How Modelling Can Enhance the Analysis of Imperfect Epidemic Data

Simon Cauchemez*, Nathanaël Hoze, Anthony Cousien, Birgit Nikolay, Quirine ten bosch

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)369-379
Number of pages11
JournalTrends in Parasitology
Volume35
Issue number5
DOIs
Publication statusPublished - May 2019
Externally publishedYes

Keywords

  • epidemic dynamics
  • mathematical modelling
  • risk assessment
  • severity
  • statistics
  • transmission

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