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Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal
doi: 10.3390/ijgi7070275
Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal
Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year. Despite this continuously growing problem, the temporal dynamics of dengue fever and associated potential environmental risk factors are not documented in Nepal. The aim of this study was to fill this research gap by utilizing epidemiological and earth observation data in Chitwan district, one of the frequent dengue outbreak areas of Nepal. We used laboratory confirmed monthly dengue cases as a dependent variable and a set of remotely sensed meteorological and environmental variables as explanatory factors to describe their temporal relationship. Descriptive statistics, cross correlation analysis, and the Poisson generalized additive model were used for this purpose. Results revealed that dengue fever is significantly associated with satellite estimated precipitation, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) synchronously and with different lag periods. However, the associations were weak and insignificant with immediate daytime land surface temperature (dLST) and nighttime land surface temperature (nLST), but were significant after 4&ndash
s Information Criterion (AIC) and maximum R-squared. The best fit model further significantly improved after including delayed effects in the model. The predicted cases were reasonably accurate based on the comparison of 10-fold cross validation and observed cases. The lagged association found in this study could be useful for the development of remote sensing-based early warning forecasts of dengue fever.
5 months. Conclusively, the selected Poisson generalized additive model based on the precipitation, dLST, and NDVI explained the largest variation in monthly distribution of dengue fever with minimum Akaike&rsquo
- Tribhuvan University Nepal
- Kunming Institute of Zoology China (People's Republic of)
- University of Chinese Academy of Sciences China (People's Republic of)
- Chinese Academy of Sciences, Kunming Institute of Zoology China (People's Republic of)
- TRIBHUVAN UNIVERSITY Nepal
Microsoft Academic Graph classification: media_common.quotation_subject Poisson distribution Normalized Difference Vegetation Index Dengue fever symbols.namesake Statistics medicine Time series media_common Variables Generalized additive model Enhanced vegetation index medicine.disease Geography symbols Akaike information criterion
Library of Congress Subject Headings: lcsh:G1-922 lcsh:Geography (General)
Geography, Planning and Development, remote sensing, Nepal, Earth and Planetary Sciences (miscellaneous), dengue fever, Computers in Earth Sciences, early warning, Geography (General), time series model, G1-922
Geography, Planning and Development, remote sensing, Nepal, Earth and Planetary Sciences (miscellaneous), dengue fever, Computers in Earth Sciences, early warning, Geography (General), time series model, G1-922
Microsoft Academic Graph classification: media_common.quotation_subject Poisson distribution Normalized Difference Vegetation Index Dengue fever symbols.namesake Statistics medicine Time series media_common Variables Generalized additive model Enhanced vegetation index medicine.disease Geography symbols Akaike information criterion
Library of Congress Subject Headings: lcsh:G1-922 lcsh:Geography (General)
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