Mathematical Modeling and Models for Optimal Decision-Making in Health Care

Open-sourced modeling and simulating tools for decision-makers during an emerging pandemic or epidemic - Systematic evaluation of utility and usability: A scoping review update

Introduction

The COVID-19 pandemic had devastating effects on health systems globally. Emerging infectious diseases and pandemics will persist as a global health threat and preparedness for an evidence based response becomes challenging for decision makers. Epidemiological modeling can and has supported decision-making throughout pandemics. This study provides an update of the review "Publicly available software tools for decision-makers during an emergent epidemic-Systematic evaluation of utility and usability"1.

Research question

What epidemiological modeling tools for decision-makers are open-sourced available for the usage in emerging epidemics or pandemics and how useful and user-friendly are these tools?

Methods

A scoping review was conducted. We identified relevant studies through a search of peer-reviewed (Medline Ovid, Embase Ovid, PubMed, Cochrane) and gray literature databases, search engines such as Google, searches through stakeholder websites as well as expert consultations.

Results

Of the 66 identified epidemiological modeling tools, 29 were included and qualitatively assessed using five-point-rating scales. The tools showed a good baseline of user-friendliness with variations in assessed components, features and utility. Room for improvement was found, specifically the capability to incorporate external data sources, detailed population descriptions, and geographic resolution.

Discussion

Development efforts should prioritize clear communication of uncertainties and expert review processes. Trainings for specific tools should be considered.

Conclusion

Tool usage can enhance decision-making when adapted to the user's needs and purpose. They should be consulted critically rather than followed blindly.

A blueprint for a new commercial driving epidemiology: An emerging paradigm grounded in integrative exposome and network epistemologies

Excess health and safety risks of commercial drivers are largely determined by, embedded in, or operate as complex, dynamic, and randomly determined systems with interacting parts. Yet, prevailing epidemiology is entrenched in narrow, deterministic, and static exposure-response frameworks along with ensuing inadequate data and limiting methods, thereby perpetuating an incomplete understanding of commercial drivers' health and safety risks. This paper is grounded in our ongoing research that conceptualizes health and safety challenges of working people as multilayered "wholes" of interacting work and nonwork factors, exemplified by complex-systems epistemologies. Building upon and expanding these assumptions, herein we: (a) discuss how insights from integrative exposome and network-science-based frameworks can enhance our understanding of commercial drivers' chronic disease and injury burden; (b) introduce the "working life exposome of commercial driving" (WLE-CD)-an array of multifactorial and interdependent work and nonwork exposures and associated biological responses that concurrently or sequentially impact commercial drivers' health and safety during and beyond their work tenure; (c) conceptualize commercial drivers' health and safety risks as multilayered networks centered on the WLE-CD and network relational patterns and topological properties-that is, arrangement, connections, and relationships among network components-that largely govern risk dynamics; and (d) elucidate how integrative exposome and network-science-based innovations can contribute to a more comprehensive understanding of commercial drivers' chronic disease and injury risk dynamics. Development, validation, and proliferation of this emerging discourse can move commercial driving epidemiology to the frontier of science with implications for policy, action, other working populations, and population health at large.

Improving resource allocation in the precision medicine Era: a simulation-based approach using R

The application of personalized medicine in developing countries is a major challenge, especially for those with poor economic status. A critical factor in improving the application of personalized medicine is the efficient allocation of resources. In healthcare systems, optimizing resource allocation without compromising patient care is paramount. This tutorial employs a simulation-based approach to evaluate the efficiency of bed allocation within a hospital setting. Utilizing a patient arrival model with an exponential distribution, we simulated patient trajectories to examine system bottlenecks, particularly focusing on waiting times. Initial simulations painted a scenario of an 'unstable' system, where waiting times and queue lengths surged due to the limited number of available beds. This research offers insights for hospital management on resource optimization leading to improved patient care.

Mathematical modeling and machine learning for public health decision-making: the case of breast cancer in Benin

Breast cancer is the most common type of cancer in women. Its mortality rate is high due to late detection and cardiotoxic effects of chemotherapy. In this work, we used the Support Vector Machine (SVM) method to classify tumors and proposed a new mathematical model of the patient dynamics of the breast cancer population. Numerical simulations were performed to study the behavior of the solutions around the equilibrium point. The findings revealed that the equilibrium point is stable regardless of the initial conditions. Moreover, this study will help public health decision-making as the results can be used to minimize the number of cardiotoxic patients and increase the number of recovered patients after chemotherapy.

Meta-Decision in Healthcare

Meta-decision as a junction between evidence and its rightful implementation is suggested in this review as a structured framework applied in healthcare, valuable to clinicians and healthcare decision-makers. The process of meta-decision requires optimum measurements to provide data necessary for identifying and developing decision alternatives and explicitly reflect on its value and choose the optimum decision. The location of value in the meta-decision framework is core component. Of equal importance are prerequisites for decision-makers' abilities to make meta-decisions and focus on optimum team environments. As well as improving their decision-making process through reflection and learning.

Utility-Based Multicriteria Model for Screening Patients under the COVID-19 Pandemic

In this paper, a utility-based multicriteria model is proposed to support the physicians to deal with an important medical decision—the screening decision problem—given the squeeze put on resources due to the COVID-19 pandemic. Since the COVID-19 emerged, the number of patients with an acute respiratory failure has increased in the health units. This chaotic situation has led to a deficiency in health resources. Thus, this study, using the concepts of the multiattribute utility theory (MAUT), puts forward a mathematical model to aid physicians in the screening decision problem. The model is used to generate which of the three alternatives is the best one for where patients with suspected COVID-19 should be treated, namely, an intensive care unit (ICU), a hospital ward, or at home in isolation. Also, a decision information system, called SIDTriagem, is constructed and illustrated to operate the mathematical model proposed.

Integrative and quantitative bioenergetics: Design of a study to assess the impact of the gut microbiome on host energy balance

The literature is replete with clinical studies that characterize the structure, diversity, and function of the gut microbiome and correlate the results to different disease states, including obesity. Whether the microbiome has a direct impact on obesity has not been established. To address this gap, we asked whether the gut microbiome and its bioenergetics quantitatively change host energy balance. This paper describes the design of a randomized crossover clinical trial that combines outpatient feeding with precisely controlled metabolic phenotyping in an inpatient metabolic ward. The target population was healthy, weight-stable individuals, age 18-45 and with a body mass index ≤30 kg/m2. Our primary objective was to determine within-participant differences in energy balance after consuming a control Western Diet versus a Microbiome Enhancer Diet intervention specifically designed to optimize the gut microbiome for positive impacts on host energy balance. We assessed the complete energy-balance equation via whole-room calorimetry, quantified energy intake, fecal energy losses, and methane production. We implemented conditions of tight weight stability and balance between metabolizable energy intake and predicted energy expenditure. We explored key factors that modulate the balance between host and microbial nutrient accessibility by measuring enteroendocrine hormone profiles, appetite/satiety, gut transit and gastric emptying. By integrating these clinical measurements with future bioreactor experiments, gut microbial ecology analysis, and mathematical modeling, our goal is to describe initial cause-and-effect mechanisms of gut microbiome metabolism on host energy balance. Our innovative methods will enable subsequent studies on the interacting roles of diet, the gut microbiome, and human physiology.

CLINICALTRIALSGOV IDENTIFIER

NCT02939703. The present study reference can be found here: https://clinicaltrials.gov/ct2/show/NCT02939703.