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Background: This paper examines the economic implications of market segmentation on consumer purchasing behavior with a particular emphasis on intertemporal pricing strategies in dynamic markets. Methods: In order to analyze optimal discount rates and the timing for price reductions for consumer segments, including loyal and deal-prone customers, a detailed mathematical model was developed. The model incorporates theories of consumer behavior and pricing elasticity to simulate market responses to price changes throughout a product's lifecycle. Results: This research indicates that market segmentation enhances sales by targeting the distinct preferences of loyal consumers, who are less price-sensitive and who stabilize revenue streams, and deal-prone consumers, who respond to price reductions. Customizing pricing strategies for loyal consumers and deal-prone consumers increases sales volumes and optimizes profitability. Conclusions: This research improves our comprehension of market segmentation and dynamic pricing, providing a practical framework for businesses to create effective pricing strategies that can be promptly implemented. It emphasizes the significance of understanding consumer behavior and price sensitivity in the interest of revenue promotion. This study also emphasizes the social implications of equitable pricing practices, promoting the implementation of transparent and value-based strategies to promote market inclusivity and consumer trust. [ABSTRACT FROM AUTHOR]

The Mount Hood Diabetes Challenge Network aimed to examine the impact of model structural uncertainty on the estimated cost-effectiveness of interventions for type 2 diabetes. Ten independent modeling groups completed a blinded simulation exercise to estimate the cost-effectiveness of 3 interventions in 2 type 2 diabetes populations. Modeling groups were provided with a common baseline population, cost and utility values associated with different model health states, and instructions regarding time horizon and discounting. We collated the results to identify variation in predictions of net monetary benefit (NMB) and the drivers of those differences. Overall, modeling groups agreed which interventions had a positive NMB (ie, were cost-effective), Although estimates of NMB varied substantially—by up to £23 696 for 1 intervention. Variation was mainly driven through differences in risk equations for complications of diabetes and their implementation between models. The number of modeled health states was also a significant predictor of NMB. This exercise demonstrates that structural uncertainty between different health economic models affects cost-effectiveness estimates. Although it is reassuring that a decision maker would likely reach similar conclusions on which interventions were cost-effective using most models, the range in numerical estimates generated across different models would nevertheless be important for price-setting negotiations with intervention developers. Minimizing the impact of structural uncertainty on healthcare decision making therefore remains an important priority. Model registries, which record and compare the impact of structural assumptions, offer one potential avenue to improve confidence in the robustness of health economic modeling. • Structural uncertainty is known to be an important, but often unexamined, contributor to healthcare decision uncertainty. • Conducting a common simulation exercise across 10 independent modeling groups using uniform parameterization has allowed us to investigate the structural drivers of differences in cost-effectiveness estimates between diabetes models. We show how blinded simulation exercises can be used to quantitatively examine both the drivers and impact of structural uncertainty. Similar exercises could be replicated to examine the effect of structural uncertainty in healthcare decision problems in other disease areas. • This article demonstrates the value of incentivizing collaboration and comparative assessments among modeling groups to critically examine their modeling practice. This generates more robust evidence for decision making. Healthcare decision makers may benefit from directly facilitating collaboration between modeling groups, such as aiding funding and maintenance of model registries, which can demonstrate the relative suitability of different decision models for specific decision problems. [ABSTRACT FROM AUTHOR]

Leveraging a combination of analytical frameworks and empirical assessments, this study investigates the effects of wait-for-free (WFF) pricing schemes on the monetization of serialized, digital entertainment content, which has become increasingly pervasive on online platforms. WFF pricing is a strategy in which consumers are given the option to either wait a certain amount of time to acquire digital content at no cost or pay to consume it immediately. We evaluate the extent to which habit formation and presentbiased preferences driven by the consumption of addictive stock affect individual consumers' willingness to wait (or pay) for content, which, in turn, determines the efficacy of WFF pricing. We also examine the conditions under which consumers switch from waiting for free content to instantaneously purchasing content. Our findings indicate that WFF pricing increases the sales of serialized digital content, generating new demand from customers who would otherwise forgo participation in the market. In addition, the pricing design effectively generates sustained profits in the long run. We found that most consumers who initiate a purchase either upon initial market entry or upon switching continue to purchase as new episodes become available. Moreover, the results indicate that as a user accumulates free episodes of a specific series, given extended waiting periods, the likelihood of their conversion from a wait-for-free customer to an instant-purchase customer increases. In particular, WFF pricing effectively augments the willingness to pay of low-valuation consumers as habit formation builds up through time with the free consumption of serialized content. One free episode can elevate the likelihood of consumer purchase by up to 13%. However, as the number of free episodes consumed goes beyond a threshold, the likelihood of conversion decreases. We conclude with a discussion of managerial implications that can help content providers monetize their serialized digital content products. [ABSTRACT FROM AUTHOR]

An evaluation was conducted to predict the economic and clinical burden of vaccinating all immunocompromised (IC) individuals aged ≥30 years with mRNA-1273 variant-adapted COVID-19 vaccines versus BNT162b2 variant-adapted vaccines in Fall 2023 and Spring 2024 in France. The number of symptomatic SARS-CoV-2 infections, hospitalizations or deaths due to COVID-19, and long COVID cases, costs and quality-adjusted life years (QALYs) were estimated using a static decision-analytic model. Predicted vaccine effectiveness (VE) were based on real-world data from the original and BA.4/5 variant-adapted vaccines, suggesting higher protection against infection and hospitalization with mRNA-1273 vaccines. VE estimates were combined with COVID-19 incidence and probability of COVID-19 severe outcomes. Uncertainty surrounding VE, vaccine coverage, infection incidence, hospitalization and mortality rates, costs and QALYs were evaluated in sensitivity analyses. In an ideal situation where 100% coverage is achieved, the mRNA-1273 variant-adapted vaccine is predicted to prevent an additional 3,882 infections, 357 hospitalizations, 81 deaths, and 326 long COVID cases when compared to BNT162b2 variant-adapted vaccines in 230,000 IC individuals. This translates to €10.1 million cost-savings from a societal perspective and 645 QALYs gained. Results were consistent across all analyses and most sensitive to variations surrounding VE and coverage. These findings highlight the importance of increasing vaccine coverage, and ability to induce higher levels of protection with mRNA-1273 formulations in this vulnerable population.

