Diet affects inflammatory arthritis: a Mendelian randomization study of 30 dietary patterns causally associated with inflammatory arthritis.

Background: The causal associations between dietary intake and the risk and severity of Inflammatory Arthritis (IA) are currently unknown. Objective: In this study, we aimed to investigate the causal relationship between nine dietary categories (30 types of diet) and IA using Mendelian randomization (MR). Methods: We analyzed data from 30 diets and IA in a genome-wide association study (GWAS). Single nucleotide polymorphisms (SNPs) that could influence the results of MR analyses were screened out through the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test. SNPs were analyzed through two-sample bidirectional MR using inverse variance weighting, MR-Egger regression, and weighted median method. The multiplicity and heterogeneity of SNPs were assessed using MR-Egger intercept term tests and Cochran's Q tests. FDR correction was used to correct the p-values. Results: IVW results showed that Beef intake [Odds ratio (OR) = 2.862; 95% confidence interval (CI), 1.360-6.021, p = 0.006, p_fdr < 0.05] was positively associated with rheumatoid arthritis(RA); Dried fruit intake (OR = 0.522; 95% CI, 0.349-0.781, p = 0.002, p_fdr < 0.05), and Iron intake (OR = 0.864; 95%CI, 0.777-0.960, p = 0.007, p_fdr < 0.05) were negatively associated with RA, all of which were evidence of significance. Fresh fruit intake (OR = 2.528. 95% CI, 1.063-6.011, p = 0.036, p_fdr > 0.05) was positively associated with psoriatic arthritis (PsA); Cheese intake (OR = 0.579; 95% CI, 0.367-0.914, p = 0.019, p_fdr > 0.05) was negatively associated with PsA; both were suggestive evidence. Processed meat intake (OR = 0.238; 95% CI, 0.100-0.565, p = 0.001, p_fdr < 0.05) was negatively associated with reactive arthritis (ReA), a protective factor, and significant evidence. All exposure data passed the heterogeneity check (Cochrane's Q test p > 0.05) and no directional pleiotropy was detected. Leave-one-out analyses demonstrated the robustness of the causal relationship in the positive results. Conclusion: Our study presents genetic evidence supporting a causal relationship between diet and an increased risk of IA. It also identifies a causal relationship between various dietary modalities and different types of IA. These findings have significant implications for the prevention and management of IA through dietary modifications.

Computational modeling and uncertainty prediction of hyperelastic constitutive responses of damaged brain tissue under different temperature and strain rates.

The effect of strain rate and temperature on the hyperelastic material stress-strain characteristics of the damaged porcine brain tissue is evaluated in this present work. The desired constitutive responses are obtained using the commercially available finite element (FE) tool ABAQUS, utilizing 8-noded brick elements. The model's accuracy has been verified by comparing the results from the previously published literature. Further, the stress-strain behavior of the brain tissue is evaluated by varying the damages at various strain rates and temperatures (13, 20, 27, and 37°C) under compression test. Additionally, the sensitivity analysis of the model is computed to check the effect of input parameters, that is, the temperature, strain rate, and damages on the material properties (shear modulus). The modeling and discussion sections enumerate the inclusive features and model capabilities.

A New Approach Refined Probabilistic Health Risk Assessment of Shaoguan Smelter Based on Microenvironment - Guangdong Province, China, 2021.

