The rising impact of mathematical modelling in epidemiology: antibiotic resistance research as a case study

Abstract

Mathematical modelling of infectious diseases has gradually become part of public health decision-making in recent years. However, the developing status of modelling in epidemiology and its relationship with other relevant scientific approaches have never been assessed quantitatively. Herein, using antibiotic resistance as a case study, 60 published models were analysed. Their interactions with other scientific fields are reported and their citation impact evaluated, as well as temporal trends. The yearly number of antibiotic resistance modelling publications increased significantly between 1990 and 2006. This rise cannot be explained by the surge of interest in resistance phenomena alone. Moreover, modelling articles are, on average, among the most frequently cited third of articles from the journal in which they were published. The results of this analysis, which might be applicable to other emerging public health problems, demonstrate the growing interest in mathematical modelling approaches to evaluate antibiotic resistance.

How Fitness Reduced, Antimicrobial Resistant Bacteria Survive and Spread: A Multiple Pig - Multiple Bacterial Strain Model

“…Such guidelines continuously require updates, and mathematical models are considered to be a valuable tool in the battle against antimicrobial resistance [5], [6]; e.g. to determine optimal dosing strategies [7].…”

Modeling as an approach to identify and manage food safety risks related to parasites in the food chain

“…Early models consisted of simple mathematical descriptions of disease incidence or mortality data that were used to gain insight into the dynamics and progression of epidemics. Considerable advances in the field over the past 50 years have led to the development of more functional modeling tools that may be used to characterize disease spread or to generate and test hypotheses about the transmission of infectious agents (Brauer et al, 2008;Grassly and Fraser, 2008;Keeling and Rohani, 2008;Temime et al, 2008;Hollingsworth, 2009). The advent of computer-assisted representations has vastly enhanced practical applications, and computerized modeling tools are now used routinely to monitor disease outbreaks, for surveillance purposes, in response planning, and to design control measures.…”

Mathematical modeling and the epidemiological research process

“…However, modeling has already made significant contributions to the health sciences (including both clinical medicine and public health) and related disciplines, including biology, mathematics, statistics, bioinformatics, and other fields [1]. A summary of some of these areas of research is highlighted in Fig.…”

“…2): (1) identifying study questions, (2) designing studies and collecting data, (3) analyzing data, and (4) applying research findings to public health. The mathematical modeling process follows four corresponding steps: (1) selecting key components for the model, (2) identifying and validating the inputs that will go into the model, (3) running the model, and (4) interpreting outputs and explaining the applications of the model results. In the following four sections, we describe the applications of models to epidemiology and introduce some of the principles and techniques of modeling.…”