Satellite-based AI for climate-driven dengue prediction in Bangladesh: A literature review
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| Natura: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866901196816515072 |
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| author | Any, Mashrufa Meghla Rion, Abid Hossain |
| author_facet | Any, Mashrufa Meghla Rion, Abid Hossain |
| contents | <p>This literature review explores the use of satellite-based artificial intelligence (AI) models for climate-driven dengue prediction in Bangladesh. Dengue outbreaks are significantly influenced by climatic factors such as temperature, rainfall, and humidity, which can be effectively monitored using satellite remote sensing data. The study synthesizes findings from existing research on integrating climate variables with AI and machine learning algorithms for early warning and outbreak forecasting. Emphasis is placed on the potential of satellite imagery, big data analytics, and predictive modeling to enhance public health surveillance, reduce the burden of dengue, and support decision-making for vector control. The review also highlights key challenges, including data availability, model generalization, and the need for interdisciplinary collaboration to improve prediction accuracy.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16789229 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Satellite-based AI for climate-driven dengue prediction in Bangladesh: A literature review Any, Mashrufa Meghla Rion, Abid Hossain Dengue prediction Artificial intelligence Artificial Intelligence Artificial Intelligence/economics Artificial Intelligence/standards Artificial Intelligence Satellite data Climate change Bangladesh Machine learning, Public Health Surveillance Public Health Surveillance/methods Public health surveillance <p>This literature review explores the use of satellite-based artificial intelligence (AI) models for climate-driven dengue prediction in Bangladesh. Dengue outbreaks are significantly influenced by climatic factors such as temperature, rainfall, and humidity, which can be effectively monitored using satellite remote sensing data. The study synthesizes findings from existing research on integrating climate variables with AI and machine learning algorithms for early warning and outbreak forecasting. Emphasis is placed on the potential of satellite imagery, big data analytics, and predictive modeling to enhance public health surveillance, reduce the burden of dengue, and support decision-making for vector control. The review also highlights key challenges, including data availability, model generalization, and the need for interdisciplinary collaboration to improve prediction accuracy.</p> |
| title | Satellite-based AI for climate-driven dengue prediction in Bangladesh: A literature review |
| topic | Dengue prediction Artificial intelligence Artificial Intelligence Artificial Intelligence/economics Artificial Intelligence/standards Artificial Intelligence Satellite data Climate change Bangladesh Machine learning, Public Health Surveillance Public Health Surveillance/methods Public health surveillance |
| url | https://doi.org/10.5281/zenodo.16789229 |