Introduction: This study introduces a novel method for developing an advanced exposure conceptual model tailored for health risk assessment, focusing on microenvironments. Methods: The research was conducted at a major smelter in China to assess the health risks associated with trace metals (TMs) pollutants in the facility and the surrounding soil. Results: Deterministic risk assessment indicated that cobalt, cadmium, antimony, manganese, arsenic, plumbum, and mercury (Co, Cd, Sb, Mn, As, Pb, and Hg) necessitated further evaluation through probabilistic risk assessment to assess potential health risks to residents. The 95% quantile concentrations of other TMs were found to be within acceptable health risk limits. For the probabilistic risk assessment, exposure parameters such as body weight, respiration rate, and exposure duration were collected using a questionnaire. This targeted assessment of the residential microenvironment revealed it as the site of the highest carcinogenic (CR) and non-carcinogenic risks (NCR), with values ranging from 2.84×10-5 to 6.7×10-5 and 1.59 to 5.57, respectively. Conclusion: The primary contaminants posing the greatest health risks in residential and industrial areas have been identified as As, Pb, and Mn. The probabilistic health risk model, which focuses on microenvironmental factors, yields more precise results and offers a valuable tool for managing soil health risks.

Spatio-temporal characteristics and multi-scale risk identification of pollution load based on sensitivity analysis in small watersheds located in Tuojiang River Basin, China.

High-quality development of water resources supports high-quality socio-economic development. High-quality development connects high-quality life, and clarifying the key management contents of small watersheds plays an important role in building ecologically clean small watersheds and promoting regional production and life. Previous research on pollution loads has focused on examining the impact of various external drivers on pollution loads but still lacks research on the impact of changes in pollution sources themselves on pollution loads. In this study, sensitivity analysis was used to determine the impact of changes from different sources on the total pollution loads, which can recognize the critical pollution sources. We first employed the pollutant discharge coefficient method to quantify non-point source pollution loads in the small watershed in the upstream Tuojiang River basin from 2010 to 2021. Then, combination sensitivity analysis with Getis-Ord Gi* was used to identify the critical sources and their crucial areas at the global, districts (counties), and towns (streets) scales, respectively. The results indicate: (1) The pollution loads of COD, NH3-N, TN, and TP all show a decreasing trend, reducing by 18.3%, 16.2%, 18.6%, and 28.1% from 2010 to 2021, respectively; (2) Livestock and poultry breeding pollution source is the most critical source for majority areas across watershed; (3) High-risk areas are mainly concentrated in Jingyang district and its subordinate towns (streets). There is a trend of low-pollution risk areas transitioning to high-pollution risk areas, with high-risk areas predominantly concentrated in the southeast and exhibiting a noticeable phenomenon of pollution load spilling around. This study can promote other similar small watersheds, holding significant importance for non-point source pollution control in small watersheds.

Techno-economic assessment of industrial food waste composting facility: Evaluating bulking agents, processing strategies, and market dynamics.

Techno-economic assessment (TEA) of a valorization of bulking agent (BA) ratios on the food waste compost value chain is made to assess economic feasibility. TEA was performed with two plans (Plan A: existing composting facilities; Plan B: new composting facilities) and each plan was under four scenarios. The BA (i.e. corn stalks, garden waste, and watermelon seedlings) ratio of 5 % (S1), 10 % (S2), 20 % (S3), and garden waste with a ratio of 20 % (S4). Results indicate that S2, with a net present value (NPV) of 128.9 million, represents Plan A's most economically viable scenario. Although the total operating costs of S4 were 18.9 %-23.5 % higher, 25.6 %-42.2 % higher total revenue made S4 have an NPV of 92.9 million, making it the most viable scenario in Plan B. All scenarios show positive NPV within a ± 20 % fluctuation range. Organic fertilizer price, government subsidies, and processing capacity were the key factors influencing NPV.

HIV-related stigma and associated factors: a systematic review and meta-analysis.

