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Appropriate models for the management of infectious diseases.

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. 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. 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.

SUBMITTER: Wearing HJ

PROVIDER: S-EPMC1181873 | biostudies-literature | 2005 Jul

REPOSITORIES: biostudies-literature

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Appropriate models for the management of infectious diseases.

Wearing Helen J HJ Rohani Pejman P Keeling Matt J MJ

PLoS medicine 20050726 7

<h4>Background</h4>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.<h4>Methods and findings</h4>First, we use analytical methods to show that (i) ignor ...[more]

PMID: 16013892

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Project description:Biogeography is an implicit and fundamental component of almost every dimension of modern biology, from natural selection and speciation to invasive species and biodiversity management. However, biogeography has rarely been integrated into human or veterinary medicine nor routinely leveraged for global health management. Here we review the theory and application of biogeography to the research and management of human infectious diseases, an integration we refer to as 'pathogeography'. Pathogeography represents a promising framework for understanding and decomposing the spatial distributions, diversity patterns and emergence risks of human infectious diseases into interpretable components of dynamic socio-ecological systems. Analytical tools from biogeography are already helping to improve our understanding of individual infectious disease distributions and the processes that shape them in space and time. At higher levels of organization, biogeographical studies of diseases are rarer but increasing, improving our ability to describe and explain patterns that emerge at the level of disease communities (e.g. co-occurrence, diversity patterns, biogeographic regionalisation). Even in a highly globalized world most human infectious diseases remain constrained in their geographic distributions by ecological barriers to the dispersal or establishment of their causal pathogens, reservoir hosts and/or vectors. These same processes underpin the spatial arrangement of other taxa, such as mammalian biodiversity, providing a strong empirical 'prior' with which to assess the potential distributions of infectious diseases when data on their occurrence is unavailable or limited. In the absence of quality data, generalized biogeographic patterns could provide the earliest (and in some cases the only) insights into the potential distributions of many poorly known or emerging, or as-yet-unknown, infectious disease risks. Encouraging more community ecologists and biogeographers to collaborate with health professionals (and vice versa) has the potential to improve our understanding of infectious disease systems and identify novel management strategies to improve local, global and planetary health.

Dataset Information

Appropriate models for the management of infectious diseases.

Publications

Appropriate models for the management of infectious diseases.

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