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Breast cancer is one of the tumors with the highest prevalence rate among women in the world, and its BRCA1/2 gene is a common mutation site. Talazoparib, as a targeted PARP inhibitor, can effectively control the occurrence and development of breast cancer with BRCA1/2 gene mutation, and play a therapeutic role. Based on the findings from the Phase III EMBRACE trial (NCT01945775 clinical trial), our analysis reveals that the talazoparib group demonstrated a significant extension in progression-free survival, along with improved response markers and patient-reported outcomes when compared to conventional therapies. This study aims to assess the cost-effectiveness of talazoparib for treating advanced breast cancer with germline BRCA1/2 mutations and HER2 negativity, considering the perspectives of health services in China and the United States. The results obtained will serve as a valuable reference for promoting rational drug utilization and enhancing medical resource efficiency. To evaluate the cost-effectiveness of Talazoparib more scientifically and provide clinicians with chemotherapy options, this paper developed a Markov model based on the EMBRACA clinical trial (clinical Trails.gov No., NCT01945775) to simulate the survival events of breast cancer patients in the Talazoparib group and the standard treatment group. The state transition probability and clinical data of breast cancer patients during treatment were extracted from the phase III EMBRACA clinical trial. The cost data generated during the treatment process comes from local hospital pricing, other references, and expert consultation. This article uses US dollars to calculate the treatment cost and incremental cost-effectiveness ratio. Health outcomes are expressed in Quality Adjusted Life Years (QALYs). In addition, Outcomes were measured in quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio, which robustness was evaluated by deterministic and probabilistic sensitivity analyses. This article establishes a Markov model for single-item sensitivity analysis. The results show that the economic benefits of using Talazoparib as a new treatment strategy in both China and the United States are higher than other drugs, and it is cost-effective. Compared to the control group, the incremental cost incurred by the Talazoparib treatment group in China was $2484.48/QALY, with an incremental QALY of 1.5. However, Talazoparib in the United States holds a dominant position, saving costs of $10,223.43 and increasing QALYs by 1.5. The clinical treatment effect of Talazoparib group in BRCA1/2 mutant advanced breast cancer patients is better than that of the standard treatment group, and the progression free survival period is significantly prolonged. From the perspective of medical and health services in China and the United States, the Talazoparib group is more economical than the standard treatment group in treating patients with BRCA1/2 mutant advanced breast cancer. [ABSTRACT FROM AUTHOR]

In most of the United States, insurance companies may use gender to determine car insurance rates. In addition, several studies have shown that women over the age of 25 generally pay more than men for car insurance. Then, we investigate whether the distributions of claims for women and men differ in location, scale and shape by means of the GAMLSS regression framework, using microdata provided by U.S. and Australian insurance companies, to use this evidence to support policy makers' decisions. We also develop a parametric-bootstrap test to investigate the tail behavior of the distributions. When covariates are not considered, the distribution of claims does not appear to differ by gender. When covariates are included, the regressions provide mixed evidence for the location parameter. However, for female claimants, the spread of the distribution is lower. Our research suggests that, at least for the contexts analyzed, there is no clear statistical reason for charging higher rates to women. While providing evidence to support unisex insurance pricing policies, given the limitations represented by the use of country-specific data, this paper aims to promote further research on this topic with different datasets to corroborate our findings and draw more general conclusions. [ABSTRACT FROM AUTHOR]

This present paper aims to examine various epidemiological aspects of the monkeypox viral infection using a fractional-order mathematical model. Initially, the model is formulated using integer-order nonlinear differential equations. The imperfect vaccination is considered for human population in the model formulation. The proposed model is then reformulated using a fractional order derivative with power law to gain a deeper understanding of disease dynamics. The values of the model parameters are determined from the cumulative reported monkeypox cases in the United States during the period from May 10th to October 10th, 2022. Besides this, some of the demographic parameters are evaluated from the population of the literature. We establish sufficient conditions to ensure the existence and uniqueness of the model's solution in the fractional case. Furthermore, the stability of the endemic equilibrium of the fractional monkeypox model is presented. The Lyapunov function approach is used to demonstrate the global stability of the model equilibria. Moreover, the fractional order model is numerically solved using an efficient numerical technique known as the fractional Adams-Bashforth-Moulton method. The numerical simulations are conducted using estimated parameters, considering various values of the fractional order of the Caputo derivative. The finding of this study reveals the impact of various model parameters and fractional order values on the dynamics and control of monkeypox. [ABSTRACT FROM AUTHOR]