Background: It has been recognized that HIV-related stigma hinders efforts in testing, treatment, and prevention. In this systematic review, we aimed to summarize available findings on the association between HIV-related stigma and age, social support, educational status, depression, employment status, wealth index, gender, residence, knowledge about HIV, marital status, duration since diagnosis, and disclosure status using a large number of studies. Methods: Electronic databases including Scopus, Medline/PubMed, Web of Sciences (WOS), Cochrane Library, Google Scholar, and Open Research Dataset Challenge were systematically searched until 15 April 2023. We included all kinds of HIV-stigma studies, regardless of language, publishing date, or geographic location. The inclusion criteria were met by 40 studies, with a total of 171,627 patients. A mixed-effect model was used to pool estimates and evaluate publication bias, as well as to conduct sensitivity analysis. Results: Factors such as older age, social support, greater education, higher socioeconomic status, good knowledge of HIV, and longer years of living with HIV significantly lowered the likelihood of HIV-related stigma. Contrarily, factors such as depression, residing in rural areas, female respondents, and non-disclosure of HIV status were significantly associated with a high risk of HIV-related stigma. Conclusion: To combat systemic HIV-associated stigma, it is crucial to develop wholesome and comprehensive social methods by raising community-level HIV awareness. In addition to activism, local economic development is also crucial for creating thriving communities with a strong social fabric.

Deciphering the enigma of Lassa virus transmission dynamics and strategies for effective epidemic control through awareness campaigns and rodenticides.

This study aims to formulate a mathematical framework to examine how the Lassa virus spreads in humans of opposite genders. The stability of the model is analyzed at an equilibrium point in the absence of the Lassa fever. The model's effectiveness is evaluated using real-life data, and all the parameters needed to determine the basic reproduction number are estimated. Sensitivity analysis is performed to pinpoint the crucial parameters significantly influencing the spread of the infection. The interaction between threshold parameters and the basic reproduction number is simulated. Control theory is employed to devise and evaluate strategies, such as awareness campaigns, advocating condom usage, and deploying rodenticides to reduce the possibility of virus transmission efficiently.

Improving tuberculosis control: assessing the value of medical masks and case detection-a multi-country study with cost-effectiveness analysis.

Tuberculosis (TB) remains a significant global health concern, necessitating effective control strategies. This article presents a mathematical model to evaluate the comparative effectiveness of medical mask usage and case detection in TB control. The model is constructed as a system of ordinary differential equations and incorporates crucial aspects of TB dynamics, including slow-fast progression, medical mask use, case detection, treatment interventions and differentiation between symptomatic and asymptomatic cases. A key objective of TB control is to ensure that the reproduction number, R c , remains below unity to achieve TB elimination or persistence if R c exceeds 1. Our mathematical analysis reveals the presence of a transcritical bifurcation when the R c = 1 signifies a critical juncture in TB control strategies. These results confirm that the effectiveness of case detection in diminishing the endemic population of symptomatic individuals within a TB-endemic equilibrium depends on exceeding a critical threshold value. Furthermore, our model is calibrated using TB yearly case incidence data per 100 000 population from Indonesia, India, Lesotho and Angola. We employed the bootstrap resampling residual approach to assess the uncertainty inherent in our parameter estimates which provides a comprehensive distribution of the parameter values. Despite a declining trend in new incidence, these four countries exhibit a reproduction number greater than 1, indicating persistent TB cases in the presence of ongoing TB control programmes. We employ the partial rank correlation coefficient in conjunction with the Latin hypercube sampling method to conduct a global sensitivity analysis of the R c parameter for each fitted parameter in every country. We find that the medical mask use is more sensitive to reduce R c compared with the case detection implementation. To further gain insight into the necessary control strategy, we formulated an optimal control and studied the cost-effectiveness analysis of our model to investigate the impact of case detection and medical mask use as control measures in TB spread. Cost-effectiveness analysis demonstrates that combining these interventions emerges as the most cost-effective strategy for TB control. Our findings highlight the critical importance of medical masks and their efficacy coupled with case detection in shaping TB control dynamics, elucidating the primary parameter of concern for managing the control reproduction number. We envisage our findings to have implications and be vital for TB control if implemented by policymakers and healthcare practitioners involved in TB control efforts.

Incorporating hydraulic gradient and pumping rate into GALDIT framework to assess groundwater vulnerability to salinity in coastal aquifers: a case study from Urmia Plain, Iran.

