Medical capacity investment for epidemic disease: The effects of policymaker's confidence and public trust

Due to the server bed shortage, which has raised ethical dilemmas in the earliest days of the COVID-19 crisis, medical capacity investment has become a vital decision-making issue in the attempt to contain the epidemic. Furthermore, economic strength has failed to explain the significant performance difference across countries in combatting COVID-19. Unlike common diseases, epidemic diseases add substantial unpredictability, complexity, and uncertainty to decision-making. Knowledge miscalibration on epidemiological uncertainties by policymaker's over- and underconfidence can seriously impact policymaking. Ineffective risk communication may lead to conflicting and incoherent information transmission. As a result, public reactions and attitudes could be influenced by policymakers' confidence due to the level of public trust, which eventually affects the degree to which an epidemic spreads. To uncover the impacts of policymakers' confidence and public trust on the medical capacity investment, we establish epidemic diffusion models to characterize how transmission evolves with (and without) vaccination and frame the capacity investment problem as a newsvendor problem. Our results show that if the public fully trusts the public health experts, the policymaker's behavioral bias is always harmful, but its effect on cost increment is marginal. If a policymaker's behavior induces public reactions due to public trust, both the spread of the epidemic and the overall performance will be significantly affected, but such impacts are not always harmful. Decision bias may be beneficial when policymakers are pessimistic or highly overconfident. Having an opportunity to amend initially biased decisions can debias a particular topic but has a limited cost-saving effect.     

Improving the Impact and Implementation of Disaster Education: Programs for Children Through Theory‐Based Evaluation

A main weakness in the evaluation of disaster education programs for children is evaluators’ propensity to judge program effectiveness based on changes in children's knowledge. Few studies have articulated an explicit program theory of how children's education would achieve desired outcomes and impacts related to disaster risk reduction in households and communities. This article describes the advantages of constructing program theory models for the purpose of evaluating disaster education programs for children. Following a review of some potential frameworks for program theory development, including the logic model, the program theory matrix, and the stage step model, the article provides working examples of these frameworks. The first example is the development of a program theory matrix used in an evaluation of ShakeOut, an earthquake drill practiced in two Washington State school districts. The model illustrates a theory of action; specifically, the effectiveness of school earthquake drills in preventing injuries and deaths during disasters. The second example is the development of a stage step model used for a process evaluation of What's the Plan Stan?, a voluntary teaching resource distributed to all New Zealand primary schools for curricular integration of disaster education. The model illustrates a theory of use; specifically, expanding the reach of disaster education for children through increased promotion of the resource. The process of developing the program theory models for the purpose of evaluation planning is discussed, as well as the advantages and shortcomings of the theory‐based approaches.     

The Impact of Population,Contact, and Spatial Heterogeneity on Epidemic Model Predictions

Our objective was to evaluate the effect that complexity in the form of different levels of spatial, population, and contact heterogeneity has in the predictions of a mechanistic epidemic model. A model that simulates the spatiotemporal spread of infectious diseases between animal populations was developed. Sixteen scenarios of foot‐and‐mouth disease infection in cattle were analyzed, involving combinations of the following factors: multiple production‐types (PT) with heterogeneous contact and population structure versus single PT, random versus actual spatial distribution of population units, high versus low infectivity, and no vaccination versus preemptive vaccination. The epidemic size and duration was larger for scenarios with multiple PT versus single PT. Ignoring the actual unit locations did not affect the epidemic size in scenarios with multiple PT/high infectivity, but resulted in smaller epidemic sizes in scenarios using multiple PT/low infectivity. In conclusion, when modeling fast‐spreading epidemics, knowing the actual locations of population units may not be as relevant as collecting information on population and contact heterogeneity. In contrast, both population and spatial heterogeneity might be important to model slower spreading epidemic diseases. Our findings can be used to inform data collection and modeling efforts to inform health policy and planning.     

