The Effect of Land Surface Temperature on Dengue Hemorrhagic Fever Incidence

Land Surface Temperature (LST) can be used to detect the occurrence of climate change. The change in LST can affect disease patterns such as Dengue Hemorrhagic Fever (DHF). The incidence of DHF in Madura from 2010-2019 showed instability. The highest DHF incidence rate in Madura occurred in 2015. This study aims to analyze the spatial and temporal effect of LST on the incidence of DHF in Madura. The analysis was carried out spatially and temporally, using multivariate regression (spatial) and Autoregression (temporal) methods with a cubic spline for LST. This study used secondary data from two institution. LST data was obtained from the MODIS NASA website, while DHF data was obtained from the East Java Provincial Health Office. LST in most sub-region, increased in 2003 and 2015, then which is in line with the incidence of dengue fever in Madura, which also increased around 2015-2016. The R2 value from the cubic spline test shows that the model used is quite good and has the same performance in all regions of Madura. The Z-value in all regions is negative, which indicates a cold area. The highest Z-value in region 1 is related to Bangkalan Regency which has more incidences of DHF in the highest category. While the lowest Z-value is found in region 3 related to Pamekasan Regency which has never been in the high category. The incidence of DHF based on LST in Madura illustrates that Bangkalan and Sumenep regencies have greater potential than Sampang and Pamekasan regencies

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 4–5 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’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