Mapping Environmental Suitability of Scrub Typhus in Nepal Using MaxEnt and Random Forest Models

Bipin Kumar Acharya, Wei Chen, Zengliang Ruan, Gobind Prasad Pant, Yin Yang, Lalan Prasad Shah, Chunxiang Cao, Zhiwei Xu, Meghnath Dhimal and Hualiang Lin
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Bipin Kumar Acharya: Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
Wei Chen: Institute of Surface-Earth System Science, Tianjin University, Tianjin 300072, China
Zengliang Ruan: Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
Gobind Prasad Pant: Department of Public Health, Manmohan Memorial Institute of Health Sciences, Kathmandu 44613, Nepal
Yin Yang: Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
Lalan Prasad Shah: Department of Health Services, Teku, Kathmandu 44600, Nepal
Chunxiang Cao: State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Zhiwei Xu: School of Public Health, Faculty of Medicine, the University of Queensland, Herston, QLD 4006, Australia
Meghnath Dhimal: Nepal Health Research Council, Kathmandu 44600, Nepal
Hualiang Lin: Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China

IJERPH, 2019, vol. 16, issue 23, 1-14

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.

Keywords: scrub typhus; suitability mapping; machine learning; Nepal (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
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