Managing Technology Selection and Development Risk in Competitive Environments

Managing development decisions for new products based on dynamically evolving technologies is a complex task, especially in highly competitive industries. Product managers often have to choose between introducing an incrementally better, safe new product early and a superior, yet highly risky, product later. Recommendations for managing such performance vs. time‐to‐market trade‐offs often ignore competitive reactions to development decisions. In this paper, we study how a firm could incorporate the presence of a strategic competitor in making technology selection and investment decisions regarding new products. We consider a model in which an innovating firm and its rival can introduce a new product immediately or pursue a more advanced product for later launch. Further, the firm can reduce the uncertainty surrounding product development by dedicating more resources; the effectiveness of this investment depends on the firm's innovative capacity. Our model generates two sets of insights. First, in highly competitive industries, firms can adopt different technologies and effectively use introduction timing to mitigate the effects of price competition. More importantly, the firm could strategically invest in the advanced product to influence its rival's technology choice. We characterize equilibrium development and investment decisions of the firms, and derive innovative capacity hurdles that govern a firm's choice between the risky and safe alternatives. The effects of development flexibility—where firms might have the option to revert to the safe product if the advanced product fails—are also considered.     

State Dependence,Serial Correlation and Heterogeneity in Intertemporal Labor Force Participation of Married Women

A dynamic search framework is developed to analyze the intertemporal labor force participation behavior of married women, using longitudinal data to allow for a rich dynamic structure. The sensitivity to alternative distributional assumptions is evaluated using linear probability and probit models. The dynamic probit models are estimated using maximum simulated likelihood (MSL) estimation, to overcome the computational difficulties inherent in maximum likelihood estimation of models with nontrivial error structures. The results find that participation decisions are characterized by significant state dependence, unobserved heterogeneity, and negative serial correlation in the error component. The hypothesis that fertility decisions are exogenous to women's participation decisions is rejected when dynamics are ignored; however, there is no evidence against this hypothesis in dynamic model specifications. Women's participation response is stronger to permanent than current nonlabor income, reflecting unobserved taste factors.     

Use of Risk Assessment and Likelihood Estimation to Analyze Spatial Distribution Pattern of Respiratory Infection Cases

Obvious spatial infection patterns are often observed in cases associated with airborne transmissible diseases. Existing quantitative infection risk assessment models analyze the observed cases by assuming a homogeneous infectious particle concentration and ignore the spatial infection pattern, which may cause errors. This study aims at developing an approach to analyze spatial infection patterns associated with infectious respiratory diseases or other airborne transmissible diseases using infection risk assessment and likelihood estimation. Mathematical likelihood, based on binomial probability, was used to formulate the retrospective component with some additional mathematical treatments. Together with an infection risk assessment model that can address spatial heterogeneity, the method can be used to analyze the spatial infection pattern and retrospectively estimate the influencing parameters causing the cases, such as the infectious source strength of the pathogen. A Varicella outbreak was selected to demonstrate the use of the new approach. The infectious source strength estimated by the Wells‐Riley concept using the likelihood estimation was compared with the estimation using the existing method. It was found that the maximum likelihood estimation matches the epidemiological observation of the outbreak case much better than the estimation under the assumption of homogeneous infectious particle concentration. Influencing parameters retrospectively estimated using the new approach can be used as input parameters in quantitative infection risk assessment of the disease under other scenarios. The approach developed in this study can also serve as an epidemiological tool in outbreak investigation. Limitations and further developments are also discussed.     

CRITICAL PERIODS DURING CHILDHOOD AND ADOLESCENCE

We identify the ages that constitute sensitive (or critical) periods in children's development towards their adult health status, skills, and human capital. For this, we use data on families migrating into Sweden from countries that are poorer, with less healthy conditions. Late‐life health is proxied by adult height and other adult outcomes. The relation between siblings’ ages at migration and their adult outcomes allows us to estimate the causal effect of conditions at specific childhood ages. We effectively exploit that, for siblings, the migration occurs simultaneously in calendar time but at different developmental stages (ages). We find evidence that the period just before the puberty growth spurt constitutes a critical period for adult height and we find related critical periods for adult cognition, mental health, and education.     

