Dengue outlook for the World Cup in Brazil: an early warning model framework driven by real-time seasonal climate forecasts

Lowe, Rachel; Barcellos, Christovam; Coelho, Caio A. S.; Bailey, Trevor; Coelho, Giovanini Evelim; Graham, Richard; Jupp, Tim E.; Ramalho, Walter Massa; Carvalho, Marília Sá; Stephenson, David B.; Rodó, Xavier

doi:10.1016/s1473-3099(14)70781-9

Cited by 121 publications

(136 citation statements)

References 33 publications

Supporting

Mentioning

128

Contrasting

Unclassified

Order By: Relevance

“…When epidemics started in the early months of the year, they were more intense and with greater capacity to spread. Epidemics that begin later in the year may not succeed in maintaining a high number of cases due to declining temperature and rainfall and thus a decrease in the vector population 38 .…”

Section: Discussionmentioning

confidence: 99%

Difusão espaço-tempo do dengue no Município do Rio de Janeiro, Brasil, no período de 2000-2013

et al. 2017

Self Cite

View full text Add to dashboard Cite

Resumo: A cidade do Rio de Janeiro, Brasil, apresenta elevado potencial de receptividade para a introdução, disseminação e persistência da transmissão de dengue. A ocupação do município conformou um mosaico heterogêneo e diversificado, com distribuição vetorial diferenciada entre e dentro dos bairros, proporcionando epidemias distintas nesta escala de observação. Este trabalho busca identificar essas epidemias e o padrão de difusão da transmissão do dengue sob a dimensão de tempo e espaço. Foi utilizado um modelo para a identificação de epidemias considerando os anos e meses de pico epidêmico, a distribuição espacial e a permanência das epidemias levando-se em conta o período de janeiro de 2000 a dezembro de 2013. Foram contabilizados 495 picos epidêmicos, e na escala de tempo evidenciou-se maior ocorrência nos meses de março, abril e fevereiro, respectivamente. Alguns bairros parecem apresentar um quadro persistente de incidência de dengue e o comportamento da difusão da doença permite identificar trajetórias e meses oportunos para a intervenção.

show abstract

Section: Discussionmentioning

confidence: 99%

Difusão espaço-tempo do dengue no Município do Rio de Janeiro, Brasil, no período de 2000-2013

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…Further, autoregressive terms with only one week or month lag offer little, if any, advance warning of an impending epidemic as the collation of such data may not be feasible in advance of the time period for which the forecast is valid. In practice, seasonal forecasts of the climate, with lead times up to several months ahead, would be the only feasible option to provide timely early warning of dengue epidemics (Lowe et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Quantifying the added value of climate information in a spatio-temporal dengue model

Lowe

Cazelles

Rácz

et al. 2015

Stoch Environ Res Risk Assess

Self Cite

View full text Add to dashboard Cite

Dengue is the world's most important vector-borne viral disease. The dengue mosquito and virus are sensitive to climate variability and change. Temperature, humidity and precipitation influence mosquito biology, abundance and habitat, and the virus replication speed. In this study, we develop a modelling procedure to quantify the added value of including climate information in a dengue model for the 76 provinces of Thailand, from 1982-2013. We first developed a seasonal-spatial model, to account for dependency structures from one month to the next and between provinces. We then tested precipitation and temperature variables at varying time lags, using linear and nonlinear functional forms, to determine an optimum combination of time lags to describe dengue relative risk. Model parameters were estimated using Integrated Nested Laplace Approximation (INLA).This approach provides a novel opportunity to perform model selection in a Bayesian framework, while accounting for underlying spatial and temporal dependency structures and linear or nonlinear functional forms. We quantified the additional variation explained by interannual climate variations, above that provided by the seasonal-spatial model. Overall, an additional 8% of the variance in dengue relative risk can be explained by accounting for interannual variations in precipitation and temperature in the previous month. The inclusion of nonlinear functions of climate in the model framework improved the model for 79% of the provinces. Therefore, climate forecast information could significantly contribute to a national dengue early warning system in Thailand.

show abstract

“…Dengue forecasts and early warning systems have been proposed using a number of approaches, including autoregressive integrative moving average (ARIMA) models [15,16], regression models [17][18][19], a spatio-temporal hierarchical Bayesian model [20], a percentile rank model [21] and an empirical Bayes model [21]. Other approaches have been used to forecast influenza, including stochastic agentbased models [22,23] and meta-population models [24].…”

Section: Introductionmentioning

confidence: 99%

Superensemble forecasts of dengue outbreaks

Yamana¹,

Kandula²,

Shaman³

2016

J. R. Soc. Interface.

111

View full text Add to dashboard Cite

In recent years, a number of systems capable of predicting future infectious disease incidence have been developed. As more of these systems are operationalized, it is important that the forecasts generated by these different approaches be formally reconciled so that individual forecast error and bias are reduced. Here we present a first example of such multi-system, or superensemble, forecast. We develop three distinct systems for predicting dengue, which are applied retrospectively to forecast outbreak characteristics in San Juan, Puerto Rico. We then use Bayesian averaging methods to combine the predictions from these systems and create superensemble forecasts. We demonstrate that on average, the superensemble approach produces more accurate forecasts than those made from any of the individual forecasting systems.

show abstract

Dengue outlook for the World Cup in Brazil: an early warning model framework driven by real-time seasonal climate forecasts

Cited by 121 publications

References 33 publications

Difusão espaço-tempo do dengue no Município do Rio de Janeiro, Brasil, no período de 2000-2013

Difusão espaço-tempo do dengue no Município do Rio de Janeiro, Brasil, no período de 2000-2013

Quantifying the added value of climate information in a spatio-temporal dengue model

Superensemble forecasts of dengue outbreaks

Contact Info

Product

Resources

About