In this paper, we propose using a mixed genetic-floyd-warshall algorithm in combination with a Floyd-warshall algorithm to model the satisficing behaviour of consumers across spatially differentiated stores. Consumer agents can pick a basket of goods from different stores to either maximize their utility or to “satisfice” by selecting the first basket with a utility that is higher than their satisfaction threshold. The Floyd-warshall algorithm is used to find the shortest path between two chosen stores by considering travel cost. Factors such as price, quality of goods, the cost of travel to the store, consumers' decision-making preferences, and store locations play significant roles in the decision-making process of consumer agents. The model is tested based on mechanisms at the individual level to show how the model works and at the macro-level to reproduce foundational theories in economics.

Abstract Background Chronic migraine (CM) is the most severe and burdensome subtype of migraine. Fremanezumab is a monoclonal antibody that targets the calcitonin gene-related peptide pathway as a migraine preventive therapy. This study aimed to conduct a cost-effectiveness analysis of fremanezumab from a societal perspective in the Netherlands, using a Markov cohort simulation model. Methods The base-case cost-effectiveness analysis adhered to the Netherlands Authority guidelines. Fremanezumab was compared with best supportive care (BSC; acute migraine treatment only) in patients with CM and an inadequate response to topiramate or valproate and onabotulinumtoxinA (Dutch patient group [DPG]). A supportive analysis was conducted in the broader group of CM patients with prior inadequate response to 2–4 different classes of migraine preventive treatments. One-way sensitivity, probabilistic sensitivity, and scenario analyses were conducted. Results Over a lifetime horizon, fremanezumab is cost saving compared with BSC in the DPG (saving of €2514 per patient) and led to an increase of 1.45 quality-adjusted life-years (QALYs). In the broader supportive analysis, fremanezumab was cost effective compared with BSC, with an incremental cost-effectiveness ratio of €2547/QALY gained. Fremanezumab remained cost effective in all sensitivity and scenario analyses. Conclusion In comparison to BSC, fremanezumab is cost saving in the DPG and cost effective in the broader population.

【Objective】Undersea optical cable plays a leading role in international communication. In the planning of undersea optical cable communication system, several groups of possible schemes are often determined according to experience. The scheme with relatively better transmission performance and investment cost is then selected. However, with the development of communication equipment and communication technology, the number of optional modulation formats and optical fibers keeps are increasing. The number of alternative schemes is too large to quickly determine the optimal scheme. Therefore, it is necessary to establish a cost optimization model of undersea optical cable communication system to solve the cost optimal scheme.【Methods】Aiming at the problem of system planning and configuration in the undersea optical cable communication system, a cost optimization model considering the parameters of transmission performance, transmission capacity and supply voltage is established by taking the span length, optical fiber type, number of optical fiber pairs, repeater output power, modulation format and number of practical channels as the variables. The cascaded-lexicographic method is used to solve the cost optimization model.【Results】The results show that the difference between the planning results of the proposed optimization algorithm and full traversal algorithm are no more than 0.34%. However, the number of operations of the optimization algorithm proposed in this paper is far less than that of the full traversal algorithm, with only 1/100 of that of the full traversal algorithm. Secondly, the number of operations of the cascade genelexicography method is less affected by the range of variables than that of the total traversal method. Finally, by comparing the optimal cost of the undersea cable communication system under different span length, it is found that the planning results have no regularity.【Conclusion】The variables always influence each other. The optimal cost design of the system cannot be obtained according to experience, so it needs to establish a model. The algorithm used in this paper is not only accurate, but also uses fewer operation times.

OpenStreetMap (OSM) is a voluntary platform designed to provide free and up-to-date geographic data. Since OSM is based on multisource geographic data provided by the public, the quality of the data has become a concern of researchers. In this study, a unified measure for evaluating OSM data quality was constructed, and the entropy weight method (EWM) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model were used to evaluate the quality of multiscale OSM data in China from 2014 to 2020. In addition to evaluating the data quality, the use of OSM data quality index factors in economic modeling at different spatial scales in China was explored by using a geographic information system (GIS) analysis method and a geographically and temporally weighted regression (GTWR) model. Four machine learning models, SVM, RF, XGBoost and CatBoost, were used to simulate the grid-scale GDP, and the effectiveness of these simulations was discussed. The results showed that (1) the weights of OSM data quality indicator factors vary across different spatial scales. (2) From 2014 to 2020, the quality of national-scale OSM data first increased, then decreased and then gradually stabilized. In addition, the quality of OSM data at the provincial and municipal scales is significantly different, and the distribution is affected by the population and geographical environment. (3) Over time, the spatial clustering characteristics of OSM data quality at different spatial scales in China has continuously strengthened. In addition, the quality of Chinese OSM data displays obvious local spatial autocorrelation characteristics, which are dominated by H-H clustering and L-L clustering. (4) The GTWR model performs well in predicting and revealing the spatiotemporal correlation characteristics between GDP and OSM data quality indicators at the provincial and municipal scales in China. The correlations increase with decreasing spatial scale (provincial to municipal). Moreover, the GDP modeling ability is better in economically underdeveloped Northwest China and economically developed East China. (5) Four machine learning models coupled with road network length completeness, relative linear density, road name attribute completeness, POI name attribute completeness, road network accuracy, road network update frequency, POI update frequency, topological consistency and directional similarity yielded the best grid-scale GDP simulation values. Notably, the CatBoost model provided the best accuracy, and the results further verified that it is feasible to use the proposed OSM data quality index system to predict the regional economic development level in China.

