Login / Signup

Geographical Detector-based influence factors analysis for Echinococcosis prevalence in Tibet, China.

Tian MaDong JiangMengmeng HaoPeiwei FanShize ZhangGongsang QuzhenChuiZhao XueShuai HanWeiPing WuCanjun ZhengFang-Yu Ding
Published in: PLoS neglected tropical diseases (2021)
Echinococcosis, caused by genus Echinococcus, is the most pathogenic zoonotic parasitic disease in the world. In Tibet of the People's Republic of China, echinococcosis refers principally to two types of severe zoonosis, cystic echinococcosis (CE) and alveolar echinococcosis (AE), which place a serious burden on public health and economy in the local community. However, research on the spatial epidemiology of echinococcosis remains inadequate in Tibet, China. Based on the recorded human echinococcosis data, maps of the spatial distribution of human CE and AE prevalence in Tibet were produced at city level and county level respectively, which show that the prevalence of echinococcosis in northern and western Tibet was much higher than that in other regions. We employ a geographical detector to explore the influencing factors for causing CE and AE while sorting information on the maps of disease prevalence and environment factors (e.g. terrain, population, and yak population). The results of our analysis showed that biological factors have the most impact on the prevalence of echinococcosis, of which the yak population contributes the most for CE, while the dog population contributes the most for AE. In addition, the interaction between various factors, as we found out, might further explain the disease prevalence, which indicated that the echinococcosis prevalence is not simply affected by one single factor, but by multiple factors that are correlated with each other complicatedly. Our results will provide an important reference for the evaluation of the echinococcosis risk, control projects, and prevention programs in Tibet.
Keyphrases
  • risk factors
  • public health
  • endothelial cells
  • healthcare
  • mental health
  • magnetic resonance imaging
  • deep learning
  • quantum dots
  • big data
  • artificial intelligence
  • energy transfer
  • health information
  • contrast enhanced