How Modelling Can Enhance the Analysis of Imperfect Epidemic Data

Simon Cauchemez; Nathanaël Hoze; Anthony Cousien; Birgit Nikolay; Quirine Ten Bosch

doi:10.1016/j.pt.2019.01.009

How Modelling Can Enhance the Analysis of Imperfect Epidemic Data

Trends Parasitol. 2019 May;35(5):369-379. doi: 10.1016/j.pt.2019.01.009. Epub 2019 Feb 6.

Authors

Simon Cauchemez¹, Nathanaël Hoze², Anthony Cousien², Birgit Nikolay², Quirine Ten Bosch²

Affiliations

¹ Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, 75015 Paris, France; All the authors made equal contributions. Electronic address: simon.cauchemez@pasteur.fr.
² Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, 75015 Paris, France; All the authors made equal contributions.

Abstract

Mathematical models play an increasingly important role in our understanding of the transmission and control of infectious diseases. Here, we present concrete examples illustrating how mathematical models, paired with rigorous statistical methods, are used to parse data of different levels of detail and breadth and estimate key epidemiological parameters (e.g., transmission and its determinants, severity, impact of interventions, drivers of epidemic dynamics) even when these parameters are not directly measurable, when data are limited, and when the epidemic process is only partially observed. Finally, we assess the hurdles to be taken to increase availability and applicability of these approaches in an effort to ultimately enhance their public health impact.

Keywords: epidemic dynamics; mathematical modelling; risk assessment; severity; statistics; transmission.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Review

MeSH terms

Animals
Data Interpretation, Statistical
Epidemiologic Methods*
Humans
Models, Theoretical*
Parasitic Diseases / epidemiology*
Parasitology / methods*