Background: Chronic migraine (CM) is the most severe and burdensome subtype of migraine. Fremanezumab is a monoclonal antibody that targets the calcitonin gene-related peptide pathway as a migraine preventive therapy. This study aimed to conduct a cost-effectiveness analysis of fremanezumab from a societal perspective in the Netherlands, using a Markov cohort simulation model. Methods: The base-case cost-effectiveness analysis adhered to the Netherlands Authority guidelines. Fremanezumab was compared with best supportive care (BSC; acute migraine treatment only) in patients with CM and an inadequate response to topiramate or valproate and onabotulinumtoxinA (Dutch patient group [DPG]). A supportive analysis was conducted in the broader group of CM patients with prior inadequate response to 2–4 different classes of migraine preventive treatments. One-way sensitivity, probabilistic sensitivity, and scenario analyses were conducted. Results: Over a lifetime horizon, fremanezumab is cost saving compared with BSC in the DPG (saving of €2514 per patient) and led to an increase of 1.45 quality-adjusted life-years (QALYs). In the broader supportive analysis, fremanezumab was cost effective compared with BSC, with an incremental cost-effectiveness ratio of €2547/QALY gained. Fremanezumab remained cost effective in all sensitivity and scenario analyses. Conclusion: In comparison to BSC, fremanezumab is cost saving in the DPG and cost effective in the broader population. [ABSTRACT FROM AUTHOR]

Venous thromboembolism (VTE) is a serious complication following orthopedic shoulder surgery; however, research is limited involving the break-even cost-effectiveness of VTE prophylaxis. The purpose of this study was to determine whether the cost of aspirin and enoxaparin would break even for VTE prevention in patients following shoulder surgery. A drug retail database was used to obtain the lowest price for a course of aspirin (81 mg) and enoxaparin (40 mg) to perform a break-even cost analysis. Our institutional purchasing records were then searched to estimate the cost of treating a symptomatic VTE. The TriNetX national database was queried to establish a rate of VTE after shoulder surgery. A break-even cost analysis was performed by determining the absolute risk reduction (ARR). This value was used to calculate the number of patients who are treated to prevent a single VTE while breaking even on cost. Sensitivity analyses were performed for drugs that did not break even at the database-derived VTE rates. Full medication courses of aspirin and enoxaparin were found to cost $1.18 and $125.37, respectively. The cost of treating a symptomatic VTE was determined to be $9407.00. Data from the TriNetX database showed rates of symptomatic VTE following shoulder arthroplasty, hemiarthroplasty, and arthroscopic rotator cuff repair of 1.60%, 1.50%, and 0.68%, respectively. Aspirin broke even on cost for all procedures if the initial rate decreased by an ARR of 0.01% (number needed to treat, 7972). Similarly, enoxaparin broke even for shoulder arthroplasty and hemiarthroplasty if the initial rate of VTE decreased by an ARR of 1.33% (number needed to treat, 75). Enoxaparin did not break even at the initial VTE rate for arthroscopic rotator cuff repair; however, sensitivity analysis found enoxaparin would break even if the drug could be obtained at a cost of ≤$60.00. Enoxaparin broke even if the cost of treating a symptomatic VTE was ≥$20,000.00. The cost of a 3-week course of twice-daily aspirin or once-daily enoxaparin breaks even for VTE prophylaxis following shoulder arthroplasty and hemiarthroplasty if these drugs reduce the VTE rate by a calculated ARR. Given the lower rate of VTE observed for patients undergoing arthroscopic rotator cuff repair, only the 3-week course of aspirin broke even under these conditions. Once-daily enoxaparin did not break even at current market rate. Further research is needed to help determine optimal VTE prophylaxis after shoulder surgery. [ABSTRACT FROM AUTHOR]

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In this paper, we propose using a mixed genetic-floyd-warshall algorithm in combination with a Floyd-warshall algorithm to model the satisficing behaviour of consumers across spatially differentiated stores. Consumer agents can pick a basket of goods from different stores to either maximize their utility or to "satisfice" by selecting the first basket with a utility that is higher than their satisfaction threshold. The Floyd-warshall algorithm is used to find the shortest path between two chosen stores by considering travel cost. Factors such as price, quality of goods, the cost of travel to the store, consumers' decision-making preferences, and store locations play significant roles in the decision-making process of consumer agents. The model is tested based on mechanisms at the individual level to show how the model works and at the macro-level to reproduce foundational theories in economics. [ABSTRACT FROM AUTHOR]

Abstract Introduction The first direct-acting antiviral (DAA) therapies for chronic hepatitis C virus (HCV) infection were reimbursed via Australia’s Pharmaceutical Benefits Scheme (PBS) in March 2016. This was based on the recommendation from the Pharmaceutical Benefits Advisory Committee (PBAC) that the regimens would be acceptably cost-effective at an incremental cost-effectiveness ratio (ICER) no greater than $15,000/quality-adjusted life-year (QALY). Since the initial PBS listings for DAA therapies and subsequent listings of newer DAA treatments such as glecaprevir/pibrentasvir (Maviret®), the demographics and some of the disease characteristics of currently treated patients have markedly changed. This analysis aims to reassess the cost-effectiveness of glecaprevir/pibrentasvir, accounting for the changes to the HCV population currently seeking treatment and incorporating retreatment in first-line failures and the treatment of new infections in previously treated individuals. Methods To assess the cost-effectiveness 7 years after initial listing of DAAs, an update was made to the Markov model used to achieve PBS reimbursement for Viekira-Pak® in May 2016. Amendments to the Viekira-Pak® model include: changes to baseline age and fibrosis distribution of treated patients, and inclusion of retreatment of first-line failures [those not achieving a sustained virologic response (SVR12)] and reinfected individuals. Treatment-related inputs including SVR12 response rates, adverse events, treatment-related disutility, and discontinuations were sourced from pivotal glecaprevir/pibrentasvir clinical trials. Results Using the published price of glecaprevir/pibrentasvir, the ICER is below $15,000/QALY. Conclusions Despite changes in demographics and disease characteristics of treated patients, and changes to the model structure to reflect retreatment in clinical practice in Australia, DAAs remain cost-effective in 2023.

