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Dengue: recent past and future threats.

Rogers DJ - Philos. Trans. R. Soc. Lond., B, Biol. Sci. (2015)

Bottom Line: Model accuracy is improved with a greater number of descriptor variables, but is surprisingly unaffected by the spatial resolution of the data.Dengue models with a reduced set of climate variables derived from the HadCM3 global circulation model predictions for the 1980s are improved when risk maps for dengue's two main vectors (Aedes aegypti and Aedes albopictus) are also included as predictor variables; disease and vector models are projected into the future using the global circulation model predictions for the 2020s, 2040s and 2080s.The Garthwaite-Koch corr-max transformation is presented as a novel way of showing the relative contribution of each of the input predictor variables to the map predictions.

View Article: PubMed Central - PubMed

Affiliation: Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK david.rogers@zoo.ox.ac.uk.

ABSTRACT
This article explores four key questions about statistical models developed to describe the recent past and future of vector-borne diseases, with special emphasis on dengue: (1) How many variables should be used to make predictions about the future of vector-borne diseases? (2) Is the spatial resolution of a climate dataset an important determinant of model accuracy? (3) Does inclusion of the future distributions of vectors affect predictions of the futures of the diseases they transmit? (4) Which are the key predictor variables involved in determining the distributions of vector-borne diseases in the present and future? Examples are given of dengue models using one, five or 10 meteorological variables and at spatial resolutions of from one-sixth to two degrees. Model accuracy is improved with a greater number of descriptor variables, but is surprisingly unaffected by the spatial resolution of the data. Dengue models with a reduced set of climate variables derived from the HadCM3 global circulation model predictions for the 1980s are improved when risk maps for dengue's two main vectors (Aedes aegypti and Aedes albopictus) are also included as predictor variables; disease and vector models are projected into the future using the global circulation model predictions for the 2020s, 2040s and 2080s. The Garthwaite-Koch corr-max transformation is presented as a novel way of showing the relative contribution of each of the input predictor variables to the map predictions.

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Risk maps for dengue using meteorological data averaged to (a) one-third degree, (b) one-half degree, (c) one degree or (d) two degrees. See figure 1c for risk map at the original resolution of one-sixth degree and table 2 for accuracy statistics of this series of models.
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RSTB20130562F2: Risk maps for dengue using meteorological data averaged to (a) one-third degree, (b) one-half degree, (c) one degree or (d) two degrees. See figure 1c for risk map at the original resolution of one-sixth degree and table 2 for accuracy statistics of this series of models.

Mentions: With one exception (model results shown in figure 5), all models were run using a bootstrap approach whereby a series of subsamples (usually 300 presence and 300 pseudo-absence points; or 200 of each for the coarsest resolution model in figure 2) were selected from each training set (points selected at random, with replacement) and a model developed for each subsample. One hundred such models were run for each situation, and the results combined at the end to produce a single output risk map.


Dengue: recent past and future threats.

Rogers DJ - Philos. Trans. R. Soc. Lond., B, Biol. Sci. (2015)

Risk maps for dengue using meteorological data averaged to (a) one-third degree, (b) one-half degree, (c) one degree or (d) two degrees. See figure 1c for risk map at the original resolution of one-sixth degree and table 2 for accuracy statistics of this series of models.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4342966&req=5

RSTB20130562F2: Risk maps for dengue using meteorological data averaged to (a) one-third degree, (b) one-half degree, (c) one degree or (d) two degrees. See figure 1c for risk map at the original resolution of one-sixth degree and table 2 for accuracy statistics of this series of models.
Mentions: With one exception (model results shown in figure 5), all models were run using a bootstrap approach whereby a series of subsamples (usually 300 presence and 300 pseudo-absence points; or 200 of each for the coarsest resolution model in figure 2) were selected from each training set (points selected at random, with replacement) and a model developed for each subsample. One hundred such models were run for each situation, and the results combined at the end to produce a single output risk map.

Bottom Line: Model accuracy is improved with a greater number of descriptor variables, but is surprisingly unaffected by the spatial resolution of the data.Dengue models with a reduced set of climate variables derived from the HadCM3 global circulation model predictions for the 1980s are improved when risk maps for dengue's two main vectors (Aedes aegypti and Aedes albopictus) are also included as predictor variables; disease and vector models are projected into the future using the global circulation model predictions for the 2020s, 2040s and 2080s.The Garthwaite-Koch corr-max transformation is presented as a novel way of showing the relative contribution of each of the input predictor variables to the map predictions.

View Article: PubMed Central - PubMed

Affiliation: Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK david.rogers@zoo.ox.ac.uk.

ABSTRACT
This article explores four key questions about statistical models developed to describe the recent past and future of vector-borne diseases, with special emphasis on dengue: (1) How many variables should be used to make predictions about the future of vector-borne diseases? (2) Is the spatial resolution of a climate dataset an important determinant of model accuracy? (3) Does inclusion of the future distributions of vectors affect predictions of the futures of the diseases they transmit? (4) Which are the key predictor variables involved in determining the distributions of vector-borne diseases in the present and future? Examples are given of dengue models using one, five or 10 meteorological variables and at spatial resolutions of from one-sixth to two degrees. Model accuracy is improved with a greater number of descriptor variables, but is surprisingly unaffected by the spatial resolution of the data. Dengue models with a reduced set of climate variables derived from the HadCM3 global circulation model predictions for the 1980s are improved when risk maps for dengue's two main vectors (Aedes aegypti and Aedes albopictus) are also included as predictor variables; disease and vector models are projected into the future using the global circulation model predictions for the 2020s, 2040s and 2080s. The Garthwaite-Koch corr-max transformation is presented as a novel way of showing the relative contribution of each of the input predictor variables to the map predictions.

Show MeSH
Related in: MedlinePlus