Mathematical modeling and the epidemiological research process

Abstract

The authors of this paper advocate for the expanded use of mathematical models in epidemiology and provide an overview of the principles of mathematical modeling. Mathematical models can be used throughout the epidemiological research process. Initially they may help to refine study questions by visually expressing complex systems, directing literature searches, and identifying sensitive variables. In the study design phase, models can be used to test sampling strategies, to estimate sample size and power, and to predict outcomes for studies impractical due to time or ethical considerations. Once data are collected, models can assist in the interpretation of results, the exploration of causal pathways, and the combined analysis of data from multiple sources. Finally, models are commonly used in the process of applying research findings to public health practice by estimating population risk, predicting the effects of interventions, and contributing to the evaluation of ongoing programs. Mathematical modeling has the potential to make significant contributions to the field of epidemiology by enhancing the research process, serving as a tool for communicating findings to policymakers, and fostering interdisciplinary collaboration.

Validation of a population coronary disease predictive system: the CASSANDRA model

“…Although disease modelling is not a novel issue,8 the use of validated multivariate predictive models with high accuracy and established ability to discriminate risk may be of particular interest for health promotion planners and public health policy makers 9 10…”

Healthy Living: The European Congress of Epidemiology, 2015

“…The primary outcome was persistent disability at 6 months, measured using the modified Disabilities of the Arm, Shoulder and Hand questionnaire (range 0-11). Outcome was dichotomised, with poor outcome defined as any disability (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11) Background: How patients with comorbidities are treated in daily practice is unknown. Understanding the decision making process of physical therapists (PTs) and exploring the treatment of comorbid patients in daily practice might be a first step to unravel the complexity of treating multi-diseased patients.…”

“…Our results underscore the importance of lifestyle by showing the effects associated with obesity on lifetime risk of type 2 diabetes. 9 Research Program for Diabetes and obesity, University of Helsinki; 10 Folkhalsan Research Center, Finland; Corresponding author's e-mail: bahareh.rasouli@ki.se Aim: Smoking is associated with an increased risk of type 2 diabetes. It has been suggested that smoking may reduce the risk of LADA, possibly through modulating immune response, but few studies have addressed this issue.…”

“…lost lifetime or cost of illness. They may make important contributions for extending the methodological toolbox used in current epidemiology, which has been advocated for recently [9,10] have been performed to compare the results of sewage-based epidemiology to more conventional epidemiological techniques. This study aimed at combining these two measures to provide a validation for sewage-based epidemiology.…”

Multiobjective Dynamic Optimization of Vaccination Campaigns Using Convex Quadratic Approximation Local Search

“…Understanding the transmission characteristics of infectious diseases in communities, regions, and countries can lead to better approaches to the control of such diseases. Mathematical models are used in comparing, planning, implementing, evaluating, and optimizing various detection, prevention, therapy, and control programs [14,4].…”