Forecasting dengue fever in Brazil: An assessment of climate conditions
| Autor: | Lucas M. Stolerman, J. Nathan Kutz, Pedro D. Maia |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Atmospheric Science
Epidemiology Rain Climate Dengue virus Disease Vectors medicine.disease_cause Mosquitoes Geographical locations Dengue fever Zika virus Dengue Machine Learning 0302 clinical medicine Mathematical and Statistical Techniques Aedes Medicine and Health Sciences 030212 general & internal medicine Chikungunya Multidisciplinary Warning system biology Zika Virus Infection Applied Mathematics Simulation and Modeling Environmental resource management Yellow fever Statistics Temperature Eukaryota Annual cycle Insects Infectious Diseases Physical Sciences Medicine Epidemiological Methods and Statistics Seasons Brazil Algorithms Research Article Arthropoda Science 030231 tropical medicine Aedes aegypti Mosquito Vectors Aedes Aegypti Environment Research and Analysis Methods 03 medical and health sciences Meteorology Yellow Fever medicine Animals Humans Statistical Methods Cities business.industry Winter Organisms Biology and Life Sciences Zika Virus South America Dengue Virus medicine.disease biology.organism_classification Invertebrates Insect Vectors Species Interactions Earth Sciences Chikungunya Fever People and places business Mathematics Forecasting |
| Zdroj: | PLoS ONE PLoS ONE, Vol 14, Iss 8, p e0220106 (2019) |
| ISSN: | 1932-6203 |
| Popis: | Local climate conditions play a major role in the biology of the Aedes aegypti mosquito, the main vector responsible for transmitting dengue, zika, chikungunya and yellow fever in urban centers. For this reason, a detailed assessment of periods in which changes in climate conditions affect the number of human cases may improve the timing of vector-control efforts. In this work, we develop new machine-learning algorithms to analyze climate time series and their connection to the occurrence of dengue epidemic years for seven Brazilian state capitals. Our method explores the impact of two key variables-frequency of precipitation and average temperature-during a wide range of time windows in the annual cycle. Our results indicate that each Brazilian state capital considered has its own climate signatures that correlate with the overall number of human dengue-cases. However, for most of the studied cities, the winter preceding an epidemic year shows a strong predictive power. Understanding such climate contributions to the vector's biology could lead to more accurate prediction models and early warning systems. |
| Databáze: | OpenAIRE |
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