An assessment of the likelihood of use and abuse potential for new tobacco products is an important part of tobacco product regulation in the United States and abroad. This paper reports the results of a randomized, open-label, crossover clinical study that assessed factors related to product adoption and abuse liability (AL), comparing two closed electronic nicotine delivery system (ENDS) products to combustible cigarettes and nicotine gum, high- and low-AL comparator products, respectively. During an 11-day confinement period that included multiple product familiarization sessions, healthy adult smokers participated in AL test sessions to evaluate the abuse liability of each product. During these test sessions, changes in subjective measures; speed and amount of nicotine uptake; and maximum changes in physiological effects before, during, and after use of each assigned product were assessed over 4 h. Positive subjective effects measures scores such as product-liking and overall intent to use again were highest for cigarettes, followed by the Vuse ENDS, with nicotine gum consistently having the lowest scores. The PK results (Cmax and Tmax) of the Vuse ENDS products are between UB cigarettes and nicotine gum, which correlates with the subjective effects. All nicotine uptake measures for the Vuse ENDS products were lower than that of usual brand (UB) cigarettes, including peak nicotine uptake and overall nicotine uptake, and were either similar to or lower than nicotine gum. The time course of nicotine uptake after use of the ENDS was more similar to that of combustible cigarettes than nicotine gum. The results indicate that the AL of each ENDS product is lower than that of UB cigarettes and similar to that of nicotine gum. [ABSTRACT FROM AUTHOR]

This present paper aims to examine various epidemiological aspects of the monkeypox viral infection using a fractional-order mathematical model. Initially, the model is formulated using integer-order nonlinear differential equations. The imperfect vaccination is considered for human population in the model formulation. The proposed model is then reformulated using a fractional order derivative with power law to gain a deeper understanding of disease dynamics. The values of the model parameters are determined from the cumulative reported monkeypox cases in the United States during the period from May 10th to October 10th, 2022. Besides this, some of the demographic parameters are evaluated from the population of the literature. We establish sufficient conditions to ensure the existence and uniqueness of the model's solution in the fractional case. Furthermore, the stability of the endemic equilibrium of the fractional monkeypox model is presented. The Lyapunov function approach is used to demonstrate the global stability of the model equilibria. Moreover, the fractional order model is numerically solved using an efficient numerical technique known as the fractional Adams-Bashforth-Moulton method. The numerical simulations are conducted using estimated parameters, considering various values of the fractional order of the Caputo derivative. The finding of this study reveals the impact of various model parameters and fractional order values on the dynamics and control of monkeypox. [ABSTRACT FROM AUTHOR]

COVID-19 has dramatically changed people's mobility geste patterns and affected the operations of different functional spots. In the environment of the successful reopening of countries around the world since 2022, it's pivotal to understand whether the reopening of different types of locales poses a threat of wide epidemic transmission. In this paper, by establishing an epidemiological model based on mobile network data, combining the data handed by the Safegraph website, and taking into account the crowd inflow characteristics and the changes of susceptible and latent populations, the trends of the number of crowd visits and the number of epidemic infections at different functional points of interest after the perpetration of continuing strategies were simulated. The model was also validated with daily new cases in ten metropolitan areas in the United States from March to May 2020, and the results showed that the model fitted the evolutionary trend of realistic data more accurately. Further, the points of interest were classified into risk levels, and the corresponding reopening minimum standard prevention and control measures were proposed to be implemented according to different risk levels. The results showed that restaurants and gyms became high-risk points of interest after the perpetration of the continuing strategy, especially the general dine-in restaurants were at higher risk levels. Religious exertion centers were the points of interest with the loftiest average infection rates after the perpetration of the continuing strategy. Points of interest such as convenience stores, large shopping malls, and pharmacies were at a lower risk for outbreak impact after the continuing strategy was enforced. Based on this, continuing forestallment and control strategies for different functional points of interest are proposed to provide decision support for the development of precise forestallment and control measures for different spots. [ABSTRACT FROM AUTHOR]

