Skip to main content
Article thumbnail
Location of Repository

Appropriate models for the management of infectious diseases

By Helen J. Wearing, Pejman Rohani and Matthew James Keeling

Abstract

Background Mathematical models have become invaluable management tools for epidemiologists, both shedding light on the mechanisms underlying observed dynamics as well as making quantitative predictions on the effectiveness of different control measures. Here, we explain how substantial biases are introduced by two important, yet largely ignored, assumptions at the core of the vast majority of such models.\ud \ud Methods and Findings First, we use analytical methods to show that (i) ignoring the latent period or (ii) making the common assumption of exponentially distributed latent and infectious periods (when including the latent period) always results in underestimating the basic reproductive ratio of an infection from outbreak data. We then proceed to illustrate these points by fitting epidemic models to data from an influenza outbreak. Finally, we document how such unrealistic a priori assumptions concerning model structure give rise to systematically overoptimistic predictions on the outcome of potential management options.\ud \ud Conclusion This work aims to highlight that, when developing models for public health use, we need to pay careful attention to the intrinsic assumptions embedded within classical frameworks

Topics: QA, R
Publisher: Public Library of Science
OAI identifier: oai:wrap.warwick.ac.uk:6730

Suggested articles

Citations

  1. (2003). Death toll continues to climb in Congo Ebola outbreak. doi
  2. (2000). Emerging infectious diseases of wildlife—Threats to biodiversity and human health. doi
  3. (2003). Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong. doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.