Introduction: The first direct-acting antiviral (DAA) therapies for chronic hepatitis C virus (HCV) infection were reimbursed via Australia's Pharmaceutical Benefits Scheme (PBS) in March 2016. This was based on the recommendation from the Pharmaceutical Benefits Advisory Committee (PBAC) that the regimens would be acceptably cost-effective at an incremental cost-effectiveness ratio (ICER) no greater than $15,000/quality-adjusted life-year (QALY). Since the initial PBS listings for DAA therapies and subsequent listings of newer DAA treatments such as glecaprevir/pibrentasvir (Maviret®), the demographics and some of the disease characteristics of currently treated patients have markedly changed. This analysis aims to reassess the cost-effectiveness of glecaprevir/pibrentasvir, accounting for the changes to the HCV population currently seeking treatment and incorporating retreatment in first-line failures and the treatment of new infections in previously treated individuals. Methods: To assess the cost-effectiveness 7 years after initial listing of DAAs, an update was made to the Markov model used to achieve PBS reimbursement for Viekira-Pak® in May 2016. Amendments to the Viekira-Pak® model include: changes to baseline age and fibrosis distribution of treated patients, and inclusion of retreatment of first-line failures [those not achieving a sustained virologic response (SVR12)] and reinfected individuals. Treatment-related inputs including SVR12 response rates, adverse events, treatment-related disutility, and discontinuations were sourced from pivotal glecaprevir/pibrentasvir clinical trials. Results: Using the published price of glecaprevir/pibrentasvir, the ICER is below $15,000/QALY. Conclusions: Despite changes in demographics and disease characteristics of treated patients, and changes to the model structure to reflect retreatment in clinical practice in Australia, DAAs remain cost-effective in 2023. [ABSTRACT FROM AUTHOR]

This Research Note explores how the major Southeast Asian countries were hit with negative growth rates due to the pandemic in 2020. National income was markedly reduced to a lower level and, even with present growth rates back at pre-pandemic levels, there will be permanent effects. The level trajectory for GDP will necessarily be lower compared to a non-COVID scenario with positive economic growth. Exponential smoothing is applied to GDP data for 2000–2019 with the purpose of estimating incomes for a non-COVID case and the deviation from the actual GDP in order to measure the loss of incomes. For the pandemic years, the negative impacts are most severe for countries with relatively large service sectors (Malaysia, Thailand) and less so for countries with a diversified economy or a large agricultural sector (Vietnam, Laos). [ABSTRACT FROM AUTHOR]

Sulfuryl fluoride (SF) is a fumigant used to eliminate drywood termites (DWT: Kalotermitidae; Froggatt) and other structural pests. Because of its global warming potential, it has been suggested that SF be restricted as a greenhouse gas (GHG). We present an economic model to assess the net social cost of restricting SF. We consider 3 approaches to address DWT control- no treatment, allowing SF fumigation and localized treatments, and only local treatment. Each approach generates private and public benefits and costs. We estimate that the annual damage and home equity loss by DWT in California is US$4.5–16.8 billion without treatment. If fumigation is used on 20% of the houses and local treatments on the others, the combined social cost of treatment, damage, and GHG emissions are between US$1–US$2 billion annually.The annual cost of local treatments only would be between US$3.2 and US$4.9 billion. If the application of SF is severely restricted or banned, the social costs will increase between US$1.43 and US$4.31 billion annually. The implied cost per ton of CO2 eliminated is between US$624 and US$1,465, much above the price range of CO2 in other applications. The restriction/ban has significant equity and environmental effects, impacting low-income individuals living in rented properties and replacing damaged wood in housing will increase GHG emissions. We further recommend the continued use of SF until a comparable whole-structure alternative is developed that fits the parameters of our model. [ABSTRACT FROM AUTHOR]

This article addresses the problem of interrelations between economic factors and military conflicts, examining the role of econometric forecasting in the context of academic management during periods of uncertainty surrounding hostilities. It delves into the two principal theories of classical macroeconomics – neutrality and dichotomy – and explores their applicability to the complex interplay between economic forces and military dynamics. In doing so, it challenges the conventional binary view of conflicts as either war or peace, emphasizing the nuanced gradations that emerge over time. The study highlights the significance of economic advantages in shaping decisions related to military conflicts and underscores the role of econometric forecasting as a critical factor in academic institutions navigating the indirect repercussions of hostilities. The article also presents a comprehensive examination of strategic interactions, introducing distinctions between moves, rounds, and plays in conflict scenarios. Drawing on recent research and publications, the article underscores the importance of understanding the economic principles governing the preservation of peace and forecasting the consequences of such decisions. It references seminal work by economists P. Romer, R. Lucas, R. Barro, I. Salla y Martin, and R. Levine, who laid the foundation for the study of endogenous economic development. The theories of trade and military conflict are two distinct branches in economic analysis and prediction. Trade theory is founded on contractual agreements and mutual benefits, while the military conflict theory centers on competition for dominance. It's important to recognize that various analytical methods exist for modeling equilibrium in such situations, whether they involve active hostilities or the coexistence of armed forces during economic stagnation due to anticipated conflicts. It becomes evident that the decision to initiate or de-escalate a military conflict is primarily driven by economic advantages, even in the domain of academic management. In this context, econometric forecasting techniques play a role in the economic activities of academic institutions, which may experience direct or indirect consequences of hostilities. This is because various sectors must compete for their survival amidst these dynamics. Purpose. The study's objectives include assessing the impact of military budgeting productivity on economic growth using economic and mathematical modeling techniques, with a focus on countries facing uncertainties related to hostilities, evolving budget and tax regulations, and inflation. The article adapts the models of J. Battis and T. Koeli to facilitate this analysis. Methods Macroeconomic Analyses, Economentric Analyses, Trend Analyses Results. Determining the optimal level of taxation to obtain the necessary income for the budgets of academic institutions in conditions of uncertainty in the conduct of hostilities. In conclusion, the study challenges classical economic doctrines by establishing a strong connection between military budget allocation and the real sectors of the economy. It underscores that a nation's economic growth is contingent upon the productivity and effectiveness of its military budgeting, even in a globalized world economic system. This study opens the door to further research in this interdisciplinary field. Keywords: Econometric forecasting, Academic management, Uncertainty, Hostilities, Economic modeling, Endogenous economic development, Academic institutions, Budgeting, Military conflicts, Financial impact, Economic growth, Strategic decision-making, Budget optimization, Risk assessment, Financial efficiency. Methods Macroeconomic Analyses, Economentric Analyses, Trend Analyses fig .: ; tab .: ; bibl .: 9.

