Wavelet Analysis of Dengue Incidence and its Correlation with Weather and Vegetation Variables in Costa Rica Article Swipe
Yury E. García
,
Luis A. Barboza
,
Fabio Sánchez
,
Paola Vásquez
,
Juan G. Calvo
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2107.05740
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2107.05740
Dengue represents a serious public health problem in tropical and subtropical regions worldwide. The number of dengue cases and its geographical expansion has increased in recent decades, driven mostly after by social and environmental factors. In Costa Rica, it has been endemic since it was first introduced in 1993. In this article, wavelet analyzes (wavelet power spectrum and wavelet coherence) were performed to detect and quantify dengue periodicity and describe patterns of synchrony between dengue incidence and climatic and environmental factors: Normalized Difference Water Index, Enhanced Vegetation Index, Normalized Difference Vegetation Index, Tropical North Atlantic indices, Land Surface Temperature, and El Niño Southern Oscillation indices in 32 different cantons, using dengue surveillance from 2000 to 2019. Results showed that the dengue dominant cycles are in periods of 1, 2, and 3 years. The wavelet coherence analysis showed that the vegetation indices are correlated with dengue incidence in places located in the central and Northern Pacific of the country in the period of 1 year. Climatic variables such as El Niño 3, 3.4, 4, showed a strong correlation with dengue incidence in the period of 3 years and the Tropical North Atlantic is correlated with dengue incidence in the period of 1 year. Land Surface Temperature showed a strong correlation with dengue time series in the 32 cantons.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2107.05740
- https://arxiv.org/pdf/2107.05740
- OA Status
- green
- Cited By
- 1
- References
- 38
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W3181960517Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2107.05740Digital Object Identifier
- Title
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Wavelet Analysis of Dengue Incidence and its Correlation with Weather and Vegetation Variables in Costa RicaWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
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2021Year of publication
- Publication date
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2021-07-02Full publication date if available
- Authors
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Yury E. García, Luis A. Barboza, Fabio Sánchez, Paola Vásquez, Juan G. CalvoList of authors in order
- Landing page
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https://arxiv.org/abs/2107.05740Publisher landing page
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https://arxiv.org/pdf/2107.05740Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2107.05740Direct OA link when available
- Concepts
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Dengue fever, Vegetation (pathology), Wavelet, Geography, Climatology, Incidence (geometry), Environmental science, Mathematics, Geology, Computer science, Virology, Biology, Medicine, Pathology, Artificial intelligence, GeometryTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.using | 109 |
| abstract_inverted_index.year. | 163, 201 |
| abstract_inverted_index.years | 185 |
| abstract_inverted_index.Dengue | 0 |
| abstract_inverted_index.Index, | 84, 87, 91 |
| abstract_inverted_index.cycles | 122 |
| abstract_inverted_index.dengue | 16, 66, 74, 110, 120, 144, 178, 194, 210 |
| abstract_inverted_index.detect | 63 |
| abstract_inverted_index.driven | 27 |
| abstract_inverted_index.health | 5 |
| abstract_inverted_index.mostly | 28 |
| abstract_inverted_index.number | 14 |
| abstract_inverted_index.period | 160, 182, 198 |
| abstract_inverted_index.places | 147 |
| abstract_inverted_index.public | 4 |
| abstract_inverted_index.recent | 25 |
| abstract_inverted_index.series | 212 |
| abstract_inverted_index.showed | 117, 136, 173, 205 |
| abstract_inverted_index.social | 31 |
| abstract_inverted_index.strong | 175, 207 |
| abstract_inverted_index.years. | 131 |
| abstract_inverted_index.Pacific | 154 |
| abstract_inverted_index.Results | 116 |
| abstract_inverted_index.Surface | 97, 203 |
| abstract_inverted_index.between | 73 |
| abstract_inverted_index.central | 151 |
| abstract_inverted_index.country | 157 |
| abstract_inverted_index.endemic | 41 |
| abstract_inverted_index.indices | 104, 140 |
| abstract_inverted_index.located | 148 |
| abstract_inverted_index.periods | 125 |
| abstract_inverted_index.problem | 6 |
| abstract_inverted_index.regions | 11 |
| abstract_inverted_index.serious | 3 |
| abstract_inverted_index.wavelet | 52, 58, 133 |
| abstract_inverted_index.(wavelet | 54 |
| abstract_inverted_index.Atlantic | 94, 190 |
| abstract_inverted_index.Climatic | 164 |
| abstract_inverted_index.Enhanced | 85 |
| abstract_inverted_index.Northern | 153 |
| abstract_inverted_index.Southern | 102 |
| abstract_inverted_index.Tropical | 92, 188 |
| abstract_inverted_index.analysis | 135 |
| abstract_inverted_index.analyzes | 53 |
| abstract_inverted_index.article, | 51 |
| abstract_inverted_index.cantons, | 108 |
| abstract_inverted_index.cantons. | 216 |
| abstract_inverted_index.climatic | 77 |
| abstract_inverted_index.decades, | 26 |
| abstract_inverted_index.describe | 69 |
| abstract_inverted_index.dominant | 121 |
| abstract_inverted_index.factors. | 34 |
| abstract_inverted_index.factors: | 80 |
| abstract_inverted_index.indices, | 95 |
| abstract_inverted_index.patterns | 70 |
| abstract_inverted_index.quantify | 65 |
| abstract_inverted_index.spectrum | 56 |
| abstract_inverted_index.tropical | 8 |
| abstract_inverted_index.coherence | 134 |
| abstract_inverted_index.different | 107 |
| abstract_inverted_index.expansion | 21 |
| abstract_inverted_index.incidence | 75, 145, 179, 195 |
| abstract_inverted_index.increased | 23 |
| abstract_inverted_index.performed | 61 |
| abstract_inverted_index.synchrony | 72 |
| abstract_inverted_index.variables | 165 |
| abstract_inverted_index.Difference | 82, 89 |
| abstract_inverted_index.Normalized | 81, 88 |
| abstract_inverted_index.Vegetation | 86, 90 |
| abstract_inverted_index.coherence) | 59 |
| abstract_inverted_index.correlated | 142, 192 |
| abstract_inverted_index.introduced | 46 |
| abstract_inverted_index.represents | 1 |
| abstract_inverted_index.vegetation | 139 |
| abstract_inverted_index.worldwide. | 12 |
| abstract_inverted_index.Oscillation | 103 |
| abstract_inverted_index.Temperature | 204 |
| abstract_inverted_index.correlation | 176, 208 |
| abstract_inverted_index.periodicity | 67 |
| abstract_inverted_index.subtropical | 10 |
| abstract_inverted_index.Temperature, | 98 |
| abstract_inverted_index.geographical | 20 |
| abstract_inverted_index.surveillance | 111 |
| abstract_inverted_index.environmental | 33, 79 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/14 |
| sustainable_development_goals[0].score | 0.5099999904632568 |
| sustainable_development_goals[0].display_name | Life below water |
| citation_normalized_percentile |