeasures against reinfection.

Does a GP service package matter in addressing the absence of health management by the occupational population? A modelling study.

OBJECTIVE: To assess the influence of supply and demand factors on the contract behavior of occupational populations with general practitioner (GP) teams. METHODS: We employed a system dynamics approach to assess and predict the effect of the general practitioner service package (GPSP) and complementary incentive policies on the contract rate for 2015-2030. First, the GPSP is designed to address the unique needs of occupational populations, enhancing the attractiveness of GP contracting services, including three personalized service contents tailored to demand-side considerations: work-related disease prevention (WDP), health education & counseling (HEC), and health-care service (HCS). Second, the complementary incentive policies on the supply-side included income incentives (II), job title promotion (JTP), and education & training (ET). Considering the team collaboration, the income distribution ratio (IDR) was also incorporated into supply-side factors. FINDINGS: The contract rate is predicted to increase to 57.8% by 2030 after the GPSP intervention, representing a 15.4% increase on the non-intervention scenario. WDP and HEC have a slightly higher (by 2%) impact on the contract rate than that from HCS. Regarding the supply-side policies, II have a more significant impact on the contract rate than JTP and ET by 3-5%. The maximum predicted contract rate of 75.2% is expected by 2030 when the IDR is 0.5, i.e., the GP receives 50% of the contract income and other members share 50%. CONCLUSION: The GP service package favorably increased the contract rate among occupational population, particularly after integrating the incentive policies. Specifically, for a given demand level, the targeted content of the package enhanced the attractiveness of contract services. On the supply side, the incentive policies boost GPs' motivation, and the income distribution motivated other team members.

Incorporating Intracellular Processes in Virus Dynamics Models.

In-host models have been essential for understanding the dynamics of virus infection inside an infected individual. When used together with biological data, they provide insight into viral life cycle, intracellular and cellular virus-host interactions, and the role, efficacy, and mode of action of therapeutics. In this review, we present the standard model of virus dynamics and highlight situations where added model complexity accounting for intracellular processes is needed. We present several examples from acute and chronic viral infections where such inclusion in explicit and implicit manner has led to improvement in parameter estimates, unification of conclusions, guidance for targeted therapeutics, and crossover among model systems. We also discuss trade-offs between model realism and predictive power and highlight the need of increased data collection at finer scale of resolution to better validate complex models.

Preparation and Application of Degradable Lignin/Poly (Vinyl Alcohol) Polymers as Urea Slow-Release Coating Materials.

The massive amount of water-soluble urea used leads to nutrient loss and environmental pollution in both water and soil. The aim of this study was to develop a novel lignin-based slow-release envelope material that has essential nitrogen and sulfur elements for plants. After the amination reaction with a hydrolysate of yak hair keratin, the coating formulation was obtained by adding different loadings (2, 5, 8, 14 wt%) of aminated lignin (AL) to 5% polyvinyl alcohol (PVA) solution. These formulations were cast into films and characterized for their structure, thermal stability, and mechanical and physicochemical properties. The results showed that the PVA-AL (8%) formulation had good physical and chemical properties in terms of water absorption and mechanical properties, and it showed good degradation in soil with 51% weight loss after 45 days. It is suitable for use as a coating material for fertilizers. Through high-pressure spraying technology, enveloped urea particles with a PVA-AL (8%) solution were obtained, which showed good morphology and slow-release performance. Compared with urea, the highest urea release was only 96.4% after 30 days, conforming to Higuchi model, Ritger-Peppas model, and second-order dynamic model. The continuous nitrogen supply of PVA-AL coated urea to Brassica napus was verified by potting experiments. Therefore, the lignin-based composite can be used as a coating material to produce a new slow-release nitrogen fertilizer for sustainable crop production.

Research on the security of natural gas supply and demand in China under the dual carbon target.