Background Limited data are available describing the global impact of COVID-19 vaccines. This study estimated the global public health and economic impact of COVID-19 vaccines before the emergence of the Omicron variant. Methods A static model covering 215 countries/territories compared the direct effects of COVID-19 vaccination to no vaccination during 13 December 2020–30 September 2021. After adjusting for underreporting of cases and deaths, base case analyses estimated total cases and deaths averted, and direct outpatient and productivity costs saved through averted health outcomes. Sensitivity analyses applied alternative model assumptions. Results COVID-19 vaccines prevented an estimated median (IQR) of 151.7 (133.7–226.1) million cases and 620.5 (411.1–698.1) thousand deaths globally through September 2021. In sensitivity analysis applying an alternative underreporting assumption, median deaths averted were 2.1 million. Estimated direct outpatient cost savings were $21.2 ($18.9–30.9) billion and indirect savings of avoided productivity loss were $135.1 ($121.1–206.4) billion, yielding a total cost savings of $155 billion globally through averted infections. Conclusions Using a conservative modeling approach that considered direct effects only, we estimated that COVID-19 vaccines have averted millions of infections and deaths, generating billions of cost savings worldwide, which underscore the continued importance of vaccination in public health response to COVID-19.

Evaluating investment options to enhance earthquake resilience of infrastructure requires a deep understanding of the economic impacts of loss of infrastructure services. This article presents an economic impact assessment of a hypothetical Wellington Fault earthquake event to support the case for a suite of infrastructure resilience enhancements in Wellington, New Zealand. The project builds a comprehensive picture of the event, pre-investment, including electricity disruptions of up to 6 months, temporary population movement of 25%, and business relocation of up to 11% in some industries. MERIT, a dynamic economic analysis tool specifically designed to evaluate "shock" events, estimates the proposed investment options (valued at NZD2016 2.2–2.6 billion) result in reduced event losses of NZD2016 6 billion over 5 years. The project illustrates the need to continue improving modeling capability to support decision processes and to concurrently evolve decision-making processes to match our increasingly sophisticated, dynamic, and multi-dimensional economic evaluation tools. [ABSTRACT FROM AUTHOR]

Sharing economy offers an alternative to customers with on‐demand services and opens up a new way for owners of underutilized resources to generate revenue. However, there is little evidence on whether sharing economy benefits the community of customers and suppliers or not. Our work is motivated by the ongoing stories of conflicts related to the ride sharing platforms (e.g., Uber, Lyft) and the information we collected from industry practitioners. We propose an economic model that describes how a price manipulating ride sharing platform crowdsources and matches supply to demand. Furthermore, our model captures how sharing economy's manipulative power impacts the community of customers and suppliers. Sharing economy platforms allow customers to make purchase decisions after evaluating the search cost, which depends on other customers' purchase decisions and leads to endogenous demand for sharing economy services. A comprehensive theoretical analysis of such models can be extremely challenging due to the endogenous demand. However, we provide such analysis by assuming that the customers evaluate the search cost with the estimated or "exogenous" demand instead of the realized or "endogenous" demand. Moreover, we support our theoretical findings in more realistic settings by performing various numerical analyses. Our results suggest that the financial objective of sharing economy may conflict with the welfare of the affected community, which can be mitigated by government regulations that impose upper bounds on price surges. Our work helps researchers, practitioners, and public policy makers more comprehensively understand the challenges that sharing economy brings and suggests possible regulatory intervention. [ABSTRACT FROM AUTHOR]

Philosophers of science regard the Lotka–Volterra model as an exemplar of model transfer across disciplines. This article traces three cases of the Lotka–Volterra model transfer to economics during the 1960–70s. Each model represents a different kind of methodological attitude towards model transfer. After detailing the historical case studies where the Lotka–Volterra model was transferred to economics and how the economists actually adopted it into their model constructions, the following philosophical discussions on interpretation and justification suggest that formal template or structure alone does not produce successful transdomain model transfer; rather it is subject to the model builders’ intentions and interpretation. In addition to interdisciplinary transfer, intradisciplinarity is also crucial to how models are constructed in order to be resituated in their economic context. [ABSTRACT FROM AUTHOR]

The issue of water resource management has become extremely complicated due to the droughts of the last two decades and the competition among the drinking sector, agricultural sector, and international Hamoun wetland to use water. To analyze the status and help managers in policy analysis and decision-making, this research adopted an integrated water resource model (WEAP) with a decision support system (DSS) approach to study the effect of drought on rural and agricultural development and its economic impacts in the region. So, the Iranian government’s water development projects were simulated within 11 scenarios for a medium-term period (up to the 2030 horizon) and the implications of their implementation for the development of the rural and agricultural sectors were assessed. According to the results, if Afghanistan observes Iran’s water rights, there will still be a great amount of unmet water demand (314.53 million m3) for the agricultural and wetland sectors. However, if this scenario is realized, the unmet demand will decrease by about 196 million m3 versus the status quo and the agricultural sector’s profit will increase by about 314 billion IRR, which will be very helpful for rural development. So, relevant officials should put their best effort into realizing the water right. It is suggested to strengthen water diplomacy between the two main stakeholders in the region in order to reduce the persistence of drought.