The prolonged COVID-19 pandemic has tied up significant medical resources, and its management poses a challenge for the public health care decision making. Accurate predictions of the hospitalizations are crucial for the decision makers to make informed decision for the medical resource allocation. This paper proposes a method named County Augmented Transformer (CAT). To generate accurate predictions of four-week-ahead COVID-19 related hospitalizations for every states in the United States. Inspired by the modern deep learning techniques, our method is based on a self-attention model (known as the transformer model) that is actively used in Natural Language Processing. Our transformer based model can capture both short-term and long-term dependencies within the time series while enjoying computational efficiency. Our model is a data based approach that utilizes the publicly available information including the COVID-19 related number of confirmed cases, deaths, hospitalizations data, and the household median income data. Our numerical experiments demonstrate the strength and the usability of our model as a potential tool for assisting the medical resources allocation. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is a global health concern that has spread around the globe. Machine Learning is promising in the fight against the COVID-19 pandemic. Machine learning and artificial intelligence have been employed by various healthcare providers, scientists, and clinicians in medical industries in the fight against COVID-19 disease. In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States during the first wave of the Covid-19 pandemic. We utilize multiple supervised and unsupervised machine learning methods and models such as decision trees, logistic regression, support vector machines, multilayer perceptron, XGBoost, CatBoost, LightGBM, AdaBoost, Chi-Squared Test, mutual information, KModes clustering and the synthetic minority oversampling technique on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to investigate the links between COVID-19-related deleterious effects and changes in alcohol consumption habits among healthcare workers. Through the interpretation of the supervised and unsupervised methods, we have concluded that healthcare workers whose children stayed home during the first wave in the US consumed more alcohol. We also found that the work schedule changes due to the Covid-19 pandemic led to a change in alcohol use habits. Changes in food consumption, age, gender, geographical characteristics, changes in sleep habits, the amount of news consumption, and screen time are also important predictors of an increase in alcohol use among healthcare workers in the United States. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is a global health concern that has spread around the globe. Machine Learning is promising in the fight against the COVID-19 pandemic. Machine learning and artificial intelligence have been employed by various healthcare providers, scientists, and clinicians in medical industries in the fight against COVID-19 disease. In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States during the first wave of the Covid-19 pandemic. We utilize multiple supervised and unsupervised machine learning methods and models such as decision trees, logistic regression, support vector machines, multilayer perceptron, XGBoost, CatBoost, LightGBM, AdaBoost, Chi-Squared Test, mutual information, KModes clustering and the synthetic minority oversampling technique on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to investigate the links between COVID-19-related deleterious effects and changes in alcohol consumption habits among healthcare workers. Through the interpretation of the supervised and unsupervised methods, we have concluded that healthcare workers whose children stayed home during the first wave in the US consumed more alcohol. We also found that the work schedule changes due to the Covid-19 pandemic led to a change in alcohol use habits. Changes in food consumption, age, gender, geographical characteristics, changes in sleep habits, the amount of news consumption, and screen time are also important predictors of an increase in alcohol use among healthcare workers in the United States. [ABSTRACT FROM AUTHOR]

As the COVID-19 spread over the globe and new variants of COVID-19 keep occurring, reliable real-time forecasts of COVID-19 hospitalizations are critical for public health decisions on medical resources allocations. This paper aims to forecast future 2 weeks national and state-level COVID-19 new hospital admissions in the United States. Our method is inspired by the strong association between public search behavior and hospitalization admissions and is extended from a previously-proposed influenza tracking model, AutoRegression with GOogle search data (ARGO). Our LASSO-penalized linear regression method efficiently combines Google search information and COVID-19 related time series information with dynamic training and rolling window prediction. Compared to other publicly available models collected from COVID-19 forecast hub, our method achieves substantial error reduction in a retrospective out-of-sample evaluation from Jan 4, 2021, to Dec 27, 2021. Overall, we showed that our method is flexible, self-correcting, robust, accurate, and interpretable, making it a potentially powerful tool to assist healthcare officials and decision making for the current and future infectious disease outbreaks. [ABSTRACT FROM AUTHOR]