基于SIR传染病模型的技术扩散模型的研究

科技进步推动经济和社会发展是技术扩散来实现的,所以研究技术扩散现象具有十分重要的理论和现实意义.于技术扩散过程类似于传染病的蔓延过程,因此本文利用经典的传染病模型(SIR模型)建立了单一技术在单一企业群中扩散的SIR技术扩散模型、单一技术在多个企业群中扩散的SIR技术扩散模型和多种竞争技术在单一企业群中扩散的SIR技术扩散模型,并通过对三种模型的分析,研究了技术扩散现象的一般规律.     

Performance of weather-conditional rebates under different risk preferences

Retailers who sell seasonal products often face challenges in demand management due to weather uncertainty. In many cases, they make their ordering and pricing decisions prior to the regular selling season but the vast majority of sales do not occur until after the season starts, during which unfavorable weather conditions may result in high monetary losses. To protect against such adverse financial outcomes, retailers may offer weather-linked promotions such as weather rebates and induce customers to make early purchases. Specifically, weather-conditional rebates are incentives offered in an advance promotional period to be paid to the early buyers if the weather state in the regular season is unfavorable. In the presence of seasonal weather uncertainty, risk attitudes of retailers and buyers may play an important role on the effectiveness of these promotions. In this paper, we analyze the performance of weather-conditional rebates by explicitly considering the impact of different risk behaviors. First, we study the case in which the retailer and customers are risk-neutral and show that the weather-conditional rebates are effective in increasing the retailer's profits. Under the assumption of the retailer's risk-neutrality, we conduct a simulation study to investigate the impact of customers' alternative early-purchase behaviors on the performance of the rebate program. Next, we consider a risk-averse retailer. We model the retailer's risk aversion primarily in the mean–variance framework and find that the rebate program can be designed to increase the mean profit and reduce the profit variance simultaneously. Furthermore, by combining the rebate program with a financial instrument such as binary weather options, the retailer can obtain greater benefits from weather-conditional rebates.     

Does disease management work?

Is disease management the "solution of the month" or a valid approach to organizing patient care? In the era of rapid change in health care, disease management is emerging as one technique suited to improving measurable health care outcomes at greater cost effectiveness. By taking a leading role in the process, those physician executives who commit to this approach can greatly enhance the potential of these programs to accomplish the desired results.     

Performance of weather-conditional rebates under different risk preferences

Retailers who sell seasonal products often face challenges in demand management due to weather uncertainty. In many cases, they make their ordering and pricing decisions prior to the regular selling season but the vast majority of sales do not occur until after the season starts, during which unfavorable weather conditions may result in high monetary losses. To protect against such adverse financial outcomes, retailers may offer weather-linked promotions such as weather rebates and induce customers to make early purchases. Specifically, weather-conditional rebates are incentives offered in an advance promotional period to be paid to the early buyers if the weather state in the regular season is unfavorable. In the presence of seasonal weather uncertainty, risk attitudes of retailers and buyers may play an important role on the effectiveness of these promotions. In this paper, we analyze the performance of weather-conditional rebates by explicitly considering the impact of different risk behaviors. First, we study the case in which the retailer and customers are risk-neutral and show that the weather-conditional rebates are effective in increasing the retailer's profits. Under the assumption of the retailer's risk-neutrality, we conduct a simulation study to investigate the impact of customers' alternative early-purchase behaviors on the performance of the rebate program. Next, we consider a risk-averse retailer. We model the retailer's risk aversion primarily in the mean–variance framework and find that the rebate program can be designed to increase the mean profit and reduce the profit variance simultaneously. Furthermore, by combining the rebate program with a financial instrument such as binary weather options, the retailer can obtain greater benefits from weather-conditional rebates.     