Calprotectin is released from neutrophil granulocytes upon activation. Several studies have indicated that plasma calprotectin is an early determinant of bacterial infections, which may serve as a diagnostic tool facilitating decision making on antibiotic treatment. The study objective was to explore the health and economic implications of calprotectin as a predictive tool to initiate antimicrobial therapy in a cohort of critically ill patients. Thus, data obtained from a previously published study on calprotectin as a hypothetical early biomarker of bacterial infections in critically ill patients were evaluated regarding the potential cost-effective impact of early analysis of calprotectin on an earlier start of antibiotic treatment. Under the assumption that calprotectin is used predictively and comparators (white blood cells, procalcitonin, and C-reactive protein) are used diagnostically, a cost-effective impact of EUR 11,000–12,000 per patient would be obtained. If calprotectin would be used predictively and comparators would be used predictively for 50% of patients, it is hypothesized that cost-effectiveness would be between EUR 6000 and 7000 per patient, based on reduced stay in the ICU and general ward, respectively. Furthermore, predictive use of calprotectin seems to reduce both mortality and the length of hospital stay. This health economic analysis on the predictive use of plasma calprotectin, which facilitates clinical decision making in cases of suspected sepsis, indicates that such determination has a cost-saving and life-saving impact on the healthcare system. [ABSTRACT FROM AUTHOR]

Owing to global population growth, more fossil fuels are being used to meet the energy demand, which has led to increases in environmental pollution. Therefore, alternative sources such as renewable energy can be employed to meet some of the needs. Renewable energy has been criticized for its related problems such as downtime and high investment costs. In this study, a system was proposed to address these two problems by integrating wind and hydropower turbines, in Khalkhal, Ardabil, Iran as a case study. To appraise the system, an economic model was generalized, and an Ant Colony Optimization (ACO) method aiming to reduce investment costs was, implemented. The results show that to cover 100% of the required energy demand with a hybrid system of wind and hydropower turbines, the total cost of this system will be $ 202138. Also, the Cost of Energy (COE) per unit of generated power with the hydropower system, wind, and battery storage is gauged at 0.261 $/kWh. For the case study, to supply the required energy demand, 10 wind turbines and 23 kVA batteries, as well as a 14.6 kW hydropower turbine are required. [ABSTRACT FROM AUTHOR]

To help health economic modelers respond to demands for greater use of complex systems models in public health. To propose identifiable features of such models and support researchers to plan public health modeling projects using these models. A working group of experts in complex systems modeling and economic evaluation was brought together to develop and jointly write guidance for the use of complex systems models for health economic analysis. The content of workshops was informed by a scoping review. A public health complex systems model for economic evaluation is defined as a quantitative, dynamic, non‐linear model that incorporates feedback and interactions among model elements, in order to capture emergent outcomes and estimate health, economic and potentially other consequences to inform public policies. The guidance covers: when complex systems modeling is needed; principles for designing a complex systems model; and how to choose an appropriate modeling technique. This paper provides a definition to identify and characterize complex systems models for economic evaluations and proposes guidance on key aspects of the process for health economics analysis. This document will support the development of complex systems models, with impact on public health systems policy and decision making. [ABSTRACT FROM AUTHOR]

This paper seeks to convince historians that investigating how tractability has shaped individual and collective modeling choices in economics is a valuable endeavor. To do so, I first survey the economic methodology literature on tractability, one that grew out of methodologists’ attempts to explain why their authors make unrealistic assumptions. I then compare these accounts with the few instances where 20th century economists discussed tractability explicitly. This short survey suggests that there is a need for historians to document the collective dynamics at work when tractability motives are invoked. I suggest that disentangling theoretical, empirical and computational tractability might be fruitful, but also difficult. I ask how and why choices made for tractability purposes meant to be idiosyncratic and temporary often become collectively entrenched, sometimes creating “tractability traps.” Finally, I consider the existence of “tractability standards” that differ across time and fields.

Biochar is a byproduct of biomass pyrolysis that has been identified as an effective and novel bio-adsorbent for contaminant removal from wastewater. A pyrolysis-chemical activation process was used to convert eucalyptus tree bark to activated biochar, which was then characterized using various techniques (BET, SEM, FTIR, EDX, XRD and pHpzc) and used as a bio-adsorbent to remove hexavalent chromium (Cr(VI)) from aqueous solution. The effects of adsorption process conditions (adsorbate concentration, contact time, adsorbent amount and pH) on Cr(VI) removal efficiency were investigated. The best experimental conditions for the maximum equilibrium uptake of Cr(VI) by activated eucalyptus bark biochar (AEBB) were 10 mg/L, 140 min, 6.0 and 0.3 g/L for initial Cr(VI) concentration, contact time, pH and adsorbent amount, respectively. The AEBB's outstanding performance was aided by its high surface area, well-developed porous structure and dominance of active surface functional groups, as validated by BET, SEM and FTIR. The Freundlich model fitted the equilibrium data the best. The pseudo-second-order model appeared to better predict the kinetics of Cr(VI) adsorption onto AEBB, indicating the dominance of chemisorption.

The issue of water resource management has become extremely complicated due to the droughts of the last two decades and the competition among the drinking sector, agricultural sector, and international Hamoun wetland to use water. To analyze the status and help managers in policy analysis and decision-making, this research adopted an integrated water resource model (WEAP) with a decision support system (DSS) approach to study the effect of drought on rural and agricultural development and its economic impacts in the region. So, the Iranian government's water development projects were simulated within 11 scenarios for a medium-term period (up to the 2030 horizon) and the implications of their implementation for the development of the rural and agricultural sectors were assessed. According to the results, if Afghanistan observes Iran's water rights, there will still be a great amount of unmet water demand (314.53 million m3) for the agricultural and wetland sectors. However, if this scenario is realized, the unmet demand will decrease by about 196 million m3 versus the status quo and the agricultural sector's profit will increase by about 314 billion IRR, which will be very helpful for rural development. So, relevant officials should put their best effort into realizing the water right. It is suggested to strengthen water diplomacy between the two main stakeholders in the region in order to reduce the persistence of drought. [ABSTRACT FROM AUTHOR]

