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Mathematical Modeling of "Chronic" Infectious Diseases: Unpacking the Black Box.

ABSTRACT: Background:Mathematical models are increasingly used to understand the dynamics of infectious diseases, including "chronic" infections with long generation times. Such models include features that are obscure to most clinicians and decision-makers. Methods:Using a model of a hypothetical active case-finding intervention for tuberculosis in India as an example, we illustrate the effects on model results of different choices for model structure, input parameters, and calibration process. Results:Using the same underlying data, different transmission models produced different estimates of the projected intervention impact on tuberculosis incidence by 2030 with different corresponding uncertainty ranges. We illustrate the reasons for these differences and present a simple guide for clinicians and decision-makers to evaluate models of infectious diseases. Conclusions:Mathematical models of chronic infectious diseases must be understood to properly inform policy decisions. Improved communication between modelers and consumers is critical if model results are to improve the health of populations.

SUBMITTER: Fojo AT

PROVIDER: S-EPMC5716064 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Mathematical Modeling of "Chronic" Infectious Diseases: Unpacking the Black Box.

Fojo Anthony T AT Kendall Emily A EA Kasaie Parastu P Shrestha Sourya S Louis Thomas A TA Dowdy David W DW

Open forum infectious diseases 20170814 4

<h4>Background</h4>Mathematical models are increasingly used to understand the dynamics of infectious diseases, including "chronic" infections with long generation times. Such models include features that are obscure to most clinicians and decision-makers.<h4>Methods</h4>Using a model of a hypothetical active case-finding intervention for tuberculosis in India as an example, we illustrate the effects on model results of different choices for model structure, input parameters, and calibration pro ...[more]

PMID: 29226167

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Project description:The research & development (R&D) of novel therapeutic agents for the treatment of autoimmune diseases is challenged by highly complex pathogenesis and multiple etiologies of these conditions. The number of targeted therapies available on the market is limited, whereas the prevalence of autoimmune conditions in the global population continues to rise. Mathematical modeling of biological systems is an essential tool which may be applied in support of decision-making across R&D drug programs to improve the probability of success in the development of novel medicines. Over the past decades, multiple models of autoimmune diseases have been developed. Models differ in the spectra of quantitative data used in their development and mathematical methods, as well as in the level of "mechanistic granularity" chosen to describe the underlying biology. Yet, all models strive towards the same goal: to quantitatively describe various aspects of the immune response. The aim of this review was to conduct a systematic review and analysis of mathematical models of autoimmune diseases focused on the mechanistic description of the immune system, to consolidate existing quantitative knowledge on autoimmune processes, and to outline potential directions of interest for future model-based analyses. Following a systematic literature review, 38 models describing the onset, progression, and/or the effect of treatment in 13 systemic and organ-specific autoimmune conditions were identified, most models developed for inflammatory bowel disease, multiple sclerosis, and lupus (5 models each). ≥70% of the models were developed as nonlinear systems of ordinary differential equations, others - as partial differential equations, integro-differential equations, Boolean networks, or probabilistic models. Despite covering a relatively wide range of diseases, most models described the same components of the immune system, such as T-cell response, cytokine influence, or the involvement of macrophages in autoimmune processes. All models were thoroughly analyzed with an emphasis on assumptions, limitations, and their potential applications in the development of novel medicines.

Dataset Information

Mathematical Modeling of "Chronic" Infectious Diseases: Unpacking the Black Box.

Publications

Mathematical Modeling of "Chronic" Infectious Diseases: Unpacking the Black Box.

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