Abstract
Being a globally emerging mite-borne zoonotic disease, scrub typhus is a serious public health concern in Nepal. Mapping environmental suitability and quantifying the human population under risk of the disease is important for prevention and control efforts. In this study, we model and map the environmental suitability of scrub typhus using the ecological niche approach, machine learning modeling techniques, and report locations of scrub typhus along with several climatic, topographic, Normalized Difference Vegetation Index (NDVI), and proximity explanatory variables and estimated population under the risk of disease at a national level. Both MaxEnt and RF technique results reveal robust predictive power with test The area under curve (AUC) and true skill statistics (TSS) of above 0.8 and 0.6, respectively. Spatial prediction reveals that environmentally suitable areas of scrub typhus are widely distributed across the country particularly in the low-land Tarai and less elevated river valleys. We found that areas close to agricultural land with gentle slopes have higher suitability of scrub typhus occurrence. Despite several speculations on the association between scrub typhus and proximity to earthquake epicenters, we did not find a significant role of proximity to earthquake epicenters in the distribution of scrub typhus in Nepal. About 43% of the population living in highly suitable areas for scrub typhus are at higher risk of infection, followed by 29% living in suitable areas of moderate-risk, and about 22% living in moderately suitable areas of lower risk. These findings could be useful in selecting priority areas for surveillance and control strategies effectively.
Highlights
Scrub typhus, an acute febrile zoonotic disease originating from Japan, is caused by bacteria called orientia tsutsugamushi 1899 [1]
Among the selected 16 geographic and environmental variables proximity to cropland, elevation, and slope and distance to urban land were the major contributors in both models, rank of importance was little different depending on the modeling techniques (Figure 4)
The negative association of proximity to cropland and proximity to urban land to the probability of occurrence of scrub typhus is observed for Nepal
Summary
The overall framework of this study included data collection, processing, the fitting machine. The overall of this study data collection, processing, the fitting machine learning model,framework model evaluation, and included prediction and generation of the scrub typhus suitability map learning model, model evaluation, and prediction and generation of the scrub typhus suitability map (Figure 1). Nepal lies on southern slope of Himalaya between India and China in latitudes of 26°22′N to. It is a mountainous country with an area of 147181 square square kilometers. The TSS compares the number of correct predictions, minus predictions attributable to random guessing. In other words, it is the sum of sensitivity and specificity minus 1. Its value ranges from −1 to +1, where +1 indicates perfect score,
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