The critical role of groundwater in meeting diverse needs, including drinking, industrial, and agricultural, highlights the urgency of effective resource management. Excessive groundwater extraction, especially in coastal regions including Urmia Plain in NW Iran, disrupts the equilibrium between freshwater and saline boundaries within aquifers. Influential parameters governing seawater intrusion-groundwater occurrence (G), aquifer hydraulic conductivity (A), the height of groundwater level above the mean sea level (L), distance from the shore (D), impact of the existing status of seawater intrusion (I), and thickness of the saturated aquifer (T)-merge to shape the GALDIT vulnerability index for coastal aquifers. This study enriches the GALDIT framework by incorporating two additional hydrogeological variables: hydraulic gradient (i) and pumping rate (P). This expansion produces seven distinct vulnerability maps (GALDIT, GAiDIT, GAiDIT-P, GALDIT-i, GALDIT-iP, GALDIT-P, GAPDIT). In the Urmia Plain, the traditional GALDIT index reveals vulnerability values ranging from 2 to 8.1, categorized into six classes from negligible to very high vulnerability. However, the modified indices, GAiDIT and GAiDIT-P, yield a three-class categorization, ranging from low to high vulnerability. The introduction of the "i" and "P" parameters in GALDIT-i and GALDIT-iP enhances the precision of vulnerability mapping, altering class distribution and intensifying vulnerability ratings. The eastern, central, and coastal areas of the Urmia Plain demonstrate high to very high vulnerability levels, in contrast to the lower vulnerability observed in the western regions. Both the GALDIT-P (r = 0.82) and GALDIT-iP (r = 0.81) indices show strong correlations with Cl concentration, thereby improving mapping accuracy over the traditional GALDIT index (r = 0.72). A sensitivity analysis highlights the critical influence of the "i" parameter, suggesting its weighting should be revised. Parameter recalibration serves to amplify the significance of "G," "L," "D," and "i" parameters, while diminishing others. The integration of multiple hydrogeological variables considerably enhances the precision of groundwater vulnerability assessments.

Inter-laboratory reproduction and sensitivity study of a finite element model to quantify human femur failure load: Case of metastases.

INTRODUCTION: Metastases increase the risk of fracture when affecting the femur. Consequently, clinicians need to know if the patient's femur can withstand the stress of daily activities. The current tools used in clinics are not sufficiently precise. A new method, the CT-scan-based finite element analysis, gives good predictive results. However, none of the existing models were tested for reproducibility. This is a critical issue to address in order to apply the technique on a large cohort around the world to help evaluate bone metastatic fracture risk in patients. The aim of this study is then to evaluate 1) the reproducibility 2) the transposition of the reproduced model to another dataset and 3) the global sensitivity of one of the most promising models of the literature (original model). METHODS: The model was reproduced based on the paper describing it and discussion with authors to avoid reproduction errors. The reproducibility was evaluated by comparing the results given in the original model by the original first team (Leuven, Belgium) and the reproduced model made by another team (Lyon, France) on the same dataset of CT-scans of ex vivo femurs. The transposition of the model was evaluated by comparing the results of the reproduced model on two different datasets. The global sensitivity analysis was done by using the Morris method and evaluates the influence of the density calibration coefficient, the segmentation, the orientations and the length of the femur. RESULTS: The original and reproduced models are highly correlated (r2 = 0.95), even though the reproduced model gives systematically higher failure loads. When using the reproduced model on another dataset, predictions are less accurate (r2 with the experimental failure load decreases, errors increase). The global sensitivity analysis showed high influence of the density calibration coefficient (mean variation of failure load of 84 %) and non-negligible influence of the segmentation, orientation and length of the femur (mean variation of failure load between 7 and 10 %). CONCLUSION: This study showed that, although being validated, the reproduced model underperformed when using another dataset. The difference in performance depending on the dataset is commonly the cause of overfitting when creating the model. However, the dataset used in the original paper (Sas et al., 2020a) and the Leuven's dataset gave similar performance, which indicates a lesser probability for the overfitting cause. Also, the model is highly sensitive to density parameters and automation of measurement may minimize the uncertainty on failure load. An uncertainty propagation analysis would give the actual precision of such model and improve our understanding of its behavior and is part of future work.

