Mathematical models of infectious disease transmission

By Nicholas C. Grassly, Christophe Fraser. Nature Reviews Microbiology, 2008

779 citations recorded by the source corpus.

Mathematical analysis and modelling is central to infectious disease epidemiology. Here, we provide an intuitive introduction to the process of disease transmission, how this stochastic process can be represented mathematically and how this mathematical representation can be used to analyse the emergent dynamics of observed epidemics. Progress in mathematical analysis and modelling is of fundamental importance to our growing understanding of pathogen evolution and ecology. The fit of mathematical models to surveillance data has informed both scientific research and health policy. This Review is illustrated throughout by such applications and ends with suggestions of open challenges in mathematical epidemiology.

Subfields: Modeling and Simulation, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/b1a621e5d21484ef944ac83e4ec6db680a9e1dd4

Citations in the corpus

• Sensitivity analysis of infectious disease models: methods, advances and their application (2013, neutral): “Recent advances in computational science and information technology have made widespread applications of mathematical modelling possible [2], yet model outputs often have complex, nonlinear relationships with model parameters, and inappropriate choices of parameter values can lead to bias in model o”
• Mathematical perspective of Covid-19 pandemic: Disease extinction criteria in deterministic and stochastic models (2020, neutral): “Mathematical model of infectious disease is central in epidemiology and might play an important role in forecasting the epidemiological burden of infectious disease [11].”