Modelling microbial infection to address global health challenges

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The continued growth of the world’s population and increased interconnectivity heighten the risk that infectious diseases pose for human health worldwide. Epidemiological modelling is a tool that can be used to mitigate this risk by predicting disease spread or quantifying the impact of different intervention strategies on disease transmission dynamics. We illustrate how four decades of methodological advances and improved data quality have facilitated the contribution of modelling to address global health challenges, exemplified by models for the HIV crisis, emerging pathogens and pandemic preparedness. Throughout, we discuss the importance of designing a model that is appropriate to the research question and the available data. We highlight pitfalls that can arise in model development, validation and interpretation. Close collaboration between empiricists and modellers continues to improve the accuracy of predictions and the optimization of models for public health decision-making.

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Fig. 1: Data-driven model prediction to evaluate the impact of manipulatable policy variables.
Fig. 2: Behavioural changes drive outbreak patterns and also respond to them.


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The authors gratefully acknowledge funding from the Notsew Orm Sands Foundation (grants to M.C.F., J.P.T. and A.P.G.), the National Institutes of Health (grant nos. K01 AI141576 and U01 GM087719 to M.C.F. and A.P.G., respectively) and the Natural Sciences and Engineering Research Council of Canada (grant no. RGPIN-04210-2014 to C.T.B.). The authors also thank C. Wells and A. Pandey, both members of the Yale Center for Infectious Disease Modeling and Analysis, for their helpful discussions regarding the HIV and Ebola modelling literature.

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M.C.F. and A.P.G. drafted the initial manuscript. M.C.F., C.T.B., J.P.T. and A.P.G. all critically revised the content.

Correspondence to Alison P. Galvani.

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