Identifying the most influential employees in infectious disease spread using stochastic mixed integer linear programming optimization.
IF 2.8
3区 医学
Q2 HEALTH POLICY & SERVICES
引用次数: 0
Abstract
Managers aim to minimize financial losses during pandemics by preventing the spread of infections among employees. Identifying employees who are most likely to infect others is crucial for implementing effective preventive measures. To do so, the infectious among employees should be modelled at first. Secondly, the members who directly or indirectly infect the maximum number of other employees are identified. This study addresses this problem as an Influence Maximization (IM) problem using a Mixed Integer Linear Programming (MILP) for analytical optimization. The MILP optimization guarantees the global optimal solution. However, given the stochastic nature of influence, the IM problem is formulated as a stochastic optimization based on a limited number of scenarios. Therefore, the whole stochastic nature of the influence process may not be captured. It is of high importance to check whether the number of scenarios used in stochastic MILP is adequate. This study evaluates it by simulating the results with numerous scenarios to assess the gap between the objective function and the exact expected value. The proposed methodology is tested on real case studies from companies of varying sizes, including a small-scale company with twelve employees and a larger-scale company with more than thousand employees. The results discuss the efficiency and adequacy of the scenarios used in the stochastic MILP approach.
利用随机混合整数线性规划优化方法识别传染病传播中最具影响力的员工。
管理人员的目标是通过防止传染病在员工之间传播来最大限度地减少流行病期间的经济损失。确定最有可能感染他人的员工对于实施有效的预防措施至关重要。要做到这一点，首先应该模拟员工之间的传染性。其次，确定直接或间接感染最多其他员工的成员。本研究将此问题作为影响最大化（IM）问题，使用混合整数线性规划（MILP）进行分析优化。MILP优化保证了全局最优解。然而，考虑到影响的随机性质，IM问题被表述为基于有限数量情景的随机优化。因此，可能无法捕捉到影响过程的整个随机性质。检查随机MILP中使用的场景数量是否足够是非常重要的。本研究通过模拟多个场景的结果来评估目标函数与准确期望值之间的差距。所提出的方法在不同规模的公司的真实案例研究中进行了测试，包括拥有12名员工的小型公司和拥有数千名员工的大型公司。结果讨论了在随机MILP方法中使用的场景的有效性和充分性。
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来源期刊
Health Care Management Science
HEALTH POLICY & SERVICES-
CiteScore
7.20
自引率
5.60%
发文量
40
期刊介绍：
Health Care Management Science publishes papers dealing with health care delivery, health care management, and health care policy. Papers should have a decision focus and make use of quantitative methods including management science, operations research, analytics, machine learning, and other emerging areas. Articles must clearly articulate the relevance and the realized or potential impact of the work. Applied research will be considered and is of particular interest if there is evidence that it was implemented or informed a decision-making process. Papers describing routine applications of known methods are discouraged.
Authors are encouraged to disclose all data and analyses thereof, and to provide computational code when appropriate.
Editorial statements for the individual departments are provided below.
Health Care Analytics
Departmental Editors:
Margrét Bjarnadóttir, University of Maryland
Nan Kong, Purdue University
With the explosion in computing power and available data, we have seen fast changes in the analytics applied in the healthcare space. The Health Care Analytics department welcomes papers applying a broad range of analytical approaches, including those rooted in machine learning, survival analysis, and complex event analysis, that allow healthcare professionals to find opportunities for improvement in health system management, patient engagement, spending, and diagnosis. We especially encourage papers that combine predictive and prescriptive analytics to improve decision making and health care outcomes.
The contribution of papers can be across multiple dimensions including new methodology, novel modeling techniques and health care through real-world cohort studies. Papers that are methodologically focused need in addition to show practical relevance. Similarly papers that are application focused should clearly demonstrate improvements over the status quo and available approaches by applying rigorous analytics.
Health Care Operations Management
Departmental Editors:
Nilay Tanik Argon, University of North Carolina at Chapel Hill
Bob Batt, University of Wisconsin
The department invites high-quality papers on the design, control, and analysis of operations at healthcare systems. We seek papers on classical operations management issues (such as scheduling, routing, queuing, transportation, patient flow, and quality) as well as non-traditional problems driven by everchanging healthcare practice. Empirical, experimental, and analytical (model based) methodologies are all welcome. Papers may draw theory from across disciplines, and should provide insight into improving operations from the perspective of patients, service providers, organizations (municipal/government/industry), and/or society.
Health Care Management Science Practice
Departmental Editor:
Vikram Tiwari, Vanderbilt University Medical Center
The department seeks research from academicians and practitioners that highlights Management Science based solutions directly relevant to the practice of healthcare. Relevance is judged by the impact on practice, as well as the degree to which researchers engaged with practitioners in understanding the problem context and in developing the solution. Validity, that is, the extent to which the results presented do or would apply in practice is a key evaluation criterion. In addition to meeting the journal’s standards of originality and substantial contribution to knowledge creation, research that can be replicated in other organizations is encouraged. Papers describing unsuccessful applied research projects may be considered if there are generalizable learning points addressing why the project was unsuccessful.
Health Care Productivity Analysis
Departmental Editor:
Jonas Schreyögg, University of Hamburg
The department invites papers with rigorous methods and significant impact for policy and practice. Papers typically apply theory and techniques to measuring productivity in health care organizations and systems. The journal welcomes state-of-the-art parametric as well as non-parametric techniques such as data envelopment analysis, stochastic frontier analysis or partial frontier analysis. The contribution of papers can be manifold including new methodology, novel combination of existing methods or application of existing methods to new contexts. Empirical papers should produce results generalizable beyond a selected set of health care organizations. All papers should include a section on implications for management or policy to enhance productivity.
Public Health Policy and Medical Decision Making
Departmental Editors:
Ebru Bish, University of Alabama
Julie L. Higle, University of Southern California
The department invites high quality papers that use data-driven methods to address important problems that arise in public health policy and medical decision-making domains. We welcome submissions that develop and apply mathematical and computational models in support of data-driven and model-based analyses for these problems.
The Public Health Policy and Medical Decision-Making Department is particularly interested in papers that:
Study high-impact problems involving health policy, treatment planning and design, and clinical applications;
Develop original data-driven models, including those that integrate disease modeling with screening and/or treatment guidelines;
Use model-based analyses as decision making-tools to identify optimal solutions, insights, recommendations.
Articles must clearly articulate the relevance of the work to decision and/or policy makers and the potential impact on patients and/or society. Papers will include articulated contributions within the methodological domain, which may include modeling, analytical, or computational methodologies.
Emerging Topics
Departmental Editor:
Alec Morton, University of Strathclyde
Emerging Topics will handle papers which use innovative quantitative methods to shed light on frontier issues in healthcare management and policy. Such papers may deal with analytic challenges arising from novel health technologies or new organizational forms. Papers falling under this department may also deal with the analysis of new forms of data which are increasingly captured as health systems become more and more digitized.
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