The transmission of many infectious diseases is affected by weather and climate variations, particularly
for diseases spread by arthropod vectors such as malaria and dengue. In recent years, epidemiological
studies have demonstrated statistically significant associations between infectious disease incidence
and climate variability. Such research has highlighted the potential for developing climate-based
epidemic early warning systems. This presentation 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 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 spatially structured and unstructured 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. The potential to provide useful early warnings
of future increased and geographically specific dengue risk is investigated. The predictive validity of the
model is evaluated by fitting the GLMM to data from 2001-2007 and comparing probabilistic predictions to
the most recent out-of-sample data in 2008-2009. 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
94% of the 54 microregions that experienced high dengue incidence rates in South East Brazil, during
February - April 2008.