Introduction: Patients with Clostridioides difficile infection (CDI) often experience recurrences (rCDI), which are associated with high morbidity, mortality, and healthcare expenditures. REBYOTA™ (fecal microbiota, live-jslm [FMBL]) is a microbiota-based live biotherapeutic approved for the prevention of rCDI following antibiotic treatment for rCDI. We quantified the budget impact of FMBL during the first 3 years following introduction from a third-party US payer perspective. Methods: A decision-tree model was used to estimate the budget impact of one-course FMBL by comparing costs under the scenario with FMBL to the scenario without FMBL (standard of care) in patients with one or more (≥ 1) recurrences after a primary episode of CDI and had completed ≥ 1 round of antibiotic treatments. Drug costs, rCDI-related medical costs, and budget impact over 1–3 years were estimated in 2022 US dollars. One-way sensitivity analyses were performed. Results: For an insurance plan with a population size of 1,000,000, 468 patients per year were estimated to have ≥ 1 rCDI. The budget impact of one-course FMBL at $9000/course was cost-saving at an» average of -$0.0039 on a per-member-per-month (PMPM) basis, an average of -$8.30 on a per-treated-member-per-month (PTMPM) basis, and a total of -$139,865 on a plan level assuming 5%, 15%, and 20% of patients receive FMBL over 1–3 years, respectively. The scenario with FMBL entry was associated with higher drug costs (difference at $0.0474 PMPM; $101.26 PTMPM; $1,706,445 total plan) and lower rCDI-related medical costs (difference at -$0.0513 PMPM; -$109.56 PTMPM; -$1,846,309 total plan). The budget impact of FMBL in patients at first rCDI was cost-saving at -$0.0139 PMPM, -$84.78 PTMPM, corresponding to an annual savings of $500,022. Conclusions: FMBL has a cost-saving budget impact for a US payer, with higher initial drug costs being offset by savings in rCDI-related medical costs. Greater cost saving was found in patients at first recurrence. [ABSTRACT FROM AUTHOR]

Introduction: Recurrent Clostridioides difficile infection (rCDI) is common and associated with considerable clinical and economic consequences. REBYOTA™ (fecal microbiota, live-jslm [FMBL]) is a microbiota-based live biotherapeutic approved for the prevention of rCDI following antibiotic treatment for rCDI. We sought to evaluate cost-effectiveness of FMBL compared to standard of care (SOC) from a US third-party payer perspective among patients with one or more (≥ 1) recurrences. Methods: A Markov model with a lifetime time horizon was developed. The model population included adult patients who had ≥ 1 recurrence after a primary CDI episode and had completed ≥ 1 round of antibiotics, or had ≥ 2 severe CDI episodes resulting in hospitalization within the last year. The model consisted of six health states with an 8-week model cycle: rCDI, absence of CDI after recurrence, colectomy, ileostomy, ileostomy reversal, and death. Drug costs and rCDI-related medical costs were estimated in 2022 US dollars and discounted at 3% annually. Deterministic sensitivity analyses were performed. Results: Compared to SOC, FMBL at $9000/course resulted in an incremental cost-effectiveness ratio (ICER) of $18,727 per quality-adjusted life year (QALY) gained. The incremental cost was $5336 (FMBL $79,236, SOC $73,900) and the incremental effectiveness was 0.285 QALYs (FMBL 10.346, SOC 10.061). The cumulative drug acquisition and administration costs for the FMBL and SOC arms were $24,245 and $16,876, while rCDI-related medical costs for FMBL and SOC were $54,991 and $57,024, respectively. The ICER in the subgroup of patients at first recurrence was $13,727 per QALY gained. FMBL remained cost-effective across all sensitivity analyses. Conclusions: FMBL was found to be cost-effective compared to SOC for the prevention of rCDI with more benefits among patients at first recurrence, with an ICER far below the payer ICER threshold of $100,000. Patients treated with FMBL experienced higher total QALYs and reduced healthcare resource utilization, including reduced hospitalizations. [ABSTRACT FROM AUTHOR]

Abstract The adoption of a bioenergy crop is affected by various factors, including but not limited to the characteristics of farmers, farm economics, market forces, and physical environment. This study develops a spatially explicit agent‐based model for ascertaining the adoption rate of carinata (Brassica carinata) among the farmers in the Little River Experimental Watershed located in the southern state of Georgia in the United States. Each farmer's adoption behavior is modeled using the profitability difference between traditional crop rotations (with and without carinata at different contract prices), the adoption rate of neighboring farmers, and their land allocation decisions from managing a risky portfolio of enterprises. Carinata production in the winter season once every 3 years has no conflict with the most profitable and popular traditional row crop rotations, such as cotton‐cotton‐cotton and cotton‐cotton‐peanut, to a larger extent. The results show that 28% and 85% of farmers in the watershed will adopt carinata after 33 years at a contract price of $13/bushel (bu) under two different assumptions of low (2.5%) and high (5%) initial neighborhood adoption rates. The proportions of land allocated to carinata to the total farmland under field crops are 38% and 85% after 33 years under the same low and high neighborhood adoption rates, respectively. Our results suggest that fixing the appropriate contract price of carinata will bring additional profits to farmers without any significant foreseeable agronomic risks, thereby increasing the adoption rate of carinata at a regional level.

With the uncertainty concerning the future use of natural resources due to depletion and lack of access caused by the pandemic and recent political events that led to increased prices, nuclear energy may become an alternative efficient energy. NPPs raise serious concerns, including waste management, and any case of an NPP accident has the potential to disrupt the positive impact of energy production in terms of circular economies. Our research analyzed the impact of nuclear incidents as examples of disasters worldwide to decide whether any of the different forms of insurance coverage could be useful in future events. By using 2533 historical records of incidents from 1901 to June 2022, we set out to find the best predictor of damage causes and further observe whether the validation of current forms of insurance may be possible. The disaster subtype and declaration represent the best predictor of the total damage value (adjusted or not) for all types of disasters, including nuclear. The results are important inputs for underwriters working in insurance, including in radioactive waste management, which must consider historical data in order to tailor future contracts, adjusting the cost and coverage to the type of disaster. Our results highlight that with an increase of only one event involving a nuclear source, the total adjusted damages will increase by USD 1,821,087.09 thousand, representing 75% of the damage costs of the rest of the disaster subtypes. The results are useful for public entities to evaluate nuclear energy as a new solution and can help further adapt existing policies to include better responses for waste prevention, reuse and recycling. [ABSTRACT FROM AUTHOR]

