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The many projected futures of dengue

Nature Reviews Microbiology volume 13, pages 230–239 (2015)Cite this article

Subjects

Key Points

  • Owing to the currently expanding range of dengue, several studies have attempted to predict the future global distribution of this vector-borne disease.

  • The models we review here differ in their modelling approach (for example, statistical versus mechanistic), the quality of the disease data that they use and the choice of variables that are used to model disease distribution.

  • We compare the main approaches that have been used to model the future global distribution of dengue and propose a set of minimum criteria for future projections that, by analogy, are applicable to other vector-borne diseases.

  • These criteria include comprehensive data representing the current distribution of the disease, a model proven to accurately predict this distribution, projections for factors that are important in this model when extrapolating it to the future, and quantification of uncertainty introduced by assumptions inherent to these criteria.

Abstract

Dengue is a vector-borne disease that causes a substantial public health burden within its expanding range. Several modelling studies have attempted to predict the future global distribution of dengue. However, the resulting projections are difficult to compare and are sometimes contradictory because the models differ in their approach, in the quality of the disease data that they use and in the choice of variables that drive disease distribution. In this Review, we compare the main approaches that have been used to model the future global distribution of dengue and propose a set of minimum criteria for future projections that, by analogy, are applicable to other vector-borne diseases.

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Figure 1: Mechanistic models to project the global distribution of dengue.
Figure 2: Statistical models to project the global distribution of dengue.
Figure 3: Proposed framework for modelling the future distribution of dengue and other vector-borne diseases.

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Acknowledgements

J.P.M., G.R.W.W., T.W.S. and S.I.H. receive funding from, and O.J.B. acknowledges the support of, the International Research Consortium on Dengue Risk Assessment Management and Surveillance (IDAMS; European Commission 7th Framework Programme (21803)). O.J.B. is funded by a Biotechnology and Biological Sciences Research Council (BBSRC) studentship. D.M.P. is funded by a Sir Richard Southwood Graduate Scholarship from the Department of Zoology at the University of Oxford, UK. N.G. is funded by a grant from the Bill & Melinda Gates Foundation (OPP1053338). M.U.G.K. is funded by the German Academic Exchange Service (DAAD) through a graduate scholarship. T.W.S. acknowledges funding from the Bill & Melinda Gates Foundation (OPP52250), the Innovative Vector Control Consortium and the US National Institutes of Health (NIH; R01-AI069341, R01-AI091980, R01-GM08322 and P01-AI098670). S.I.H. is funded by a Senior Research Fellowship from the Wellcome Trust (095066). T.W.S., D.L.S. and S.I.H. also acknowledge funding support from the Research and Policy for Infectious Disease Dynamics (RAPIDD) Program of the Science and Technology Directorate, the US Department of Homeland Security and the Fogarty International Center, NIH. Funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. All authors contributed to the writing of the paper.

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Authors and Affiliations

  1. Department of Zoology, Spatial Ecology and Epidemiology Group, University of Oxford, Oxford, OX1 3PS, UK

    Jane P. Messina, Oliver J. Brady, David M. Pigott, Nick Golding, Moritz U. G. Kraemer, David L. Smith & Simon I. Hay

  2. Department of Entomology and Nematology, University of California Davis, Davis, 95616, California, USA

    Thomas W. Scott

  3. Fogarty International Center, National Institutes of Health, Bethesda, 20892, Maryland, USA

    Thomas W. Scott, David L. Smith & Simon I. Hay

  4. Department of Zoology, Environmental Research Group Oxford, University of Oxford, Oxford, OX1 3PS, UK

    G. R. William Wint

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  1. Jane P. Messina
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Correspondence to Jane P. Messina.

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Glossary

Hyper-endemicity

A term used to indicate that a disease is constantly present in a location, with high incidence and/or prevalence rates and affecting all age groups. For dengue, co-circulation of all four serotypes in a location can be an indicator of hyper-endemic transmission.

Covariates

Variables such as temperature or rainfall that may be used to predict disease occurrence. These may be direct or indirect in terms of the hypothesized relationship with the outcome.

Epidemic potential

The reciprocal of the critical density threshold (that is, the average number of adult female mosquitoes per person required for one infectious human case of dengue to give rise to a new one in a susceptible human population). A greater epidemic potential in a location indicates that the climate conditions in that location are such that fewer vectors are needed to effectively spread a vector-borne disease such as dengue.

General circulation models

Mathematical models of the general circulation of the atmosphere or ocean, which constitute an important component of most global climate models. These models can be applied at a variety of temporal scales and used to project climate conditions up to 100 years in the future.

Logistic regression

A probabilistic statistical model that is used to predict a binary response (for example, presence versus absence of a disease) based on a linear combination of hypothesized predictor variables or covariates.

Annual mean vapour pressure

In meteorology, this refers to the partial pressure of water vapour in the atmosphere as measured (or estimated) and averaged over 1 year. It has been used as a measure of humidity in dengue modelling.

Parsimonious model selection

When the goodness of fit of a statistical model is weighted against its complexity when choosing a final model. More-parsimonious models are less complex, with fewer covariates chosen for inclusion. This makes them generally more interpretable and more straightforward to extrapolate into different environments.

Generalized additive models

Statistical models relating a response variable to a set of covariates, modelling the response as the sum of nonlinear relationships with different covariates. The response variable is assigned a specific distribution and a link function used to relate it to the sum of covariate relationships, as in a generalized linear model.

Gross domestic product (GDP) per capita

The value of all final goods and services produced within a given year in a country, divided by the average or mid-year population in the country for that year. This metric is often used as an indicator of the overall standard of living in a country, but it does not convey variation in this standard of living across populations or locations within the country.

Uncertainty estimates

A measure of how uncertain each prediction from a model is. This acts as a quantitative estimate of how well the model is able to make predictions. When predictions are made across a spatial grid, uncertainty estimates can be made for each cell. This enables a map of uncertainty to be produced, showing places where the model performs relatively better or worse.

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Messina, J., Brady, O., Pigott, D. et al. The many projected futures of dengue. Nat Rev Microbiol 13, 230–239 (2015). https://doi.org/10.1038/nrmicro3430

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