Dynamical analysis of methicillin-resistant Staphylococcus aureus infection in North Cyprus with optimal control: prevalence and awareness.

The number of Methicillin-resistant Staphylococcus aureus (MRSA) cases in communities and hospitals is on the rise worldwide. In this work, a nonlinear deterministic model for the dynamics of MRSA infection in society was developed to visualize the significance of awareness in interventions that could be applied in the prevention of transmission with and without optimal control. Positivity and uniqueness were verified for the proposed corruption model to identify the level of resolution of infection factors in society. Furthermore, how various parameters affect the reproductive number R 0 and sensitivity analysis of the proposed model was explored through mathematical techniques and figures. The global stability of model equilibria analysis was established by using Lyapunov functions with the first derivative test. A total of seven years of data gathered from a private hospital consisting of inpatients and outpatients of MRSA were used in this model for numerical simulations and for observing the dynamics of infection by using a non-standard finite difference (NSFD) scheme. When optimal control was applied as a second model, it was determined that increasing awareness of hand hygiene and wearing a mask were the key controlling measures to prevent the spread of community-acquired MRSA (CA-MRSA) and hospital-acquired MRSA (HA-MRSA). Lastly, it was concluded that both CA-MRSA and HA-MRSA cases are on the rise in the community, and increasing awareness concerning transmission is extremely significant in preventing further spread.

An integrated deep learning approach for modeling dissolved oxygen concentration at coastal inlets based on hydro-climatic parameters.

Climate change has a significant impact on dissolved oxygen (DO) concentrations, particularly in coastal inlets where numerous human activities occur. Due to the various water quality (WQ), hydrological, and climatic parameters that influence this phenomenon, predicting and modeling DO variation is a challenging process. Accordingly, this study introduces an innovative Deep Learning Neural Network (DLNN) methodology to model and predict DO concentrations for the Egyptian Rashid coastal inlet, leveraging field-recorded WQ and hydroclimatic datasets. Initially, statistical and exploratory data analyses are performed to provide a thorough understanding of the relationship between DO fluctuations and associated WQ and hydroclimatic stressors. As an initial step towards developing an effective DO predictive model, conventional Machine Learning (ML) approaches such as Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Decision Tree Regressor (DTR) are employed. Subsequently, a DLNN approach is utilized to validate the prediction capabilities of the investigated conventional ML approaches. Finally, a sensitivity analysis is conducted to evaluate the impact of WQ and hydroclimatic parameters on predicted DO. The outcomes demonstrate that DLNN significantly improves DO prediction accuracy by 4% compared to the best-performing ML approach, achieving a Correlation Coefficient of 0.95 with a root mean square error (RMSE) of 0.42 mg/l. Solar radiation (SR), pH, water levels (WL), and atmospheric pressure (P) emerge as the most significant hydroclimatic parameters influencing DO fluctuations. Ultimately, the developed models could serve as effective indicators for coastal authorities to monitor DO changes resulting from accelerated climate change along the Egyptian coast.

Research on the method of identifying vertical earth pressure of hinged prefabricated culvert box culvert on the top slab.