Limited data are available describing the global impact of COVID-19 vaccines. This study estimated the global public health and economic impact of COVID-19 vaccines before the emergence of the Omicron variant. A static model covering 215 countries/territories compared the direct effects of COVID-19 vaccination to no vaccination during 13 December 2020–30 September 2021. After adjusting for underreporting of cases and deaths, base case analyses estimated total cases and deaths averted, and direct outpatient and productivity costs saved through averted health outcomes. Sensitivity analyses applied alternative model assumptions. COVID-19 vaccines prevented an estimated median (IQR) of 151.7 (133.7–226.1) million cases and 620.5 (411.1–698.1) thousand deaths globally through September 2021. In sensitivity analysis applying an alternative underreporting assumption, median deaths averted were 2.1 million. Estimated direct outpatient cost savings were $21.2 ($18.9–30.9) billion and indirect savings of avoided productivity loss were $135.1 ($121.1–206.4) billion, yielding a total cost savings of $155 billion globally through averted infections. Using a conservative modeling approach that considered direct effects only, we estimated that COVID-19 vaccines have averted millions of infections and deaths, generating billions of cost savings worldwide, which underscore the continued importance of vaccination in public health response to COVID-19. [ABSTRACT FROM AUTHOR]

Background: Symptomatic venous thromboembolism (VTE) following total ankle arthroplasty (TAA) can cause substantial morbidity and mortality. To prevent this complication, surgeons often prescribe postoperative chemoprophylaxis. However, much controversy exists regarding the efficacy of chemoprophylaxis because of the limited studies exploring its use. Furthermore, even less is known about its cost-effectiveness. Therefore, this study sought to determine the cost-effectiveness of commonly prescribed chemoprophylactic agents using a break-even analysis economic model. Methods: The literature was searched, and an online database was used to identify patients who developed a symptomatic VTE after undergoing TAA. Our institutional records were used to estimate the cost of treating a symptomatic VTE, and an online drug database was used to obtain the cost of commonly prescribed chemoprophylactic agents. A break-even analysis was then performed to determine the final break-even rate necessary to make a drug cost-effective. Results: The low and high rates of symptomatic VTE were determined to be 0.46% and 9.8%. From 2011 to 2021, a total of 3455 patients underwent total ankle arthroplasty. Of these patients, 16 developed a postoperative symptomatic VTE (1.01%). Aspirin 81 mg was cost-effective if the initial symptomatic VTE rates decreased by an absolute risk reduction (ARR) of 0.0003% (NNT = 31 357). Aspirin 325 mg was also cost-effective if the initial rates decreased by an ARR 0.02% (NNT = 5807). Likewise, warfarin (5 mg) was cost-effective at all initial rates with an ARR of 0.02% (NNT = 4480). In contrast, enoxaparin (40 mg) and rivaroxaban (20 mg) were only cost-effective at higher initial symptomatic VTE rates with ARRs of 1.48% (NNT = 68) and 5.36% (NNT = 19). Additional analyses demonstrated that enoxaparin (40 mg) and rivaroxaban (20 mg) become cost-effective when costs of treating a symptomatic VTE are higher than our estimates. Conclusion: Chemoprophylaxis following TAA can be cost-effective. A tailored approach to VTE prophylaxis with cost-effectiveness in mind may be beneficial to the patient and health system. [ABSTRACT FROM AUTHOR]

Many diseases have a sequential treatment pathway. Compared with patients without previous treatment, patients who fail initial treatment may have lower success rates with a second treatment. This phenomenon can be explained by a correlation between treatment effects. We developed a statistical model of covariance for the underlying unobserved correlation between treatments and established a mathematical expression for the magnitude of the latent correlation term. We conducted a simulation study of clinical trials to investigate the correlation between two treatments and explored clinical examples based on published literature to illustrate the identification and evaluation of these correlations. Our simulation study confirmed that a treatment correlation reduces the probability of success for the second treatment, compared with no correlation. We found that treatment correlations may be observable in clinical trials, such as for depression and lung cancer, and the magnitude of correlation may be estimated. We illustrated that treatment correlations can be incorporated into an economic model, with possible impacts on cost-effectiveness results. Additional applications of correlation concepts are also discussed. We evaluated the correlation between treatment effects and our approach can be applied to clinical trial design and economic modeling of sequential clinical treatment pathways. [ABSTRACT FROM AUTHOR]

An evaluation was conducted to predict the economic and clinical burden of vaccinating all immunocompromised (IC) individuals aged ≥30 years with mRNA-1273 variant-adapted COVID-19 vaccines versus BNT162b2 variant-adapted vaccines in Fall 2023 and Spring 2024 in France. The number of symptomatic SARS-CoV-2 infections, hospitalizations or deaths due to COVID-19, and long COVID cases, costs and quality-adjusted life years (QALYs) were estimated using a static decision-analytic model. Predicted vaccine effectiveness (VE) were based on real-world data from the original and BA.4/5 variant-adapted vaccines, suggesting higher protection against infection and hospitalization with mRNA-1273 vaccines. VE estimates were combined with COVID-19 incidence and probability of COVID-19 severe outcomes. Uncertainty surrounding VE, vaccine coverage, infection incidence, hospitalization and mortality rates, costs and QALYs were evaluated in sensitivity analyses. In an ideal situation where 100% coverage is achieved, the mRNA-1273 variant-adapted vaccine is predicted to prevent an additional 3,882 infections, 357 hospitalizations, 81 deaths, and 326 long COVID cases when compared to BNT162b2 variant-adapted vaccines in 230,000 IC individuals. This translates to €10.1 million cost-savings from a societal perspective and 645 QALYs gained. Results were consistent across all analyses and most sensitive to variations surrounding VE and coverage. These findings highlight the importance of increasing vaccine coverage, and ability to induce higher levels of protection with mRNA-1273 formulations in this vulnerable population.