dc.contributor.author | Lowe, Rachel | en_GB |
dc.contributor.author | Bailey, Trevor C. | en_GB |
dc.contributor.author | Stephenson, David B. | en_GB |
dc.contributor.author | Jupp, Tim E. | en_GB |
dc.contributor.author | Graham, Richard J. | en_GB |
dc.contributor.author | Barcellos, Christovam | en_GB |
dc.contributor.author | Carvalho, Marilia Sa | en_GB |
dc.date.accessioned | 2013-03-11T15:28:07Z | en_GB |
dc.date.accessioned | 2013-03-20T12:25:27Z | |
dc.date.issued | 2012-08-24 | en_GB |
dc.description.abstract | Previous studies demonstrate statistically significant associations between disease and climate variations, highlighting the potential for developing climate-based epidemic early warning systems. However, limitations include failure to allow for non-climatic confounding factors, limited geographical/temporal resolution, or lack of evaluation of predictive validity. Here, we consider such issues for dengue in Southeast Brazil using a spatio-temporal generalised linear mixed model with parameters estimated in a Bayesian framework, allowing posterior predictive distributions to be derived in time and space. This paper builds upon a preliminary study by Lowe et al. but uses extended, more recent data and a refined model formulation, which, amongst other adjustments, incorporates past dengue risk to improve model predictions. For the first time, a thorough evaluation and validation of model performance is conducted using out-of-sample predictions and demonstrates considerable improvement over a model that mirrors current surveillance practice. Using the model, we can issue probabilistic dengue early warnings for pre-defined 'alert' thresholds. With the use of the criterion 'greater than a 50% chance of exceeding 300 cases per 100,000 inhabitants', there would have been successful epidemic alerts issued for 81% of the 54 regions that experienced epidemic dengue incidence rates in February-April 2008, with a corresponding false alarm rate of 25%. We propose a novel visualisation technique to map ternary probabilistic forecasts of dengue risk. This technique allows decision makers to identify areas where the model predicts with certainty a particular dengue risk category, to effectively target limited resources to those districts most at risk for a given season. | en_GB |
dc.identifier.citation | Vol. 32 (5), pp. 864 - 883 | en_GB |
dc.identifier.doi | 10.1002/sim.5549 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10036/4452 | en_GB |
dc.language.iso | en | en_GB |
dc.subject | climate | en_GB |
dc.subject | dengue | en_GB |
dc.subject | early warning systems | en_GB |
dc.subject | random effects | en_GB |
dc.subject | spatio-temporal modelling | en_GB |
dc.title | The development of an early warning system for climate-sensitive disease risk with a focus on dengue epidemics in Southeast Brazil | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2013-03-11T15:28:07Z | en_GB |
dc.date.available | 2013-03-20T12:25:27Z | |
dc.identifier.issn | 0277-6715 | en_GB |
pubs.declined | 2016-02-23T20:14:58.317+0000 | |
pubs.deleted | 2016-02-23T20:14:58.677+0000 | |
exeter.place-of-publication | England | en_GB |
dc.description | Copyright © 2012 John Wiley & Sons, Ltd. | en_GB |
dc.identifier.eissn | 1097-0258 | en_GB |
dc.identifier.journal | Statistics in Medicine | en_GB |