In China, several expressways have been designed as prefabricated box culverts with hinge connections, which have different structural features from the prefabricated culverts in other countries. The difference would contribute to the culvert-soil interaction of prefabricated box culverts, which could affect the earth pressure on the culvert. Based on the field test and numerical simulation method, a hinged prefabricated box culvert (HPBC) with a span of 4 m and a rise of 4 m was investigated, which was applied to the Xi-Yu expressway in China. The objective of this research was to investigate the vertical earth pressure on the top slab of the HPBC culvert at different backfill heights through the field tests. The FLAC3D software was employed to conduct further analysis of the effects of backfill height, backfill modulus, and foundation modulus on the vertical earth pressure on the top slab of HPBC. The differences between the HPBC and monolithic box culvert (MBC) were also examined. Furthermore, a revised method for calculating the vertical earth pressure on the top slab was put proposed and compared with the AASHTO method for calculating earth pressure on the top of culverts and the values taken from the Chinese culvert design code. The proposed method is capable of improving the accuracy of the earth pressure approach, making it more representative of actual conditions. Subsequently, the sensitivity analysis of backfill height, backfill modulus and foundation modulus to the vertical earth pressure concentration coefficient of the top slab was carried out by using the principle of orthogonal array analysis and the modified calculation method proposed in this paper. The findings of this study offer valuable insights into the determination of culvert top earth pressure of HPBC.

A Bayesian framework for virtual comparative trials and bioequivalence assessments.

Introduction: In virtual bioequivalence (VBE) assessments, pharmacokinetic models informed with in vitro data and verified with small clinical trials' data are used to simulate otherwise unfeasibly large trials. Simulated VBE trials are assessed in a frequentist framework as if they were real despite the unlimited number of virtual subjects they can use. This may adequately control consumer risk but imposes unnecessary risks on producers. We propose a fully Bayesian model-integrated VBE assessment framework that circumvents these limitations. Methods: We illustrate our approach with a case study on a hypothetical paliperidone palmitate (PP) generic long-acting injectable suspension formulation using a validated population pharmacokinetic model published for the reference formulation. BE testing, study power, type I and type II error analyses or their Bayesian equivalents, and safe-space analyses are demonstrated. Results: The fully Bayesian workflow is more precise than the frequentist workflow. Decisions about bioequivalence and safe space analyses in the two workflows can differ markedly because the Bayesian analyses are more accurate. Discussion: A Bayesian framework can adequately control consumer risk and minimize producer risk . It rewards data gathering and model integration to make the best use of prior information. The frequentist approach is less precise but faster to compute, and it can still be used as a first step to narrow down the parameter space to explore in safe-space analyses.

An Apoptosis-Related Specific Risk Model for Breast Cancer: From Genomic Analysis to Precision Medicine.

BACKGROUND: Breast cancer (BC) ranks as the most prevalent malignancy affecting women globally, with apoptosis playing a pivotal role in its pathological progression. Despite the crucial role of apoptosis in BC development, there is limited research exploring the relationship between BC prognosis and apoptosis-related genes (ARGs). Therefore, this study aimed to establish a BC-specific risk model centered on apoptosis-related factors, presenting a novel approach for predicting prognosis and immune responses in BC patients. METHODS: Utilizing data from The Cancer Gene Atlas (TCGA), Cox regression analysis was employed to identify differentially prognostic ARGs and construct prognostic models. The accuracy and clinical relevance of the model, along with its efficacy in predicting immunotherapy outcomes, were evaluated using independent datasets, Receiver Operator Characteristic (ROC) curves, and nomogram. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were used to predict potential mechanical pathways. The CellMiner database is used to assess drug sensitivity of model genes. RESULTS: A survival risk model comprising eight prognostically relevant apoptotic genes (PMAIP1, TP53AIP1, TUBA3D, TUBA1C, BCL2A1, EMP1, GSN, F2) was established based on BC patient samples from TCGA. Calibration curves validated the ROC curve and nomogram, demonstrating excellent accuracy and clinical utility. In samples from the Gene Expression Omnibus (GEO) datasets and immunotherapy groups, the low-risk group (LRG) demonstrated enhanced immune cell infiltration and improved immunotherapy responses. Model genes also displayed positive associations with sensitivity to multiple drugs, including vemurafenib, dabrafenib, PD-98059, and palbociclib. CONCLUSIONS: This study successfully developed and validated a prognostic model based on ARGs, offering new insights into prognosis and immune response prediction in BC patients. These findings hold promise as valuable references for future research endeavors in this field.

