دانلود رایگان مقاله انگلیسی شرایط هواشناسی، ارتفاع و پوشش زمین به عنوان پیش بینی کننده برای تحلیل توزیع لیشمانیوز احشا - الزویر 2019

abstract

Visceral leishmaniasis (VL) is a fatal disease caused by sandfly-borne protozoa of the Leishmania genus. This study explored the influence of environmental factors on the distribution of VL in Sinkiang province, Mainland China, which is a known natural focus of leishmaniasis. Disease identification records were obtained from publicly available data, in which the existence of VL at each geographical location had been recorded as part of the surveillance of leishmaniasis in Sinkiang province. Maximum entropy modelling (Maxent) was used to predict the distribution of VL across Sinkiang province, and to match this distribution against environmental variables relating to elevation, climate and land cover, obtained from the WorldClim database, China Meteorological Data Sharing System and the National Geomatic Center of China dataset, respectively. Finally, a regional-scale map was developed to show the potential distribution of VL in the Sinkiang province. Receiver-Operating characteristic (ROC) analysis was used to evaluate the performance of the model. The daily average temperature, maximum temperature of the warmest quarter, daily precipitation and precipitation of the driest month were each found to be predictive of the distribution of VL in Sinkiang. Moreover, we found that presence of VL was significantly influenced by the distribution of grassland and shrubland. The results demonstrate that with proper construction and design, probability surfaces using Maxent can be used as an accurate method by which to predict the distribution of VL in Sinkiang province. The information generated by the model could be used to inform the design of detailed prevention and control strategies for leishmaniasis in this region of Mainland China.

5. Conclusion

Maxent and jackknife testing of VL distribution data against defined environmental variables produced a predictive map of the distribution of VL in Sinkiang province and indicated that temperature, precipitation and land cover all independently influenced the distribution of VL. This information could be used to inform the design of more detailed surveillance programmes and more evidence-based planning for the control of VL in the Sinkiang region, making better use of the limited availability of human resources and funding for such programmes. In addition, the results will improve scientific understanding of the risks of the spread of VL. Further research that incorporates socio-economic information into the model would further enhance predictions about likely changes in the distribution of VL in the Sinkiang region.