Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pntd.0009392
Title: Using heterogeneous data to identify signatures of dengue outbreaks at fine spatiotemporal scales across Brazil
Authors: Castro, LA
Generous, N
Luo, W 
Pastore y Piontti, A
Martinez, K
Gomes, MFC
Osthus, D
Fairchild, G
Ziemann, A
Vespignani, A
Santillana, M
Manore, CA
Del Valle, SY
Keywords: Brazil
Dengue
Disease Outbreaks
Forecasting
Humans
Models, Statistical
Seasons
Weather
Issue Date: 1-May-2021
Publisher: Public Library of Science (PLoS)
Citation: Castro, LA, Generous, N, Luo, W, Pastore y Piontti, A, Martinez, K, Gomes, MFC, Osthus, D, Fairchild, G, Ziemann, A, Vespignani, A, Santillana, M, Manore, CA, Del Valle, SY (2021-05-01). Using heterogeneous data to identify signatures of dengue outbreaks at fine spatiotemporal scales across Brazil. PLoS Neglected Tropical Diseases 15 (5) : e0009392-. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pntd.0009392
Abstract: Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- and interseasonal dynamics. Here, we present a framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010-2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset. In addition, we use a combination of 18 satellite remote sensing imagery, weather, clinical, mobility, and census data streams and regression methods to identify a parsimonious set of covariates that explain each time series property. The models explained 54% of the variation in outbreak shape, 38% of seasonal onset, 33% of pairwise correlation in outbreak timing, and 10% of pairwise correlation in outbreak magnitude. Regions that have experienced longer periods of drought sensitivity, as captured by the “normalized burn ratio,” experienced less intense outbreaks, while regions with regular fluctuations in relative humidity had less regular seasonal outbreaks. Both the pairwise correlations in outbreak timing and outbreak trend between mesoresgions were best predicted by distance. Our analysis also revealed the presence of distinct geographic clusters where dengue properties tend to be spatially correlated. Forecasting models aimed at predicting the dynamics of dengue activity need to identify the most salient variables capable of contributing to accurate predictions. Our findings show that successful models may need to leverage distinct variables in different locations and be catered to a specific task, such as predicting outbreak magnitude or timing characteristics, to be useful. This advocates in favor of “adaptive models” rather than “one-size-fitsall” models. The results of this study can be applied to improving spatial hierarchical or target- focused forecasting models of dengue activity across Brazil.
Source Title: PLoS Neglected Tropical Diseases
URI: https://scholarbank.nus.edu.sg/handle/10635/229361
ISSN: 19352727
19352735
DOI: 10.1371/journal.pntd.0009392
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