How Many Monte Carlo Samples are Needed for Probabilistic Cost-Effectiveness Analyses?

OBJECTIVES: Probabilistic sensitivity analysis (PSA) is conducted to account for the uncertainty in cost and effect of decision options under consideration. PSA involves obtaining a large sample of input parameter values (N) to estimate the expected cost and effect of each alternative in the presence of parameter uncertainty. When the analysis involves using stochastic models (eg, individual-level models), the model is further replicated P times for each sampled parameter set. We study how N and P should be determined. METHODS: We show that PSA could be structured such that P can be an arbitrary number (say, P=1). To determine N, we derive a formula based on Chebyshev's inequality such that the error in estimating the incremental cost-effectiveness ratio (ICER) of alternatives (or equivalently, the willingness-to-pay value at which the optimal decision option changes) is within a desired level of accuracy. We described 2 methods to confirm, visually and quantitatively, that the N informed by this method results in ICER estimates within the specified level of accuracy. RESULTS: When N is arbitrarily selected, the estimated ICERs could be substantially different from the true ICER (even as P increases), which could lead to misleading conclusions. Using a simple resource allocation model, we demonstrate that the proposed approach can minimize the potential for this error. CONCLUSIONS: The number of parameter samples in probabilistic cost-effectiveness analyses should not be arbitrarily selected. We describe 3 methods to ensure that enough parameter samples are used in probabilistic cost-effectiveness analyses.

A compartmental model for smoking dynamics in Italy: a pipeline for inference, validation, and forecasting under hypothetical scenarios.

We propose a compartmental model for investigating smoking dynamics in an Italian region (Tuscany). Calibrating the model on local data from 1993 to 2019, we estimate the probabilities of starting and quitting smoking and the probability of smoking relapse. Then, we forecast the evolution of smoking prevalence until 2043 and assess the impact on mortality in terms of attributable deaths. We introduce elements of novelty with respect to previous studies in this field, including a formal definition of the equations governing the model dynamics and a flexible modelling of smoking probabilities based on cubic regression splines. We estimate model parameters by defining a two-step procedure and quantify the sampling variability via a parametric bootstrap. We propose the implementation of cross-validation on a rolling basis and variance-based Global Sensitivity Analysis to check the robustness of the results and support our findings. Our results suggest a decrease in smoking prevalence among males and stability among females, over the next two decades. We estimate that, in 2023, 18% of deaths among males and 8% among females are due to smoking. We test the use of the model in assessing the impact on smoking prevalence and mortality of different tobacco control policies, including the tobacco-free generation ban recently introduced in New Zealand.

Modeling and investigating malaria P. Falciparum and P. Vivax infections: Application to Djibouti data.

Malaria is an infectious and communicable disease, caused by one or more species of Plasmodium parasites. There are five species of parasites responsible for malaria in humans, of which two, Plasmodium Falciparum and Plasmodium Vivax, are the most dangerous. In Djibouti, the two species of Plasmodium are present in different proportions in the infected population: 77% of P. Falciparum and 33% of P. Vivax. In this study we present a new mathematical model describing the temporal dynamics of Plasmodium Falciparum and Plasmodium Vivax co-infection. We focus briefly on the well posedness of this model and on the calculation of the basic reproductive numbers for the infections with each Plasmodium species that help us understand the long-term dynamics of this model (i.e., existence and stability of various eqiuilibria). Then we use computational approaches to: (a) identify model parameters using real data on malaria infections in Djibouti; (b) illustrate the influence of different estimated parameters on the basic reproduction numbers; (c) perform global sensitivity and uncertainty analysis for the impact of various model parameters on the transient dynamics of infectious mosquitoes and infected humans, for infections with each of the Plasmodium species. The originality of this research stems from employing the FAST method and the LHS method to identify the key factors influencing the progression of the disease within the population of Djibouti. In addition, sensitivity analysis identified the most influential parameter for Falciparium and Vivax reproduction rates. Finally, the uncertainty analysis enabled us to understand the variability of certain parameters on the infected compartments.

