In:
PLOS Neglected Tropical Diseases, Public Library of Science (PLoS), Vol. 15, No. 5 ( 2021-5-21), p. e0009392-
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, 34% of pairwise correlation in outbreak timing, and 11% 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-fits-all” models. The results of this study can be applied to improving spatial hierarchical or target-focused forecasting models of dengue activity across Brazil.
Type of Medium:
Online Resource
ISSN:
1935-2735
DOI:
10.1371/journal.pntd.0009392
DOI:
10.1371/journal.pntd.0009392.g001
DOI:
10.1371/journal.pntd.0009392.g002
DOI:
10.1371/journal.pntd.0009392.g003
DOI:
10.1371/journal.pntd.0009392.g004
DOI:
10.1371/journal.pntd.0009392.g005
DOI:
10.1371/journal.pntd.0009392.t001
DOI:
10.1371/journal.pntd.0009392.t002
DOI:
10.1371/journal.pntd.0009392.t003
DOI:
10.1371/journal.pntd.0009392.s001
DOI:
10.1371/journal.pntd.0009392.s002
DOI:
10.1371/journal.pntd.0009392.s003
DOI:
10.1371/journal.pntd.0009392.s004
DOI:
10.1371/journal.pntd.0009392.s005
DOI:
10.1371/journal.pntd.0009392.s006
DOI:
10.1371/journal.pntd.0009392.s007
DOI:
10.1371/journal.pntd.0009392.s008
DOI:
10.1371/journal.pntd.0009392.s009
DOI:
10.1371/journal.pntd.0009392.s010
DOI:
10.1371/journal.pntd.0009392.s011
DOI:
10.1371/journal.pntd.0009392.s012
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2429704-5
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