Prevalence and prediction of Lyme disease in Hainan province.
Lyme disease (LD) is one of the most important vector-borne diseases worldwide. However, there is limited information on the prevalence and risk analysis using correlated factors in the tropical areas. A total of 1583 serum samples, collected from five hospitals of Hainan Province, were tested by im...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-03-01
|
| Series: | PLoS Neglected Tropical Diseases |
| Online Access: | https://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0009158&type=printable |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850181839956738048 |
|---|---|
| author | Lin Zhang Xiong Zhu Xuexia Hou Huan Li Xiaona Yang Ting Chen Xiaoying Fu Guangqing Miao Qin Hao Sha Li |
| author_facet | Lin Zhang Xiong Zhu Xuexia Hou Huan Li Xiaona Yang Ting Chen Xiaoying Fu Guangqing Miao Qin Hao Sha Li |
| author_sort | Lin Zhang |
| collection | DOAJ |
| description | Lyme disease (LD) is one of the most important vector-borne diseases worldwide. However, there is limited information on the prevalence and risk analysis using correlated factors in the tropical areas. A total of 1583 serum samples, collected from five hospitals of Hainan Province, were tested by immunofluorescence assay (IFA) and western blot (WB) analyses using anti-Borrelia burgdorferi antibodies. Then, we mapped the distribution of positive rate (by IFA) and the spread of confirmed Lyme patients (by WB). Using ArcGIS, we compiled host-vector-human interactions and correlated data as risk factor layers to predict LD risk in Hainan Province. There are three LD hotspots, designated hotspot I, which is located in central Hainan, hotspot II, which contains Sanya district, and hotspot III, which lies in the Haikou-Qiongshan area. The positive rate (16.67% by IFA) of LD in Qiongzhong, located in hotspot I, was higher than that in four other areas. Of confirmed cases of LD, 80.77% of patients (42/52) whose results had been confirmed by WB were in hotspots I and III. Hotspot II, with unknowed prevalence of LD, need to be paid more attention considering human-vector interaction. Wuzhi and Limu mountains might be the most important areas for the prevalence of LD, as the severe host-vector and human-vector interactions lead to a potential origin site for LD. Qiongzhong is the riskiest area and is located to the east of Wuzhi Mountain. In the Sanya and Haikou-Qiongshan area, intervening in the human-vector interaction would help control the prevalence of LD. |
| format | Article |
| id | doaj-art-9021998de80f4c09bc465bab57e72ea5 |
| institution | OA Journals |
| issn | 1935-2727 1935-2735 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Neglected Tropical Diseases |
| spelling | doaj-art-9021998de80f4c09bc465bab57e72ea52025-08-20T02:17:49ZengPublic Library of Science (PLoS)PLoS Neglected Tropical Diseases1935-27271935-27352021-03-01153e000915810.1371/journal.pntd.0009158Prevalence and prediction of Lyme disease in Hainan province.Lin ZhangXiong ZhuXuexia HouHuan LiXiaona YangTing ChenXiaoying FuGuangqing MiaoQin HaoSha LiLyme disease (LD) is one of the most important vector-borne diseases worldwide. However, there is limited information on the prevalence and risk analysis using correlated factors in the tropical areas. A total of 1583 serum samples, collected from five hospitals of Hainan Province, were tested by immunofluorescence assay (IFA) and western blot (WB) analyses using anti-Borrelia burgdorferi antibodies. Then, we mapped the distribution of positive rate (by IFA) and the spread of confirmed Lyme patients (by WB). Using ArcGIS, we compiled host-vector-human interactions and correlated data as risk factor layers to predict LD risk in Hainan Province. There are three LD hotspots, designated hotspot I, which is located in central Hainan, hotspot II, which contains Sanya district, and hotspot III, which lies in the Haikou-Qiongshan area. The positive rate (16.67% by IFA) of LD in Qiongzhong, located in hotspot I, was higher than that in four other areas. Of confirmed cases of LD, 80.77% of patients (42/52) whose results had been confirmed by WB were in hotspots I and III. Hotspot II, with unknowed prevalence of LD, need to be paid more attention considering human-vector interaction. Wuzhi and Limu mountains might be the most important areas for the prevalence of LD, as the severe host-vector and human-vector interactions lead to a potential origin site for LD. Qiongzhong is the riskiest area and is located to the east of Wuzhi Mountain. In the Sanya and Haikou-Qiongshan area, intervening in the human-vector interaction would help control the prevalence of LD.https://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0009158&type=printable |
| spellingShingle | Lin Zhang Xiong Zhu Xuexia Hou Huan Li Xiaona Yang Ting Chen Xiaoying Fu Guangqing Miao Qin Hao Sha Li Prevalence and prediction of Lyme disease in Hainan province. PLoS Neglected Tropical Diseases |
| title | Prevalence and prediction of Lyme disease in Hainan province. |
| title_full | Prevalence and prediction of Lyme disease in Hainan province. |
| title_fullStr | Prevalence and prediction of Lyme disease in Hainan province. |
| title_full_unstemmed | Prevalence and prediction of Lyme disease in Hainan province. |
| title_short | Prevalence and prediction of Lyme disease in Hainan province. |
| title_sort | prevalence and prediction of lyme disease in hainan province |
| url | https://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0009158&type=printable |
| work_keys_str_mv | AT linzhang prevalenceandpredictionoflymediseaseinhainanprovince AT xiongzhu prevalenceandpredictionoflymediseaseinhainanprovince AT xuexiahou prevalenceandpredictionoflymediseaseinhainanprovince AT huanli prevalenceandpredictionoflymediseaseinhainanprovince AT xiaonayang prevalenceandpredictionoflymediseaseinhainanprovince AT tingchen prevalenceandpredictionoflymediseaseinhainanprovince AT xiaoyingfu prevalenceandpredictionoflymediseaseinhainanprovince AT guangqingmiao prevalenceandpredictionoflymediseaseinhainanprovince AT qinhao prevalenceandpredictionoflymediseaseinhainanprovince AT shali prevalenceandpredictionoflymediseaseinhainanprovince |