Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil.
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 pr...
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ftdoajarticles:oai:doaj.org/article:30e4aad942cf40d2b9405bc55ed7613d 2023-05-15T15:14:06+02:00 Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil. Lauren A Castro Nicholas Generous Wei Luo Ana Pastore Y Piontti Kaitlyn Martinez Marcelo F C Gomes Dave Osthus Geoffrey Fairchild Amanda Ziemann Alessandro Vespignani Mauricio Santillana Carrie A Manore Sara Y Del Valle 2021-05-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0009392 https://doaj.org/article/30e4aad942cf40d2b9405bc55ed7613d EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pntd.0009392 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0009392 https://doaj.org/article/30e4aad942cf40d2b9405bc55ed7613d PLoS Neglected Tropical Diseases, Vol 15, Iss 5, p e0009392 (2021) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2021 ftdoajarticles https://doi.org/10.1371/journal.pntd.0009392 2022-12-31T11:50:03Z 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. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 15 5 e0009392 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
spellingShingle |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Lauren A Castro Nicholas Generous Wei Luo Ana Pastore Y Piontti Kaitlyn Martinez Marcelo F C Gomes Dave Osthus Geoffrey Fairchild Amanda Ziemann Alessandro Vespignani Mauricio Santillana Carrie A Manore Sara Y Del Valle Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Lauren A Castro Nicholas Generous Wei Luo Ana Pastore Y Piontti Kaitlyn Martinez Marcelo F C Gomes Dave Osthus Geoffrey Fairchild Amanda Ziemann Alessandro Vespignani Mauricio Santillana Carrie A Manore Sara Y Del Valle |
author_facet |
Lauren A Castro Nicholas Generous Wei Luo Ana Pastore Y Piontti Kaitlyn Martinez Marcelo F C Gomes Dave Osthus Geoffrey Fairchild Amanda Ziemann Alessandro Vespignani Mauricio Santillana Carrie A Manore Sara Y Del Valle |
author_sort |
Lauren A Castro |
title |
Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil. |
title_short |
Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil. |
title_full |
Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil. |
title_fullStr |
Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil. |
title_full_unstemmed |
Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil. |
title_sort |
using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across brazil. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2021 |
url |
https://doi.org/10.1371/journal.pntd.0009392 https://doaj.org/article/30e4aad942cf40d2b9405bc55ed7613d |
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Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 15, Iss 5, p e0009392 (2021) |
op_relation |
https://doi.org/10.1371/journal.pntd.0009392 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0009392 https://doaj.org/article/30e4aad942cf40d2b9405bc55ed7613d |
op_doi |
https://doi.org/10.1371/journal.pntd.0009392 |
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PLOS Neglected Tropical Diseases |
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15 |
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e0009392 |
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