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| Titel |
Communicating probabilistic climate-sensitive disease risk forecasts |
| VerfasserIn |
Rachel Lowe, David Stephenson, Trevor Bailey, Tim Jupp, Richard Graham, Adrian Tompkins |
| Konferenz |
EGU General Assembly 2011
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 13 (2011) |
| Datensatznummer |
250048439
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| Zusammenfassung |
| The transmission of many infectious diseases can be affected by weather and climate
variability, particularly for diseases spread by arthropod vectors such as malaria and dengue.
Previous epidemiological studies have demonstrated statistically significant associations
between the incidence of certain infectious diseases and climate variability, and have
highlighted the potential for developing climate-based early warning systems for disease
epidemics. To establish how much variation in disease risk can be attributed to climatic
conditions, non-climatic confounding factors should also be considered in the model
parameterization to avoid reporting misleading climate-disease associations. This issue is
sometimes overlooked in climate related disease studies. Due to the lack of spatial resolution
and/or the capability to predict future disease risk (e.g. several months ahead), some previous
models are of limited value for public health decision making. This paper proposes a
framework to model spatio-temporal variation in disease risk using both climate and
non-climate information. The framework is developed in the context of dengue fever in South
East Brazil. Dengue is currently one of the most important emerging tropical diseases and
dengue epidemics impact heavily on Brazilian public health services. A negative binomial
generalised linear mixed model (GLMM) is adopted which makes allowances for
unobserved confounding factors by including structured and unstructured spatial and
temporal random effects. The model successfully accounts for the large amount of
overdispersion found in disease counts. The parameters in this spatio-temporal Bayesian
hierarchical model are estimated using Markov Chain Monte Carlo (MCMC). This
allows posterior predictive distributions for disease risk to be derived for each spatial
location and time period (month/season). Given decision and epidemic thresholds,
probabilistic forecasts can be issued, which are useful for developing epidemic
early warning systems. A novel visualisation technique is presented and used to
communicate ternary probabilistic forecasts for dengue risk using the proposed
forecasting system. A comparison is made to a simple model representative of current
practice for dengue surveillance in Brazil. For a probability decision threshold of
30% and the pre-defined epidemic threshold of 300 cases per 100,000 inhabitants,
successful epidemic alerts would have been issued for 92% of the 54 microregions that
experienced high dengue incidence rates in South East Brazil, during February -
April 2008. The use of seasonal climate forecasts and previous dengue risk could
allow predictions to be made several months ahead of an impending epidemic. |
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