The security of natural gas supply is not only an important part of China's energy security, it also serves as a basic guarantee for China to achieve its dual carbon target and energy transition. Therefore, it is very important to conduct research on the security of China's natural gas supply and demand in the context of the dual carbon target. This paper develops a system dynamics (SD) model for natural gas demand forecasting and a generalized Weng's model for production forecasting to predict China's natural gas demand and production under different scenarios during 2022-2060, and then analyzes China's natural gas supply and demand situation and potential import and external dependence based on the forecast results. The simulation results show that (1) under the two demand scenarios D1 and D2, China's natural gas demand will peak at 766.02 billion m3 in 2046 and 708.07 billion m3 in 2036 and decline to 521.65 billion m3 and 278.99 billion m3 in 2060 respectively; (2) under the two production scenarios S1 and S2, China's natural gas production will peak at 344.581 billion m3 in 2042 and 366.341 billion m3 in 2043 and decrease to about 250 billion m3 in 2060; (3) before 2035, the security of natural gas supply in China will face a challenging situation, the total volume of potential gas imports will gradually increase to about 350 billion m3, and China's dependence on natural gas imports will exceed 50%; after 2035, the progress of China's energy transition will improve the security of its natural gas supply. This paper proposes four recommendations for expanding gas demand in the near to medium term, promoting conventional and unconventional gas production, diversifying import channels and building emergency reserves to ensure China's gas supply security and enable gas to play a "bridging" role in the energy transition.

Bridging responses to a human telomerase reverse transcriptase-based peptide cancer vaccine candidate in a mechanism-based model.

Therapeutic cancer vaccines are novel immuno-therapeutics, aiming to improve clinical outcomes with other immunotherapies. However, obstacles to their successful clinical development remain, which model-informed drug development approaches may address. UV1 is a telomerase based therapeutic cancer vaccine candidate being investigated in phase I clinical trials for multiple indications. We developed a mechanism-based model structure, using a nonlinear mixed-effects modeling techniques, based on longitudinal tumor sizes (sum of the longest diameters, SLD), UV1-specific immunological assessment (stimulation index, SI) and overall survival (OS) data obtained from a UV1 phase I trial including non-small cell lung cancer (NSCLC) patients and a phase I/IIa trial including malignant melanoma (MM) patients. The final structure comprised a mechanistic tumor growth dynamics (TGD) model, a model describing the probability of observing a UV1-specific immune response (SI ≥ 3) and a time-to-event model for OS. The mechanistic TGD model accounted for the interplay between the vaccine peptides, immune system and tumor. The model-predicted UV1-specific effector CD4+ T cells induced tumor shrinkage with half-lives of 103 and 154 days in NSCLC and MM patients, respectively. The probability of observing a UV1-specific immune response was mainly driven by the model-predicted UV1-specific effector and memory CD4+ T cells. A high baseline SLD and a high relative increase from nadir were identified as main predictors for a reduced OS in NSCLC and MM patients, respectively. Our model predictions highlighted that additional maintenance doses, i.e. UV1 administration for longer periods, may result in more sustained tumor size shrinkage.

Dynamic simulation of land use change and habitat quality assessment under climate change scenarios in Nanchang, China.

Investigating habitat quality under different climate scenarios holds significant importance for sustainable land resource management and ecological conservation. In this study, considering Nanchang as a case study, a coupled patch-generating land use simulation (PLUS) and system dynamics (SD) model was employed in the simulation and prediction of land usage under shared socioeconomic pathway (SSP) and representative concentration pathway (RCP) scenarios. To assess the habitat quality in Nanchang from 2000 to 2020 and in 2030 under three diverse climate scenarios, we used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to analyze spatial and temporal changes. The findings indicate that the regions of forest land, cultivated land, and grassland in Nanchang City will dramatically decrease by 2030, the construction land will rapidly expand, and the fluctuations in the unutilized land and water area will be minimal. Additionally, the habitat quality declined from 2000 to 2020, and its spatial distributions changed. Zones having a high overall habitat quality were distributed in the mountains, hills, and lake areas, whereas those with relatively low quality were found in cultivated and urban areas. Under three climate scenarios, in 2030, the habitat quality index for Nanchang City will show a decreasing trend, mainly owing to areas with an index of 0.3-0.5 transitioning to <0.3. Considering each scenario, the degree of habitat degradation increased in the order SSP585>SSP245>SSP119. The findings of this study will inform high-quality development and biodiversity conservation in Nanchang City.

