Rachel Lowe1, Christovam Barcellos2, Caio A S Coelho3, Trevor C Bailey4, Giovanini Evelim Coelho5, Richard Graham6, Tim Jupp4, Walter Massa Ramalho7, Marilia Sá Carvalho2, David B Stephenson4, Xavier Rodó8. 1. Climate Dynamics and Impacts Unit, Institut Català de Ciències del Clima (IC3), Barcelona, Spain. Electronic address: rachel.lowe@ic3.cat. 2. Fundação Oswaldo Cruz (FIOCRUZ), Rio de Janeiro, RJ, Brazil. 3. Centro de Previsão de Tempo e Estudos Climáticos, Instituto Nacional de Pesquisas Espaciais, Cachoeira Paulista, SP, Brazil. 4. Exeter Climate Systems, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK. 5. Coordenação Geral do Programa Nacional de Controle da Dengue, Ministério da Saúde, Brasília, DF, Brazil. 6. Met Office Hadley Centre, Exeter, Devon, UK. 7. Faculdade de Ceilândia, Universidade de Brasília, Brasília, DF, Brazil. 8. Climate Dynamics and Impacts Unit, Institut Català de Ciències del Clima (IC3), Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain.
Abstract
BACKGROUND: With more than a million spectators expected to travel among 12 different cities in Brazil during the football World Cup, June 12-July 13, 2014, the risk of the mosquito-transmitted disease dengue fever is a concern. We addressed the potential for a dengue epidemic during the tournament, using a probabilistic forecast of dengue risk for the 553 microregions of Brazil, with risk level warnings for the 12 cities where matches will be played. METHODS: We obtained real-time seasonal climate forecasts from several international sources (European Centre for Medium-Range Weather Forecasts [ECMWF], Met Office, Meteo-France and Centro de Previsão de Tempo e Estudos Climáticos [CPTEC]) and the observed dengue epidemiological situation in Brazil at the forecast issue date as provided by the Ministry of Health. Using this information we devised a spatiotemporal hierarchical Bayesian modelling framework that enabled dengue warnings to be made 3 months ahead. By assessing the past performance of the forecasting system using observed dengue incidence rates for June, 2000-2013, we identified optimum trigger alert thresholds for scenarios of medium-risk and high-risk of dengue. FINDINGS: Our forecasts for June, 2014, showed that dengue risk was likely to be low in the host cities Brasília, Cuiabá, Curitiba, Porto Alegre, and São Paulo. The risk was medium in Rio de Janeiro, Belo Horizonte, Salvador, and Manaus. High-risk alerts were triggered for the northeastern cities of Recife (p(high)=19%), Fortaleza (p(high)=46%), and Natal (p(high)=48%). For these high-risk areas, particularly Natal, the forecasting system did well for previous years (in June, 2000-13). INTERPRETATION: This timely dengue early warning permits the Ministry of Health and local authorities to implement appropriate, city-specific mitigation and control actions ahead of the World Cup. FUNDING: European Commission's Seventh Framework Research Programme projects DENFREE, EUPORIAS, and SPECS; Conselho Nacional de Desenvolvimento Científico e Tecnológico and Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro.
BACKGROUND: With more than a million spectators expected to travel among 12 different cities in Brazil during the football World Cup, June 12-July 13, 2014, the risk of the mosquito-transmitted disease dengue fever is a concern. We addressed the potential for a dengue epidemic during the tournament, using a probabilistic forecast of dengue risk for the 553 microregions of Brazil, with risk level warnings for the 12 cities where matches will be played. METHODS: We obtained real-time seasonal climate forecasts from several international sources (European Centre for Medium-Range Weather Forecasts [ECMWF], Met Office, Meteo-France and Centro de Previsão de Tempo e Estudos Climáticos [CPTEC]) and the observed dengue epidemiological situation in Brazil at the forecast issue date as provided by the Ministry of Health. Using this information we devised a spatiotemporal hierarchical Bayesian modelling framework that enabled dengue warnings to be made 3 months ahead. By assessing the past performance of the forecasting system using observed dengue incidence rates for June, 2000-2013, we identified optimum trigger alert thresholds for scenarios of medium-risk and high-risk of dengue. FINDINGS: Our forecasts for June, 2014, showed that dengue risk was likely to be low in the host cities Brasília, Cuiabá, Curitiba, Porto Alegre, and São Paulo. The risk was medium in Rio de Janeiro, Belo Horizonte, Salvador, and Manaus. High-risk alerts were triggered for the northeastern cities of Recife (p(high)=19%), Fortaleza (p(high)=46%), and Natal (p(high)=48%). For these high-risk areas, particularly Natal, the forecasting system did well for previous years (in June, 2000-13). INTERPRETATION: This timely dengue early warning permits the Ministry of Health and local authorities to implement appropriate, city-specific mitigation and control actions ahead of the World Cup. FUNDING: European Commission's Seventh Framework Research Programme projects DENFREE, EUPORIAS, and SPECS; Conselho Nacional de Desenvolvimento Científico e Tecnológico and Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro.
Authors: Evan L Ray; Krzysztof Sakrejda; Stephen A Lauer; Michael A Johansson; Nicholas G Reich Journal: Stat Med Date: 2017-09-14 Impact factor: 2.373
Authors: Willem G van Panhuis; Sangwon Hyun; Kayleigh Blaney; Ernesto T A Marques; Giovanini E Coelho; João Bosco Siqueira; Ryan Tibshirani; Jarbas B da Silva; Roni Rosenfeld Journal: PLoS Negl Trop Dis Date: 2014-07-31
Authors: Mateus Chediak; Fabiano G Pimenta; Giovanini E Coelho; Ima A Braga; José Bento P Lima; Karina Ribeiro Lj Cavalcante; Lindemberg C de Sousa; Maria Alice V de Melo-Santos; Maria de Lourdes da G Macoris; Ana Paula de Araújo; Constância Flávia J Ayres; Maria Teresa M Andrighetti; Ricristhi Gonçalves de A Gomes; Kauara B Campos; Raul Narciso C Guedes Journal: Mem Inst Oswaldo Cruz Date: 2016-04-29 Impact factor: 2.743