Panel Size and Overbooking Decisions for Appointment‐Based Services under Patient No‐Shows

Many service systems that work with appointments, particularly those in healthcare, suffer from high no‐show rates. While there are many reasons why patients become no‐shows, empirical studies found that the probability of a patient being a no‐show typically increases with the patient's appointment delay, i.e., the time between the call for the appointment and the appointment date. This paper investigates how demand and capacity control decisions should be made while taking this relationship into account. We use stylized single server queueing models to model the appointments scheduled for a provider, and consider two different problems. In the first problem, the service capacity is fixed and the decision variable is the panel size; in the second problem, both the panel size and the service capacity (i.e., overbooking level) are decision variables. The objective in both cases is to maximize some net reward function, which reduces to system throughput for the first problem. We give partial or complete characterizations for the optimal decisions, and use these characterizations to provide insights into how optimal decisions depend on patient's no‐show behavior in regards to their appointment delay. These insights especially provide guidance to service providers who are already engaged in or considering interventions such as sending reminders in order to decrease no‐show probabilities. We find that in addition to the magnitudes of patient show‐up probabilities, patients' sensitivity to incremental delays is an important determinant of how demand and capacity decisions should be adjusted in response to anticipated changes in patients' no‐show behavior.     

重大传染病疫情演化情境下应急物资配置决策建模分析:以新冠肺炎疫情为例

基于对新冠肺炎疫情时空分布的分析,从时段、关键事件、传播动力学、空间分布、感染规模、信息特征、医疗资源等7个维度构建了重大传染病疫情演化的5种情境,提出了各种情境下需要解决的5个关键应急物资配置决策问题。综合考虑应急物资配置的空间、信息、物资、供应、需求和网络等特性,分析了每一个决策问题进行建模优化的关键因素。基于这些关键因素构建了一个多周期贝叶斯序贯决策模型,给出了求解步骤和解析解,并结合武汉疫情进行了算例分析,验证了模型的有效性。在重大传染病疫情演化情境下,综合考虑这些关键因素,应用贝叶斯决策分析进行应急物资配置决策建模有利于建立更加符合实际的决策模型,减少决策损失。     

Impact of Demand Uncertainty on Stability of Supplier Alliances in Assembly Models

In their recent paper, Nagarajan and Sošić study an assembly supply chain in which n suppliers sell complementary components to a downstream assembler, who faces a price‐sensitive deterministic demand. Suppliers may form alliances, and each alliance then sells a kit of components to the assembler and determines the price for that kit. The assembler buys the components (kits) from the alliances and sets the selling price of the product. Nagarajan and Sošić consider three modes of competition—supplier Stackelberg, vertical Nash (VN), and assembler Stackelberg models—which correspond to different power structures in the market, and study stable supplier alliances when the assembler faces linear and isoelastic demand. In this paper, we study the impact that demand uncertainty has on stability results obtained in Nagarajan and Sošić. We first analyze models in which all decisions are made before the uncertainty is resolved, and show that the alliance of all suppliers remains stable when demand is isoelastic, or under Stackelberg models when demand is linear. However, demand uncertainty may change stability results when both parties make decisions simultaneously (VN model) and demand is linear. We then extend our results by considering scenarios in which some decisions may be postponed and made after the actual demand is known. When the ordering quantity can be determined after observing the true demand, we show that stable outcomes correspond to those obtained in the deterministic case and uncertainty has no impact on coalition stability; if only the assembler's pricing decision is postponed, we need additional conditions for stability results to carry over in the additive demand model.     

Can Environmental Insurance Succeed Where Other Strategies Fail? The Case of Underground Storage Tanks

Private risk reduction will be socially efficient only when firms are liable for all the damage that they cause. We find that environmental insurance can achieve social efficiency even when two traditional policy instruments—ex post fines and risk management mandates with ex ante fines—do not. Inefficiency occurs with ex post fines, when small firms declare bankruptcy and escape their liabilities, limiting the incentives from this policy tool. Firms ignore mandates to implement efficient risk management because regulatory agencies do not have sufficient resources to monitor every firm. The evolution of the U.S. Environmental Protection Agency's and states’ underground storage tank programs suggests that mandating environmental insurance can address inefficiency due to small firms declaring bankruptcy. Comparing insurance mandates to risk management mandates, the burden on a regulator is lower if all it has to do is to confirm that the firm has insurance rather than that the firm has actually, and effectively, implemented required management practices. For underground storage tanks, we show that insurance lowered toxic releases.     