Development of a framework to forecast the urban residential building CO2 emission trend and reduction potential to 2060: A case study of Jiangxi province, China.

Investigating the CO2 abatement potential of urban residential building from systematic perspective is essential to reach the urban carbon neutrality target. However, previous studies on building CO2 emission trend forecasting were mainly focused on the building operational phase. In this study, a new framework that includes four building stages under a system dynamic model is developed to simulate urban residential building carbon emission changes and the related reduction potentials under three scenarios in Jiangxi Province up to 2060. Results showed that the overall process carbon emission dynamic had already peaked in 2014 under the three scenarios, with a peak value of 38.52 Mt. It then fell to 9.56 Mt in 2060 under the baseline (BAU) scenario. More importantly, seven carbon abatement measures were adopted during four building activities in this study, and the total carbon reduction was not the sum of the carbon reduction potential of the individual measures. Some carbon abatement strategies displayed synergistic effects such as low-carbon electrification where the combination of electrification and clean energy power generation was the largest contributor to reduced carbon emissions during building operation as a comprehensive carbon reduction measure. By contrast, extending a building's lifetime restrained the carbon abatement potential during the demolition stage, and it inhibited the carbon emission reduction by 24.84 Mt. These results highlight the significant need for effective policy interventions for clean production and the need to improve prefabricated building proportions, promote electrification, improve energy efficiency, strengthen recycling practices, and extend building lifetimes to promote decarbonization of urban residential building system development.

Combined control algorithm based on synchronous reinforcement learning for a self-balancing bicycle robot.

In this paper, balance control of a bicycle robot is studied without either a trail or a mechanical regulator when the robot moves in an approximately rectilinear motion. Based on the principle of moment balance, an input nonaffine nonlinear dynamics model of the bicycle robot is established. A driving velocity condition is proposed to maintain the robot balance. The nonaffine nonlinear system is transformed into an affine nonlinear system by defining the equivalent control. Subsequently, a feedback linearization controller is designed for the equivalent control. We design a combined control algorithm of synchronous policy iteration based on the actor-critic architecture. The actor neural network (NN) is designed based on the feedback linearization control law. Weight tuning laws for the critic and actor NNs are proposed. The system closed-loop stability and convergence of the NN weights are guaranteed based on the Lyapunov analysis. The optimality of the equivalent control policy is guaranteed. To satisfy the driving velocity condition, the values of the steering angle and driving velocity are determined based on the optimal equivalent control. The effectiveness of the proposed algorithm is verified through simulations and real experiments.

A System Dynamics Model of Caries Preventive Interventions in Thailand's School-Aged Population.

PURPOSE: To compare the long-term effects of the Ministry of Public Health's (MOPH) caries preventive interventions for 6- to 12-year-olds (supervised toothbrushing [STB], dental sealant, and combined STB+sealant) to the base case (no intervention) using the System Dynamics Model. MATERIALS AND METHODS: The System Dynamics Model was used to evaluate the intervention scenarios of supervised toothbrushing (STB), sealant, and combined STB+sealant with the base-case scenario. The effectiveness data for the model's interventions were obtained from systematic reviews and meta-analyses. RESULTS: The model determined that the caries-free population increased by 36.2%, 25.5%, and 14.5%, while the caries-affected population decreased by 8.1%, 5.5%, and 3.1% in the combined STB+sealant, sealant, and supervised toothbrushing scenarios compared to the base case at 15 years of age. CONCLUSION: Combined STB+sealant is the most efficacious intervention among those administered to children between the ages of 6 and 12 with permanent teeth. In addition, the System Dynamics Model could be helpful in comparing interventions or policies to determine the optimal intervention for a given population.

Integrated Controller Design and Application for CNC Machine Tool Servo Systems Based on Model Reference Adaptive Control and Adaptive Sliding Mode Control.

