globalchange  > 气候减缓与适应
DOI: 10.4103/1995-7645.250838
WOS记录号: WOS:000457712400002
论文题名:
Modeling and predicting dengue fever cases in key regions of the Philippines using remote sensing data
作者: Pineda-cortel, Maria Ruth B.1,2,3; Clemente, Benjie M.1; Pham Thi Thanh Nga4,5
通讯作者: Pineda-cortel, Maria Ruth B.
刊名: ASIAN PACIFIC JOURNAL OF TROPICAL MEDICINE
ISSN: 1995-7645
EISSN: 2352-4146
出版年: 2019
卷: 12, 期:2, 页码:60-66
语种: 英语
英文关键词: Dengue fever ; Climate change ; Remote sensing data ; Autoregressive Integrated Moving ; Average models
WOS关键词: CLIMATE-CHANGE ; METEOROLOGICAL FACTORS ; HUMAN HEALTH ; EL-NINO ; TRANSMISSION ; TEMPERATURE ; GUANGZHOU ; VARIABILITY ; DYNAMICS ; DISEASES
WOS学科分类: Public, Environmental & Occupational Health ; Tropical Medicine
WOS研究方向: Public, Environmental & Occupational Health ; Tropical Medicine
英文摘要:

Objective: To correlate climatic and environmental factors such as land surface temperature, rainfall, humidity and normalized difference vegetation index with the incidence of dengue to develop prediction models for the Philippines using remote-sensing data. Methods: limeseries analysis was performed using dengue cases in four regions of the Philippines and monthly climatic variables extracted from Global Satellite Mapping of Precipitation for rainfall, and MODIS for the land surface temperature and normalized difference vegetation index from 2008-2015. Consistent dataset during the period of study was utilized in Autoregressive Integrated Moving Average models to predict dengue incidence in the four regions being studied. Results: The best-fitting models were selected to characterize the relationship between dengue incidence and climate variables. The predicted cases of dengue for January to December 2015 period fitted well with the actual dengue cases of the same timeframe. It also showed significantly good linear regression with a square of correlation of 0.869 5 for the four regions combined. Conclusion: Climatic and environmental variables are positively associated with dengue incidence and suit best as predictor factors using Autoregressive Integrated Moving Average models. This finding could be a meaningful tool in developing an early warning model based on weather forecasts to deliver effective public health prevention and mitigation programs.


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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/128378
Appears in Collections:气候减缓与适应

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作者单位: 1.Univ Santo Tomas, Fac Pharm, Dept Med Technol, Espana Blvd, Manila, Philippines
2.Univ Santo Tomas, Res Ctr Nat & Appl Sci, Manila, Philippines
3.Univ Santo Tomas, Grad Sch, Manila, Philippines
4.Vietnam Natl Space Ctr, Hanoi, Vietnam
5.Vietnam Acad Sci & Technol, Hanoi, Vietnam

Recommended Citation:
Pineda-cortel, Maria Ruth B.,Clemente, Benjie M.,Pham Thi Thanh Nga. Modeling and predicting dengue fever cases in key regions of the Philippines using remote sensing data[J]. ASIAN PACIFIC JOURNAL OF TROPICAL MEDICINE,2019-01-01,12(2):60-66
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