Thousands of people are reported lost in the wilderness in the United States every year and locating these missing individuals as rapidly as possible depends on coordinated search and rescue (SAR) operations. As time passes, the search area grows, survival rate decreases, and searchers are faced with an increasingly daunting task of searching large areas in a short amount of time. To optimize the search process, mathematical models of lost person behavior with respect to landscape can be used in conjunction with current SAR practices. In this paper, we introduce an agent-based model of lost person behavior which allows agents to move on known landscapes with behavior defined as independent realizations of a random variable. The behavior random variable selects from a distribution of six known lost person reorientation strategies to simulate the agent's trajectory. We systematically simulate a range of possible behavior distributions and find a best-fit behavioral profile for a hiker with the International Search and Rescue Incident Database. We validate these results with a leave-one-out analysis. This work represents the first time-discrete model of lost person dynamics validated with data from real SAR incidents and has the potential to improve current methods for wilderness SAR. [ABSTRACT FROM AUTHOR]

Millions commute to work every day in cities and interact with colleagues, partners, friends, and strangers. Commuting facilitates the mixing of people from distant and diverse neighborhoods, but whether this has an imprint on social inclusion or instead, connections remain assortative is less explored. In this paper, we aim to better understand income sorting in social networks inside cities and investigate how commuting distance conditions the online social ties of Twitter users in the 50 largest metropolitan areas of the United States. An above-median commuting distance in cities is linked to more diverse individual networks, moreover, we find that longer commutes are associated with a nearly uniform, moderate reduction of overall social tie assortativity across all cities. This suggests a universal relation between long-distance commutes and the integration of social networks. Our results inform policy that facilitating access across distant neighborhoods can advance the social inclusion of low-income groups. [ABSTRACT FROM AUTHOR]

Containing the COVID-19 pandemic while balancing the economy has proven to be quite a challenge for the world. We still have limited understanding of which combination of policies have been most effective in flattening the curve; given the challenges of the dynamic and evolving nature of the pandemic, lack of quality data etc. This paper introduces a novel data mining-based approach to understand the effects of different non-pharmaceutical interventions in containing the COVID-19 infection rate. We used the association rule mining approach to perform descriptive data mining on publicly available data for 50 states in the United States to understand the similarity and differences among various policies and underlying conditions that led to transitions between different infection growth curve phases. We used a multi-peak logistic growth model to label the different phases of infection growth curve. The common trends in the data were analyzed with respect to lockdowns, face mask mandates, mobility, and infection growth. We observed that face mask mandates combined with mobility reduction through moderate stay-at-home orders were most effective in reducing the number of COVID-19 cases across various states. [ABSTRACT FROM AUTHOR]

The COVID-19 global pandemic has significantly impacted every aspect of life all over the world. The United States is reported to have suffered more than 20% of the global casualties from this pandemic. It is imperative to investigate the growth dynamics of the disease in the US based on varying geographical and governmental factors that best manifest itself in each State of the Country. This paper utilizes a hybrid machine learning and continuum deformation-based approach for analyzing the stability and growth rate of the pandemic. To this end, principal stress values of the pandemic continuum body are obtained using Mohr's Circle method and overlapping, moving windows of data are analysed successively. This helps in finding the correlations between the growth rate and Governments' action/Public's reaction. Government actions include "state of emergency", "shelter at place", and "phase declarations". We also consider the vaccination rate milestones, which shows us the coordinated Governments' action/Public's reaction. Finally, a number of recommendations are made to the Governments and people for better management of future pandemics. [ABSTRACT FROM AUTHOR]

Our source code to process and combine these data sources, as well as the resulting combined dataset itself, is part of the Supplementary Information." now reads: "The raw data that support the findings in this paper are openly available at https://usda.library.cornell.edu/concern/publications/rn301137d (United States Department of Agriculture), https://nass.usda.gov/Research%5fand%5fScience/Cropland/Release/index.php (United States Department of Agriculture Cropland Data Layer), and https://www.prism.oregonstate.edu/ (Parameter-elevation Regressions on Independent Slopes Model Climate Group). "The raw data that support the findings in this paper are openly available at https://usda.library.cornell.edu/concern/publications/rn301137d (United States Department of Agriculture), https://nass.usda.gov/Research%5fand%5fScience/Cropland/Release/index.php (United States Department of Agriculture Cropland Data Layer), and https://prism.oregonstate.edu (Parameter-elevation Regressions on Independent Slopes Model Climate Group). [Extracted from the article]