In order to reduce the effect of nonlinear friction and time-varying factors on the servo system of a computer numerical control (CNC) machine tool and improve its motion control accuracy, this paper uses an adaptive sliding mode control (ASMC) method based on model reference adaptive control (MRAC). The method adopts ASMC in the control outer loop and obtains the optimal control parameters by making the sliding mode control (SMC) law continuous and adaptively estimating the control parameters. At the same time, MRAC is used in the control inner loop to enhance the "invariance" of the controlled object so that the switching gain of SMC can satisfy the disturbance matching condition even under lesser conditions. Simulation and experimental results show that compared with the traditional SMC, the ASMC based on MRAC proposed in this paper effectively reduces the influence of nonlinear friction on the system performance, and the reduction in following error reaches 71.2%, which significantly improves the motion control accuracy of the control system. The spectral analysis of the following errors shows that the maximum magnitude reduction rate of the high-frequency chattering is 89.02%, which significantly reduces the effect of the high-frequency chattering and effectively improves the stability performance of the control system.

Curriculum-based humanoid robot identification using large-scale human motion database.

Identifying an accurate dynamics model remains challenging for humanoid robots. The difficulty is mainly due to the following two points. First, a good initial model is required to evaluate the feasibility of motions for data acquisition. Second, a highly nonlinear optimization problem needs to be solved to design movements to acquire the identification data. To cope with the first point, in this paper, we propose a curriculum of identification to gradually learn an accurate dynamics model from an unreliable initial model. For the second point, we propose using a large-scale human motion database to efficiently design the humanoid movements for the parameter identification. The contribution of our study is developing a humanoid identification method that does not require the good initial model and does not need to solve the highly nonlinear optimization problem. We showed that our curriculum-based approach was able to more efficiently identify humanoid model parameters than a method that just randomly picked reference motions for identification. We evaluated our proposed method in a simulation experiment and demonstrated that our curriculum was led to obtain a wide variety of motion data for efficient parameter estimation. Consequently, our approach successfully identified an accurate model of an 18-DoF, simulated upper-body humanoid robot.

Research on robust fuzzy logic sliding mode control of Two-DOF intelligent underwater manipulators.

This study investigates the independent motion control of a two-degree-of-freedom (Two-DOF) intelligent underwater manipulator. The dynamics model of two-DOF manipulators in an underwater environment is proposed by combining Lagrange's equation and Morison's empirical formulation. Disturbing factors such as water resistance moments, additional mass force moments and buoyancy forces on the intelligent underwater manipulator are calculated exactly. The influence of these factors on the trajectory tracking of the intelligent underwater manipulator is studied through simulation analysis. Based on the design of the sliding mode surface of the PID structure, a new Fuzzy-logic Sliding Mode Control (FSMC) algorithm is presented for the control error and control input chattering defects of traditional sliding mode control algorithm. The experimental simulation results show that the FSMC algorithm proposed in this study has a good effect in the elimination of tracking error and convergence speed, and has a great improvement in control accuracy and input stability.

Plasma SARS-CoV-2 RNA elimination and RAGE kinetics distinguish COVID-19 severity.

Objectives: Identifying biomarkers causing differential SARS-CoV-2 infection kinetics associated with severe COVID-19 is fundamental for effective diagnostics and therapeutic planning. Methods: In this work, we applied mathematical modelling to investigate the relationships between patient characteristics, plasma SARS-CoV-2 RNA dynamics and COVID-19 severity. Using a straightforward mathematical model of within-host viral kinetics, we estimated key model parameters from serial plasma viral RNA (vRNA) samples from 256 hospitalised COVID-19+ patients. Results: Our model predicted that clearance rates distinguish key differences in plasma vRNA kinetics and severe COVID-19. Moreover, our analyses revealed a strong correlation between plasma vRNA kinetics and plasma receptor for advanced glycation end products (RAGE) concentrations (a plasma biomarker of lung damage), collected in parallel to plasma vRNA from patients in our cohort, suggesting that RAGE can substitute for viral plasma shedding dynamics to prospectively classify seriously ill patients. Conclusion: Overall, our study identifies factors of COVID-19 severity, supports interventions to accelerate viral clearance and underlines the importance of mathematical modelling to better understand COVID-19.

Understanding the dynamics of the obesity transition associated with physical activity, sedentary lifestyle, and consumption of ultra-processed foods in Colombia.

