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      Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil

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          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.

          Author summary

          Dengue virus spreads through mosquitoes in many tropical and subtropical parts of the world, including Brazil. Each year, dengue virus causes seasonal outbreaks that vary in magnitude and timing across the country. This variation makes tailoring preparation efforts for fine spatio-temporal scales challenging. In this study, we described four properties of historical dengue time series at the mesoregion level, the Brazilian subdivision below state, and examined how they varied across the country. We found that the duration and timing of seasonal outbreaks are largely driven by climate factors, while relational properties, i.e., the similarity in outbreak timing and magnitude between two mesoregions, are explained by a mix of mobility patterns and climate similarities. Surprisingly, we found that remote sensing derived products and movement inferred through Twitter were adequate proxies for climate and mobility patterns respectively. Knowledge of how dengue outbreaks differ across the country and the factors that may influence specific outbreak properties may be important for improving efforts to build forecasting and prediction models.

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          Most cited references 55

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          Regularization Paths for Generalized Linear Models via Coordinate Descent

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            The global distribution and burden of dengue

            Dengue is a systemic viral infection transmitted between humans by Aedes mosquitoes 1 . For some patients dengue is a life-threatening illness 2 . There are currently no licensed vaccines or specific therapeutics, and substantial vector control efforts have not stopped its rapid emergence and global spread 3 . The contemporary worldwide distribution of the risk of dengue virus infection 4 and its public health burden are poorly known 2,5 . Here we undertake an exhaustive assembly of known records of dengue occurrence worldwide, and use a formal modelling framework to map the global distribution of dengue risk. We then pair the resulting risk map with detailed longitudinal information from dengue cohort studies and population surfaces to infer the public health burden of dengue in 2010. We predict dengue to be ubiquitous throughout the tropics, with local spatial variations in risk influenced strongly by rainfall, temperature and the degree of urbanisation. Using cartographic approaches, we estimate there to be 390 million (95 percent credible interval 284-528) dengue infections per year, of which 96 million (67-136) manifest apparently (any level of clinical or sub-clinical severity). This infection total is more than three times the dengue burden estimate of the World Health Organization 2 . Stratification of our estimates by country allows comparison with national dengue reporting, after taking into account the probability of an apparent infection being formally reported. The most notable differences are discussed. These new risk maps and infection estimates provide novel insights into the global, regional and national public health burden imposed by dengue. We anticipate that they will provide a starting point for a wider discussion about the global impact of this disease and will help guide improvements in disease control strategies using vaccine, drug and vector control methods and in their economic evaluation. [285]
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              Silhouettes: A graphical aid to the interpretation and validation of cluster analysis

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: ResourcesRole: Writing – original draft
                Role: Data curationRole: ResourcesRole: Writing – review & editing
                Role: Data curationRole: ResourcesRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Negl Trop Dis
                PLoS Negl Trop Dis
                plos
                PLoS Neglected Tropical Diseases
                Public Library of Science (San Francisco, CA USA )
                1935-2727
                1935-2735
                May 2021
                21 May 2021
                : 15
                : 5
                Affiliations
                [1 ] Information Systems and Modeling Group, Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
                [2 ] Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
                [3 ] National Security and Defense Program Office, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
                [4 ] Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, United States of America
                [5 ] Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America
                [6 ] Geography Department, National University of Singapore, Singapore, Singapore
                [7 ] Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
                [8 ] Department of Mathematics & Statistics, Colorado School of Mines, Golden, Colorado, United States of America
                [9 ] Núcleo de Métodos Analíticos em Vigilância Epidemiológica Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, Brazil
                [10 ] Statistical Sciences Group, Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
                [11 ] Space Data Science and Systems Group, Intelligence and Space Research Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
                [12 ] School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, United States of America
                University of Hong Kong, HONG KONG
                Author notes

                I have read the journal’s policy and the authors of this manuscript have the following competing interests: A.P.y.P and A.V. report grants from Metabiota Inc.

                Article
                PNTD-D-20-02058
                10.1371/journal.pntd.0009392
                8174735
                34019536
                © 2021 Castro et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 5, Tables: 3, Pages: 24
                Product
                Funding
                Funded by: NIH/NIGMS
                Award ID: R01GM130668-02
                Award Recipient :
                Funded by: NIH/NIGMS
                Award ID: R01GM130668-02
                Award Recipient :
                Funded by: NIH/NIGMS
                Award ID: R01GM130668-02
                Award Recipient :
                Funded by: Johnson and Johnson Corporate Citizenship Trust and the Johnson and Johnson Global Public Health
                Award Recipient :
                Funded by: Johnson and Johnson Corporate Citizenship Trust and the Johnson and Johnson Global Public Health
                Award Recipient :
                Funded by: National University of Singapore
                Award ID: WBS R-109-000- 270-133
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100008982, National Science Foundation;
                Award ID: DMS-1551229
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100008902, Los Alamos National Laboratory;
                Award ID: 20190581ECR
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100008902, Los Alamos National Laboratory;
                Award ID: 20180740ER
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100008902, Los Alamos National Laboratory;
                Award ID: 20200033DR
                Award Recipient :
                Funded by: U.S. Department of Energy
                Award Recipient :
                This research was partially funded by NIH/NIGMS under grant R01GM130668-01 awarded to SYD, MS, and AV. MS and WL thank the Johnson and Johnson Corporate Citizenship Trust and the Johnson and Johnson Global Public Health for providing institutional research funds to partially support this work. WL is also partially supported by National University of Singapore Start-up Grant under WBS R-109-000-270-133. KM was supported by the National Science Foundation via an NSF Graduate Research Fellowship and under award DMS-1551229. This work was also supported by the Laboratory Directed Research and Development program of Los Alamos National Laboratory, projects 20190581ECR, 20180740ER, and 20200033DR awarded to CAM. LAC was supported by the U.S. Department of Energy through the LANL/LDRD Program and the Center for Nonlinear Studies for this work. Los Alamos National Laboratory is operated by Triad National Security, LLC under Contract No. 89233218CNA000001 with the U.S. Department of Energy. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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