A Retrospective Study of Climate Change Affecting Dengue
Evidences, Challenges and Future Directions
Bibliographic Data
| ID | 22068022 |
|---|---|
| Authors | Surbhi Bhatia (0000-0003-3097-6568, King Faisal University, corresponding author), Dhruvisha Bansal (Symbiosis International University), Seema Patil (0000-0003-1733-9476), S H Patil (0000-0001-7140-9522, Symbiosis International University), Sharnil Pandya (0000-0002-4507-1844, Symbiosis International University), Qazi Mudassar Ilyas (0000-0003-4238-8093, King Faisal University), Sajida Imran (0000-0001-7648-8442, King Faisal University) |
| Year | 2022 |
| Volume | 10 |
| Pages | 884645-884645 |
| Publication date | 2022-05-27 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2022.884645 |
| PMID | 35712272 |
| OpenAlex | W4281712262 |
| Language | EN |
| Citations received | 5 |
| References cited | 56 |
Climate change is unexpected weather patterns that can create an alarming situation. Due to climate change, various sectors are affected, and one of the sectors is healthcare. As a result of climate change, the geographic range of several vector-borne human infectious diseases will expand. Currently, dengue is taking its toll, and climate change is one of the key reasons contributing to the intensification of dengue disease transmission. The most important climatic factors linked to dengue transmission are temperature, rainfall, and relative humidity. The present study carries out a systematic literature review on the surveillance system to predict dengue outbreaks based on Machine Learning modeling techniques. The systematic literature review discusses the methodology and objectives, the number of studies carried out in different regions and periods, the association between climatic factors and the increase in positive dengue cases. This study also includes a detailed investigation of meteorological data, the dengue positive patient data, and the pre-processing techniques used for data cleaning. Furthermore, correlation techniques in several studies to determine the relationship between dengue incidence and meteorological parameters and machine learning models for predictive analysis are discussed. In the future direction for creating a dengue surveillance system, several research challenges and limitations of current work are discussed
Climate change · Dengue fever · Environmental health · Environmental resource management · Geography · Outbreak · Telecommunications · Computer Science · COVID-19 epidemiological studies · Dengue and Mosquito Control Research · Environmental Science · Medicine · Mosquito-borne diseases and control · Ecology · Virology
Impact of urban heat island effect on dengue incidence
Exploring community willingness and barriers to digital solutions and training for dengue prevention
Correlation of Dengue and Meteorological Factors in Bangladesh
Youth’s climate consciousness
Predicting subsequent childbirth plans of Korean mothers using machine learning
Artificial Intelligence and Management
The global distribution and burden of dengue
Random Forests
Covid-19 Patient Health Prediction Using Boosted Random Forest Algorithm
Artificial intelligence in healthcare
Artificial intelligence in medicine and the disclosure of risks
Regional variability in relationships between climate and dengue/DHF in Indonesia
Treating loss-to-follow-up as a missing data problem
Artificial intelligence, systemic risks, and sustainability
| Unique citing works | 5 |
|---|---|
| Citations per year | 1,67 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 5 |