Dengue Outbreak Prediction Based on Artificial Neural Networking Model Using Climatic Parameters Article Swipe
Biplab Ghosh
,
Monika Soni
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-86587/v1
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-86587/v1
Background: Dengue fever is a vector-borne tropical disease radically amplified by 30 times in occurrence between 1960 and 2010. The upsurge is considered to be because of urbanization, population growth and climate change. Therefore, Meteorological parameters (temperature, precipitation and relative humidity) have impact on the occurrence and outbreaks of dengue fever. There are not many studies that enumerate the relationship between the dengue cases in a particular locality and the meteorological parameters. This study explores the relationship between the dengue cases and the meteorological parameters. In prevalent localities, it is essential to alleviate the outbreaks using modelling techniques for better disease control. Methods: An artificial neural network (ANN) model was developed for predicting the number of dengue cases by knowing the meteorological parameters. The model was trained with 7 years of dengue fever data of Kamrup and Lakhimpur district of Assam, India. The practicality of the model was corroborated using independent data set with satisfactory outcomes. Findings: It was apparent from the sensitivity analysis that precipitation is more sensitive to the number of dengue cases than other meteorological parameters. Conclusion : This model would assist dengue fever alleviation and control in the long run.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-86587/v1
- https://www.researchsquare.com/article/rs-86587/v1.pdf
- OA Status
- green
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4256119560
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https://openalex.org/W4256119560Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-86587/v1Digital Object Identifier
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Dengue Outbreak Prediction Based on Artificial Neural Networking Model Using Climatic ParametersWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-10-08Full publication date if available
- Authors
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Biplab Ghosh, Monika SoniList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-86587/v1Publisher landing page
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https://www.researchsquare.com/article/rs-86587/v1.pdfDirect link to full text PDF
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YesWhether a free full text is available
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-
greenOpen access status per OpenAlex
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https://www.researchsquare.com/article/rs-86587/v1.pdfDirect OA link when available
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Dengue fever, Outbreak, Geography, Urbanization, Environmental science, Precipitation, Meteorology, Ecology, Virology, BiologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.occurrence | 15, 46 |
| abstract_inverted_index.parameters | 36 |
| abstract_inverted_index.particular | 67 |
| abstract_inverted_index.population | 29 |
| abstract_inverted_index.predicting | 113 |
| abstract_inverted_index.techniques | 98 |
| abstract_inverted_index.Background: | 1 |
| abstract_inverted_index.alleviation | 188 |
| abstract_inverted_index.independent | 151 |
| abstract_inverted_index.localities, | 88 |
| abstract_inverted_index.parameters. | 72, 85, 123, 179 |
| abstract_inverted_index.sensitivity | 163 |
| abstract_inverted_index.corroborated | 149 |
| abstract_inverted_index.practicality | 144 |
| abstract_inverted_index.relationship | 60, 77 |
| abstract_inverted_index.satisfactory | 155 |
| abstract_inverted_index.vector-borne | 6 |
| abstract_inverted_index.(temperature, | 37 |
| abstract_inverted_index.precipitation | 38, 166 |
| abstract_inverted_index.urbanization, | 28 |
| abstract_inverted_index.Meteorological | 35 |
| abstract_inverted_index.meteorological | 71, 84, 122, 178 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.3923403 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |