Analysis of bluetongue disease epizootics in sheep of Andhra Pradesh, India using spatial and temporal autocorrelation.

Bluetongue (BT) disease poses a constant risk to the livestock population around the world. A better understanding of the risk factors will enable a more accurate prediction of the place and time of high-risk events. Mapping the disease epizootics over a period in a particular geographic area will identify the spatial distribution of disease occurrence. A Geographical Information System (GIS) based methodology to analyze the relationship between bluetongue epizootics and spatial-temporal patterns was used for the years 2000 to 2015 in sheep of Andhra Pradesh, India. Autocorrelation (ACF), partial autocorrelation (PACF), and cross-correlation (CCF) analyses were carried out to find the self-dependency between BT epizootics and their dependencies on environmental factors and livestock population. The association with climatic or remote sensing variables at different months lag, including wind speed, temperature, rainfall, relative humidity, normalized difference vegetation index (NDVI), normalized difference water index (NDWI), land surface temperature (LST), was also examined. The ACF & PACF of BT epizootics with its lag showed a significant positive autocorrelation with a month's lag (r = 0.41). Cross-correlations between the environmental variables and BT epizootics indicated the significant positive correlations at 0, 1, and 2 month's lag of rainfall, relative humidity, normalized difference water index (NDWI), and normalized difference vegetation index (NDVI). Spatial autocorrelation analysis estimated the univariate global Moran's I value of 0.21. Meanwhile, the local Moran's I value for the year 2000 (r = 0.32) showed a high degree of spatial autocorrelation. The spatial autocorrelation analysis revealed that the BT epizootics in sheep are having considerable spatial association among the outbreaks in nearby districts, and have to be taken care of while making any forecasting or disease prediction with other risk factors.

Spatio-temporal and time series analysis of bluetongue outbreaks with environmental factors extracted from Google Earth Engine (GEE) in Andhra Pradesh, India.

This study describes the spatial and temporal patterns of bluetongue (BT) outbreaks with environmental factors in undivided Andhra Pradesh, India. Descriptive analysis of the reported BT outbreaks (n = 2,697) in the study period (2000-2017) revealed a higher frequency of outbreaks during monsoon and post-monsoon months. Correlation analysis of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), rainfall and relative humidity (RH) displayed a significant positive correlation with BT outbreaks (p < .05). Retrospective unadjusted space-time, adjusted temporal and spatial analysis detected two, five and two statistically significant (p < .05) clusters, respectively. Time series distribution lag analysis examined the temporal patterns of BT outbreaks with environmental, biophysical factors and estimated that a decrease in 1 unit of rainfall (mm) was associated with 0.2% increase in the outbreak at lag 12 months. Similarly, a 1°C increase in land surface temperature (LST) was associated with 6.54% increase in the outbreaks at lag 12 months. However, an increase in 1 unit of wind speed (m/s) was associated with a 16% decrease in the outbreak at lag 10 months. The predictive model indicated that the peak of BT outbreaks were from October to December, the post-monsoon season in Andhra Pradesh region. The findings suggest that environmental factors influence BT outbreaks, and due to changes in climatic conditions, we may notice higher numbers of BT outbreaks in the coming years. The knowledge of spatial and temporal clustering of BT outbreaks may assist in adopting proper measures to prevent and control the BT spread.

Environmental risk factor assessment for major respiratory disorders in metropolitan cities of India using VIIRS Suomi Aerosol data and Google Trends.

This study has investigated the association between the amount of atmospheric aerosols and the occurrences of Asthma, Chronic Obstructive Pulmonary Disease (COPD) and Lung Cancer in Delhi, Mumbai, Chennai, Kolkata and Bengaluru. Aerosol Optical Thickness (AOT) data of Visible Infrared Imaging Radiometer Suite (VIIRS) and Google Trends (GT) have been used to acquire information regarding the abundance of atmospheric aerosols and the occurrences of the respiratory diseases respectively. The result of Granger causality test between AOT and GT has shown that Delhi, Mumbai and Chennai were quite vulnerable to the three respiratory diseases whereas Bengaluru did not display so much vulnerability to these ailments. Kolkata was not so much vulnerable to Asthma but did exhibit susceptibility to the other two diseases. GT is validated by correlating with Annual Morbidity data of Delhi. The result of Granger causality test between Particulate Matter (diameter ≤ 10 µm) (PM10) data and GT validates the result of Granger causality between AOT and GT, and shows the trustworthiness of GT and AOT. Thus, this study also proves the usefulness of VIIRS AOT and GT as dependable sources of information on atmospheric aerosols and prevalence of the respiratory diseases respectively, and the effectiveness of Granger causality test as a tool of analysis in health and geographic information systems (GIS).