[Analysis of Ozone and VOCs Pollution Characteristics, Sources, and Abatement Control Strategies in Hebi].

In order to control the increasing ozone （O3） pollution in Hebi, Henan Province, clarifying the pollution characteristics of ozone and its precursors is vital. Therefore, we conducted a comprehensive analysis of O3 pollution utilizing the OFP-PMF-EKMA method combined with online hourly resolution monitoring data of conventional pollutants and volatile organic compounds （VOCs） in the summer of 2022 （June-September）. Ozone formation potential （OFP） was used to identify the key VOCs species, and the PMF model was used to identify the VOCs emission sources, whereas EKMA curves and scenario analysis were used to identify the main ozone control area in Hebi and to determine the reduction ratio of VOCs and NOx in a scientifically refined way. In 2022, Hebi had persistent O3 pollution, with the highest concentration in June. Conditions of high temperature, low humidity, and low atmospheric pressure contributed to the O3 accumulation. Aromatic and oxygenated volatile organic compounds （OVOCs） contributed significantly to the OFP and VOCs fraction, which were the dominant active substance and concentration dominant species. The results of the VOCs source analysis indicated that vehicle exhaust sources （25.3%） were the main source of atmospheric VOCs, followed by process sources （17.7%） and biomass combustion sources （17.6%）. Thus, emission sources associated with the combustion of fossil fuels and industrial production emissions were the most urgent sources of atmospheric VOCs to be controlled in Hebi. The O3 generation in Hebi occurred in the VOCs-sensitive zones, and the emission reduction results showed that a synergistic emission reduction of VOCs and nitrogen oxide （NOx） could effectively control O3 pollution with a 75% reduction in VOCs and a 10% reduction in NOx.

Estimating the relative importance of epidemiological and behavioural parameters for epidemic mpox transmission: a modelling study.

BACKGROUND: Many European countries experienced outbreaks of mpox in 2022, and there was an mpox outbreak in 2023 in the Democratic Republic of Congo. There were many apparent differences between these outbreaks and previous outbreaks of mpox; the recent outbreaks were observed in men who have sex with men after sexual encounters at common events, whereas earlier outbreaks were observed in a wider population with no identifiable link to sexual contacts. These apparent differences meant that data from previous outbreaks could not reliably be used to parametrise infectious disease models during the 2022 and 2023 mpox outbreaks, and modelling efforts were hampered by uncertainty around key transmission and immunity parameters. METHODS: We developed a stochastic, discrete-time metapopulation model for mpox that allowed for sexual and non-sexual transmission and the implementation of non-pharmaceutical interventions, specifically contact tracing and pre- and post-exposure vaccinations. We calibrated the model to case data from Berlin and used Sobol sensitivity analysis to identify parameters that mpox transmission is especially sensitive to. We also briefly analysed the sensitivity of the effectiveness of non-pharmaceutical interventions to various efficacy parameters. RESULTS: We found that variance in the transmission probabilities due to both sexual and non-sexual transmission had a large effect on mpox transmission in the model, as did the level of immunity to mpox conferred by a previous smallpox vaccination. Furthermore, variance in the number of pre-exposure vaccinations offered was the dominant contributor to variance in mpox dynamics in men who have sex with men. If pre-exposure vaccinations were not available, both the accuracy and timeliness of contact tracing had a large impact on mpox transmission in the model. CONCLUSIONS: Our results are valuable for guiding epidemiological studies for parameter ascertainment and identifying key factors for success of non-pharmaceutical interventions.