Rachel Lowe1, Anna M Stewart-Ibarra2, Desislava Petrova3, Markel García-Díez4, Mercy J Borbor-Cordova5, Raúl Mejía6, Mary Regato7, Xavier Rodó8. 1. Centre for the Mathematical Modelling of Infectious Diseases and Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; CLIMA-Climate and Health Programme, Barcelona Institute for Global Health (ISGLOBAL), Barcelona, Spain. Electronic address: rachel.lowe@lshtm.ac.uk. 2. Center for Global Health and Translational Science and Department of Medicine, SUNY Upstate Medical University, Syracuse, NY, USA. 3. CLIMA-Climate and Health Programme, Barcelona Institute for Global Health (ISGLOBAL), Barcelona, Spain. 4. Predictia Intelligent Data Solutions, Santander, Spain. 5. School of Maritime Engineering, Biological Sciences, Oceanic and Natural Resources, Escuela Superior Politecnica del Litoral (ESPOL), Guayaquil, Ecuador. 6. National Institute of Meteorology and Hydrology (INAMHI), Guayaquil, Ecuador. 7. National Institute of Public Health Research (INSPI) of the Ministry of Health, Guayaquil, Ecuador. 8. CLIMA-Climate and Health Programme, Barcelona Institute for Global Health (ISGLOBAL), Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
Abstract
BACKGROUND: El Niño and its effect on local meteorological conditions potentially influences interannual variability in dengue transmission in southern coastal Ecuador. El Oro province is a key dengue surveillance site, due to the high burden of dengue, seasonal transmission, co-circulation of all four dengue serotypes, and the recent introduction of chikungunya and Zika. In this study, we used climate forecasts to predict the evolution of the 2016 dengue season in the city of Machala, following one of the strongest El Niño events on record. METHODS: We incorporated precipitation, minimum temperature, and Niño3·4 index forecasts in a Bayesian hierarchical mixed model to predict dengue incidence. The model was initiated on Jan 1, 2016, producing monthly dengue forecasts until November, 2016. We accounted for misreporting of dengue due to the introduction of chikungunya in 2015, by using active surveillance data to correct reported dengue case data from passive surveillance records. We then evaluated the forecast retrospectively with available epidemiological information. FINDINGS: The predictions correctly forecast an early peak in dengue incidence in March, 2016, with a 90% chance of exceeding the mean dengue incidence for the previous 5 years. Accounting for the proportion of chikungunya cases that had been incorrectly recorded as dengue in 2015 improved the prediction of the magnitude of dengue incidence in 2016. INTERPRETATION: This dengue prediction framework, which uses seasonal climate and El Niño forecasts, allows a prediction to be made at the start of the year for the entire dengue season. Combining active surveillance data with routine dengue reports improved not only model fit and performance, but also the accuracy of benchmark estimates based on historical seasonal averages. This study advances the state-of-the-art of climate services for the health sector, by showing the potential value of incorporating climate information in the public health decision-making process in Ecuador. FUNDING: European Union FP7, Royal Society, and National Science Foundation.
BACKGROUND: El Niño and its effect on local meteorological conditions potentially influences interannual variability in dengue transmission in southern coastal Ecuador. El Oro province is a key dengue surveillance site, due to the high burden of dengue, seasonal transmission, co-circulation of all four dengue serotypes, and the recent introduction of chikungunya and Zika. In this study, we used climate forecasts to predict the evolution of the 2016 dengue season in the city of Machala, following one of the strongest El Niño events on record. METHODS: We incorporated precipitation, minimum temperature, and Niño3·4 index forecasts in a Bayesian hierarchical mixed model to predict dengue incidence. The model was initiated on Jan 1, 2016, producing monthly dengue forecasts until November, 2016. We accounted for misreporting of dengue due to the introduction of chikungunya in 2015, by using active surveillance data to correct reported dengue case data from passive surveillance records. We then evaluated the forecast retrospectively with available epidemiological information. FINDINGS: The predictions correctly forecast an early peak in dengue incidence in March, 2016, with a 90% chance of exceeding the mean dengue incidence for the previous 5 years. Accounting for the proportion of chikungunya cases that had been incorrectly recorded as dengue in 2015 improved the prediction of the magnitude of dengue incidence in 2016. INTERPRETATION: This dengue prediction framework, which uses seasonal climate and El Niño forecasts, allows a prediction to be made at the start of the year for the entire dengue season. Combining active surveillance data with routine dengue reports improved not only model fit and performance, but also the accuracy of benchmark estimates based on historical seasonal averages. This study advances the state-of-the-art of climate services for the health sector, by showing the potential value of incorporating climate information in the public health decision-making process in Ecuador. FUNDING: European Union FP7, Royal Society, and National Science Foundation.
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