Plug-and-play inference for disease dynamics: measles in large and small populations as a case study

By Daihai He, Edward L. Ionides, Aaron M. King. Journal of the Royal Society Interface, 2009

308 citations recorded by the source corpus.

Statistical inference for mechanistic models of partially observed dynamic systems is an active area of research. Most existing inference methods place substantial restrictions upon the form of models that can be fitted and hence upon the nature of the scientific hypotheses that can be entertained and the data that can be used to evaluate them. In contrast, the so-called plug-and-play methods require only simulations from a model and are thus free of such restrictions. We show the utility of the plug-and-play approach in the context of an investigation of measles transmission dynamics. Our novel methodology enables us to ask and answer questions that previous analyses have been unable to address. Specifically, we demonstrate that plug-and-play methods permit the development of a modelling and inference framework applicable to data from both large and small populations. We thereby obtain novel insights into the nature of heterogeneity in mixing and comment on the importance of including extra-demographic stochasticity as a means of dealing with environmental stochasticity and model misspecification. Our approach is readily applicable to many other epidemiological and ecological systems.

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

View Paper: https://www.semanticscholar.org/paper/91b6e6a867abca446f467eeadcdbbfda198af6a1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

Citations in the corpus

Optimal rate of direct estimators in systems of ordinary differential equations linear in functions of the parameters (2013, background): “models for the blood coagulation process ([27]); problems in chemistry ([40]); gene regulatory networks ([5]); models describing the spread of infectious diseases ([25, 29]); calcium measurements analysis ([39]); pharmacokinetic models ([14]).”