Department of Biology, San Francisco State University, 1600 Holloway Avenue, San Francisco, CA 94132, USA.
Proc Biol Sci. 2011 Apr 7;278(1708):1025-33. doi: 10.1098/rspb.2010.1720. Epub 2010 Sep 29.
Critical to the mitigation of parasitic vector-borne diseases is the development of accurate spatial predictions that integrate environmental conditions conducive to pathogen proliferation. Species of Plasmodium and Trypanosoma readily infect humans, and are also common in birds. Here, we develop predictive spatial models for the prevalence of these blood parasites in the olive sunbird (Cyanomitra olivacea). Since this species exhibits high natural parasite prevalence and occupies diverse habitats in tropical Africa, it represents a distinctive ecological model system for studying vector-borne pathogens. We used PCR and microscopy to screen for haematozoa from 28 sites in Central and West Africa. Species distribution models were constructed to associate ground-based and remotely sensed environmental variables with parasite presence. We then used machine-learning algorithm models to identify relationships between parasite prevalence and environmental predictors. Finally, predictive maps were generated by projecting model outputs to geographically unsampled areas. Results indicate that for Plasmodium spp., the maximum temperature of the warmest month was most important in predicting prevalence. For Trypanosoma spp., seasonal canopy moisture variability was the most important predictor. The models presented here visualize gradients of disease prevalence, identify pathogen hotspots and will be instrumental in studying the effects of ecological change on these and other pathogens.
寄生虫媒介传播疾病的缓解关键在于开发能够整合有利于病原体增殖的环境条件的准确空间预测模型。疟原虫和锥虫等物种很容易感染人类，也常见于鸟类。在这里，我们为橄榄太阳鸟（Cyanomitra olivacea）中的这些血液寄生虫的流行情况开发了预测性空间模型。由于该物种表现出较高的自然寄生虫流行率，并占据了热带非洲的多种栖息地，因此它代表了一个独特的生态模型系统，可用于研究媒介传播病原体。我们使用 PCR 和显微镜从非洲中部和西部的 28 个地点筛选血液寄生虫。构建物种分布模型将基于地面和遥感的环境变量与寄生虫的存在联系起来。然后，我们使用机器学习算法模型来确定寄生虫流行率与环境预测因子之间的关系。最后，通过将模型输出投影到地理上未采样的区域来生成预测图。结果表明，对于疟原虫属，最冷月的最高温度是预测流行率的最重要因素。对于锥虫属，季节性冠层水分变异性是最重要的预测因子。这里提出的模型可视化了疾病流行率的梯度，确定了病原体热点，对于研究生态变化对这些病原体和其他病原体的影响将具有重要意义。
