Modularity maximization to design contiguous policy zones for pandemic response.

The health and economic devastation caused by the COVID-19 pandemic has created a significant global humanitarian disaster. Pandemic response policies guided by geospatial approaches are appropriate additions to traditional epidemiological responses when addressing this disaster. However, little is known about finding the optimal set of locations or jurisdictions to create policy coordination zones. In this study, we propose optimization models and algorithms to identify coordination communities based on the natural movement of people. To do so, we develop a mixed-integer quadratic-programming model to maximize the modularity of detected communities while ensuring that the jurisdictions within each community are contiguous. To solve the problem, we present a heuristic and a column-generation algorithm. Our computational experiments highlight the effectiveness of the models and algorithms in various instances. We also apply the proposed optimization-based solutions to identify coordination zones within North Carolina and South Carolina, two highly interconnected states in the U.S. Results of our case study show that the proposed model detects communities that are significantly better for coordinating pandemic related policies than the existing geopolitical boundaries.

Organizational Resilience to Disruption Risks: Developing Metrics and Testing Effectiveness of Operational Strategies.

This study draws from the system resilience literature to propose three different metrics for evaluating the resilience performance of organizations against disruptions: the initial loss due to the disruption, the maximum loss, and the total loss over time. In order to show the usefulness of the developed metrics in practice, we deploy these metrics to study the effectiveness of two resilience strategies: maintaining operational slack and broadening operational scope, by empirically analyzing the performance of manufacturing firms that experienced a disruption during the period from 2005 to the end of 2014. The results show that maintaining certain aspects of operational slack and broadening business scope and geographic scope can affect these different metrics in different ways. Our results help decisionmakers in risk management to gain a better understanding of the conditions under which the recommended strategies actually improve organizations' resilience, as well as the ways in which they may do so.

Optimal Investment in Prevention and Recovery for Mitigating Epidemic Risks.

The worldwide healthcare and economic crisis caused by the COVID-19 pandemic highlights the need for a deeper understanding of investing in the mitigation of epidemic risks. To address this, we built a mathematical model to optimize investments into two types of measures for mitigating the risks of epidemic propagation: prevention/containment measures and treatment/recovery measures. The new model explicitly accounts for the characteristics of networks of individuals, as a critical element of epidemic propagation. Subsequent analysis shows that, to combat an epidemic that can cause significant negative impact, optimal investment in either category increases with a higher level of connectivity and intrinsic loss, but it is limited to a fraction of that total potential loss. However, when a fixed and limited mitigation investment is to be apportioned among the two types of measures, the optimal proportion of investment for prevention and containment increases when the investment limit goes up, and when the network connectivity decreases. Our results are consistent with existing studies and can be used to properly interpret what happened in past pandemics as well as to shed light on future and ongoing events such as COVID-19.

An investigation into online videos as a source of safety hazard reports.

INTRODUCTION: Despite the advantages of video-based product reviews relative to text-based reviews in detecting possible safety hazard issues, video-based product reviews have received no attention in prior literature. This study focuses on online video-based product reviews as possible sources to detect safety hazards. METHODS: We use two common text mining methods - sentiment and smoke words - to detect safety issues mentioned in videos on the world's most popular video sharing platform, YouTube. RESULTS: 15,402 product review videos from YouTube were identified as containing either negative sentiment or smoke words, and were carefully manually viewed to verify whether hazards were indeed mentioned. 496 true safety issues (3.2%) were found. Out of 9,453 videos that contained smoke words, 322 (3.4%) mentioned safety issues, vs. only 174 (2.9%) of the 5,949 videos with negative sentiment words. Only 1% of randomly-selected videos mentioned safety hazards. CONCLUSIONS: Comparing the number of videos with true safety issues that contain sentiment words vs. smoke words in their title or description, we show that smoke words are a more accurate predictor of safety hazards in video-based product reviews than sentiment words. This research also discovers words that are indicative of true hazards versus false positives in online video-based product reviews. Practical applications: The smoke words lists and word sub-groups generated in this paper can be used by manufacturers and consumer product safety organizations to more efficiently identify product safety issues from online videos. This project also provides realistic baselines for resource estimates for future projects that aim to discover safety issues from online videos or reviews.

Semi-online patient scheduling in pathology laboratories.

OBJECTIVE: Nowadays, effective scheduling of patients in clinics, laboratories, and emergency rooms is becoming increasingly important. Hospitals are required to maximize the level of patient satisfaction, while they are faced with lack of space and facilities. An effective scheduling of patients in existing conditions is vital for improving healthcare delivery. The shorter waiting time of patients improves healthcare service quality and efficiency. Focusing on real settings, this paper addresses a semi-online patient scheduling problem in a pathology laboratory located in Tehran, Iran, as a case study. METHODS AND MATERIAL: Due to partial precedence constraints of laboratory tests, the problem is formulated as a semi-online hybrid shop scheduling problem and a mixed integer linear programming model is proposed. A genetic algorithm (GA) is developed for solving the problem and response surface methodology is used for setting GA parameters. A lower bound is also calculated for the problem, and several experiments are conducted to estimate the validity of the proposed algorithm. RESULTS: Based on the empirical data collected from the pathology laboratory, comparison between the current condition of the laboratory and the results obtained by the proposed approach is performed through simulation experiments. The results indicate that the proposed approach can significantly reduce waiting time of the patients and improve operations efficiency. CONCLUSION: The proposed approach has been successfully applied to scheduling patients in a pathology laboratory considering the real-world settings including precedence constraints of tests, constraint on the number of sites or operators for taking tests (i.e. multi-machine problem), and semi-online nature of the problem.