ABSTRACT
Learning outcomes: 1. Probabilistic calculations of cost, and profit/loss using standard probability functions 2. Decision tree to find the expected monetary value (EMV) of different options. 3. Monte Carlo simulation for risk analysis. 4. Risk analysis in project management. Learning objectives: Learners will be able to understand and apply the following: how to approach uncertainty in business decisions using probabilistic calculations of cost, and profit/loss using standard probability functions;how to address uncertainty in business decisions by looking forward and reasoning backward, using the decision tree technique and the EMV of different decisions;how to analyse the risk inherent in business decisions by incorporating probability distributions for all critical variables in the form of Monte Carlo simulation;and appreciation of strategic considerations in risk analysis as it applies to project management Case overview/synopsis: The case describes the challenge facing Vilas Birari, the owner and chief executive of Harsh Constructions, a construction company headquartered in Nasik, India. Birari had to decide on the bid for a construction project in September of 2021, during the COVID-19 (COVID) pandemic. Due to successive waves of the pandemic, the state and federal governments announced lockdowns intermittently, causing uncertainty in costs related to labor, material and project completion. The dilemma before Birari was how to set a bid price that was not so low as to incur a loss and not so high as to lose the bid to competitors. The uncertainty made Birari's decision-making complex. The case invites students to help Birari find an optimum bid price by using various quantitative techniques, such as Monte Carlo simulation and decision trees. Complexity academic level: This case is intended for students of management at a master's level, in an elective course on management science, which is often also known as decision science. This compact case can be positioned in the second half of the course, when exploring risk management using computer simulation as a tool. The case serves both as an introduction to using simulation to manage uncertainty as well a contrast with simpler methods that are covered earlier in the course. Supplementary material: Teaching notes are available for educators only. Subject code: CSS 7: Management Science. © 2023, Emerald Publishing Limited.
ABSTRACT
Though available for all age groups in the US, only about half of those vaccinated have obtained a COVID-19 booster. Similar to the unvaccinated, those vaccinated-but-not-boosted may reduce the effectiveness of widespread viral protection. Booster hesitancy differs from general vaccine hesitancy yet remains less researched. We examined booster perceptions across vaccination status using qualitative methodologies. Four focus groups and 11 individual interviews (total n = 32) revealed nuanced changes and differences compared to the first-dose decision. Booster hesitancy stemmed from questions and surprises. Most vaccinated participants accepted the booster, though to varying degrees: enthusiastically with feelings of appreciation and added confidence, passively as an intuitive next step, indifferently following recommendation-"primed" by the yearly flu shot, and reluctantly with worries. The vaccinated-but-not-boosted group expressed confusion about the need for a new shot and discontentment as to why it was not communicated from the start, which coincided with their uncertainty about ending the pandemic. Inadvertently, booster recommendations further polarized non-vaccinated participants, augmenting their skepticism of the original dosages' efficacy or necessity and intensifying their distrust of the government. The findings illuminate the need for adjusting vaccination promotions to better tailor communications (e.g., distinguishing its benefits from the first vaccine and emphasizing the continued risk of COVID-19 spread). Future researchers should further explore the vaccine-accepting-yet-booster-hesitant groups' motivations and risk perceptions to reduce booster rejection.
ABSTRACT
Purpose: The main purpose of this work is to provide an overview of the COVID-19 issue, this article discusses in detail and fully the important and meaningful applications of Decision Sciences to the prevention of COVID-19. Because COVID-19 is an extremely hot topic and the most fascinating question in recent years, the research on this topic is very interesting and noticed by scientists. Design/methodology/approach: In the scope of this study, we first introduce definitions and issues related to COVID-19 and study the negative impacts of COVID-19 diseases on all sectors of society. We then provide a comprehensive introduction to the applied aspects of Decision Science in the prevention of COVID-19. Findings: The findings of our research help people have a correct, complete, overview,and comprehensive view of the COVID-19 issue. All COVID-19 issues are discussed in great detail and completeness in this article. Originality/value: All the issues discussed in this study are original and new in the literary literature. Practical implications: This will help the countries'leaders have the best way to fight the COVID-19 pandemic more effectively and cost-effectively. © 2022 Hindawi Limited. All rights reserved.
ABSTRACT
Coronavirus disease 2019 (COVID-19) forecasts from over 100 models are readily available. However, little published information exists regarding the performance of their uncertainty estimates (i.e. probabilistic performance). To evaluate their probabilistic performance, we employ the classical model (CM), an established method typically used to validate expert opinion. In this analysis, we assess both the predictive and probabilistic performance of COVID-19 forecasting models during 2021. We also compare the performance of aggregated forecasts (i.e. ensembles) based on equal and CM performance-based weights to an established ensemble from the Centers for Disease Control and Prevention (CDC). Our analysis of forecasts of COVID-19 mortality from 22 individual models and three ensembles across 49 states indicates that-(i) good predictive performance does not imply good probabilistic performance, and vice versa; (ii) models often provide tight but inaccurate uncertainty estimates; (iii) most models perform worse than a naive baseline model; (iv) both the CDC and CM performance-weighted ensembles perform well; but (v) while the CDC ensemble was more informative, the CM ensemble was more statistically accurate across states. This study presents a worthwhile method for appropriately assessing the performance of probabilistic forecasts and can potentially improve both public health decision-making and COVID-19 modelling.