Български
Albanian
Arabic
Armenian
Azerbaijani
Belarusian
Bengali
Bosnian
Catalan
Czech
Danish
Deutsch
Dutch
English
Estonian
Finnish
Français
Greek
Haitian Creole
Hebrew
Hindi
Hungarian
Icelandic
Indonesian
Irish
Italian
Japanese
Korean
Latvian
Lithuanian
Macedonian
Mongolian
Norwegian
Persian
Polish
Portuguese
Romanian
Russian
Serbian
Slovak
Slovenian
Spanish
Swahili
Swedish
Turkish
Ukrainian
Vietnamese
Български
中文(简体)
中文(繁體)
Vector-Borne and Zoonotic Diseases 2019-Apr

Environmental Risk Factors and Geographic Distribution of Severe Fever with Thrombocytopenia Syndrome in Jiangsu Province, China.

Само регистрирани потребители могат да превеждат статии
Вход / Регистрация
Линкът е запазен в клипборда
Dawei Zhang
Changkui Sun
Huiyan Yu
Jingxin Li
Wendong Liu
Zhifeng Li
Changjun Bao
Dapeng Liu
Nan Zhang
Fengcai Zhu

Ключови думи

Резюме

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging natural focus, tick-borne disease caused by a novel bunyavirus named SFTS virus (SFTSV). The main purpose of this study was to analyze the environmental risk factors and geographic distribution of SFTS natural foci in Jiangsu Province. A retrospective space-time analysis by SaTScan software was used to detect clusters at the town level. The maximum entropy modeling method was applied to construct the ecological niche model and analyze the environmental risk factors, and then to draw the predicted risk map. The performance of the model was assessed using the area under the curve (AUC) and known occurrence locations. During the years 2010-2016, a total of 140 laboratory-confirmed indigenous SFTS cases occurred in Jiangsu Province, with 66 occurrence locations. The reported number of SFTS cases increased year by year and SFTS cases occurred from April to October with a peak between May and August each year. Three clusters detected by space-time scan statistical analysis were connected together and shared the similar ecological environmental characteristic of hilly landscape. Fifteen environmental variables with different percent contribution can influence the ecological niche model in different degrees, whereas slope (suitable range: 0.1-4) and maximum temperature of warmest month (suitable range: 32.8-34.2°C) as the key environmental factors contributed 46.1% and 23.2%, respectively. The model had high accuracy on prediction with the averaged training AUC of 0.926. Within a predicted risk map, potential areas at high risk and 10 previously unidentified endemic regions were recognized. The distribution of SFTS natural foci was under the influence of multidimensional environmental factors. Slope and maximum temperature of warmest month were the key environmental risk factors. These results provide a valuable basis for the selection of prevention and control strategies, and the identification of potential risk areas.

Присъединете се към нашата
страница във facebook

Най-пълната база данни за лечебни билки, подкрепена от науката

  • Работи на 55 езика
  • Билкови лекове, подкрепени от науката
  • Разпознаване на билки по изображение
  • Интерактивна GPS карта - маркирайте билките на място (очаквайте скоро)
  • Прочетете научни публикации, свързани с вашето търсене
  • Търсете лечебни билки по техните ефекти
  • Организирайте вашите интереси и бъдете в крак с научните статии, клиничните изследвания и патентите

Въведете симптом или болест и прочетете за билките, които биха могли да помогнат, напишете билка и вижте болестите и симптомите, срещу които се използва.
* Цялата информация се базира на публикувани научни изследвания

Google Play badgeApp Store badge