OBJECTIVE: We investigate the obesity transition at the country- and regional-levels, by age, gender, and socioeconomic status (SES) and its relationship to three health behavior attributes, including physical activity (PA), sedentary activities (ST), and consumption of ultra-processed foods (CUPF) within the urban population of Colombia, from 20,010 to 2050. METHODS: The study is informed by cross-sectional data from ENSIN survey. We used these data to develop a system dynamics model that simulates the dynamics of obesity by body mass index (BMI) categories, gender, and SES. This model also uses a conservative co-flow structure for three health-related behaviors (PA, ST, and CUPF). RESULTS: At the national level, our results indicate that the burden of obesity is shifting towards populations with lower SES as the gross domestic product (GDP) increases, particularly women aged 20-59 years with lower SES. Among this group of women, the highest burden of obesity is among those who do not meet the PA, ST and CUPF recommendations. At the regional level, our findings suggest that the regions are at different stages in the obesity transition. CONCLUSIONS: The burden of obesity is shifting towards women with lower SES as GDP increases at the national level and across several regions. This obesity transition is paralleled by a high prevalence of women from low SES groups who do not meet the minimum recommendations for PA, CUPF, and ST. Our findings can be used by decision-makers to inform age- and SES- specific policies seeking to tackle the obesity.

Development of a behaviour pattern-based testing approach for coupled socioeconomic and environmental models.

Understanding the interactions between human and environmental systems is key to sustainable environmental management. Dynamically Coupled Socioeconomic system dynamics models integrated with physically-based Environmental Models (DCSEMs) are promising tools to appropriately capture the non-linear relationships between complex socioeconomic and biophysical systems, thereby supporting sustainable environmental management. However, existing approaches for testing integrated models are commonly based on the point-to-point analysis of model outputs, which is not suitable for DCSEMs that are behaviour pattern oriented. Consequently, the lack of well-defined behaviour pattern-based approaches has limited the adaptability of DCSEMs. To address this gap, this study proposes a novel behaviour pattern-based model testing approach that includes global sensitivity analysis (GSA), auto-calibration algorithms, and evaluation to assess behaviour pattern similarities between model outputs and real-world trends. The proposed approach is demonstrated through a real-world case study, in which an existing DCSEM is calibrated and evaluated to simulate water table depth in the Rechna Doab region of Pakistan. Compared to the conventional numerical point approach, the proposed approach is better suited for DCSEMs, as it replicates observed system behaviour patterns (as opposed to observed point values) over time. Furthermore, the outcomes of the Theil inequality statistical analysis and parameter distribution analysis provide evidence that the suggested approach is effective in testing and improving the performance of the DCSEM by capturing the spatial heterogeneity within the study area. The proposed behaviour-pattern testing procedure is a useful approach for model testing in data-limited, spatially-distributed DCSEMs.

Long-term Effectiveness of Integrated Caries Preventive Programs in Thailand Among Preschool Children Using a System Dynamics Model.

OBJECTIVE: To compare the effect of three caries preventive interventions provided by Thailand's Ministry of Public Health (MOPH): supervised toothbrushing (STB), fluoride varnish (FV), and combined STB+FV from 0 to 5 years old to the base case using the System Dynamics Model (SDM). METHODS: The SDM was developed to compare the intervention scenarios: STB, FV, and combined STB+FV with the base case scenario. Meta-analyses determined the effective rates of the interventions included in the model. RESULTS: The model indicated that the population with no caries in deciduous teeth increased by 7.5%, 5.7%, and 4.2% at three years of age, and 12.8%, 9.9%, and 5.9% at five years of age, under STB+FV, STB, and FV respectively when compared to the base case. At three years old, the population with untreated caries in deciduous teeth decreased by 12.9% in STB+FV, 9.8% in STB, and 7.3% in FV, and by 10.9% in STB+FV, 8.5% in STB, and 4.9% in FV, at five years old. The increase in the population without caries is considered clinically significant compared to baseline. CONCLUSION: The model determined that an effective combination of STB and FV should be administered between 0 and 5 years of age to reduce caries in the primary dentition. The SDM could be applied as a "what-if" analysis in order to determine the most beneficial intervention or policy by comparing them.