Experimental Analysis of Neighborhood Effects

Families, primarily female‐headed minority households with children, living in high‐poverty public housing projects in five U.S. cities were offered housing vouchers by lottery in the Moving to Opportunity program. Four to seven years after random assignment, families offered vouchers lived in safer neighborhoods that had lower poverty rates than those of the control group not offered vouchers. We find no significant overall effects of this intervention on adult economic self‐sufficiency or physical health. Mental health benefits of the voucher offers for adults and for female youth were substantial. Beneficial effects for female youth on education, risky behavior, and physical health were offset by adverse effects for male youth. For outcomes that exhibit significant treatment effects, we find, using variation in treatment intensity across voucher types and cities, that the relationship between neighborhood poverty rate and outcomes is approximately linear.     

Linking Process Quality and Resource Usage: An Empirical Analysis

Motivated by an increasing adoption of evidence‐based medical guidelines in the delivery of medical care, we examine whether increased adherence to such guidelines (typically referred to as higher process quality) is associated with reduced resource usage in the course of patient treatment. In this study, we develop a sample of US hospitals and use cardiac care as our context to empirically examine our questions. To measure a patient's resource usage, we use the total length of stay, which includes any additional inpatient stay necessitated by unplanned readmissions within thirty days after initial hospitalization. We find evidence that higher process quality, and more specifically its clinical (as opposed to its administrative) dimensions, are associated with a reduction in resource usage. Moreover, the standardization of care that is achieved via the implementation of medical guidelines, makes this effect more pronounced in less focused environments: higher process quality is more beneficial when the cardiac department's patient population is distributed across a wider range of medical conditions. We explore the implications of these findings for process‐oriented pay‐for‐performance programs, which tie the reimbursement of hospitals to their adherence to evidence‐based medical guidelines.     

Early Sales of Seasonal Products with Weather‐Conditional Rebates

Some retailers of seasonal products adopt weather‐conditional rebate programs to induce early sales and increase profits. In such promotions, customers who buy the product in an advance preselling period are offered rebates if a pre‐specified weather condition is realized during the later normal selling season. We investigate the potential benefits of these programs for retailers. We show that the weather‐conditional rebate program can increase sales by price discriminating among a customer's post‐purchase states. Taking advantage of the early sales, it can also reduce the inventory holding cost and ordering cost, and hence can increase the retailer's expected profits. In addition, we numerically investigate the sensitivity of the rebate program's effectiveness to the model parameters and illustrate its advantages over an advance‐discount policy.     

Supplier Selection Behavior Under Uncertainty: Contextual and Cognitive Effects on Risk Perception and Choice

Buyers often make supplier selection decisions under conditions of uncertainty. Although the analytical aspects of supplier selection are well developed, the psychological aspects are less so. This article uses supply chain management and behavioral decision theories to propose that attributes of the purchasing situation (category difficulty, category importance, and contingent pay) affect cognition that, in turn, affects a supply manager's choice. We conducted a supplier selection behavioral experiment with practicing managers to test the model's hypotheses. When the context involves an important or difficult sourcing category, higher risk perceptions exist that increase preference for a supplier with more certain outcomes, even when that choice has a lower expected payoff. However, the presence of contingent pay decreases risk perceptions through higher perceived supplier control. We also find that a manager's risk propensity increases preferences for a supplier with less certain outcomes regardless of perceived risk. Our model and results provide a theoretical framework for further study into the cognitive aspects of supplier selection behavior and provide insight into biases that influence practicing supply chain managers.