High Resolution Niche Models of Malaria Vectors in Northern Tanzania: A New Capacity to Predict Malaria Risk?
High Resolution Niche Models of Malaria Vectors in Northern Tanzania: A New Capacity to Predict Malaria Risk?
复制标题
DOI：
10.1371/journal.pone.0009396
复制
发表时间：
2010-02-24
期刊：
影响因子：
3.7
通讯作者：
Kerr, Jeremy T.
中科院分区：
文献类型：
--
作者：
Kulkarni, Manisha A.;Desrochers, Rachelle E.;Kerr, Jeremy T.
背景：非洲的疟疾传播率在几公里的范围内可能存在巨大差异。这种空间异质性反映了媒介蚊子栖息地的变化，并对疟疾控制资源的有效分配构成了重要障碍。由于媒介物种的组合对控制干预措施的反应不同，疟疾控制变得更加复杂。最近的建模创新使得预测非洲大陆媒介分布和推断疟疾风险成为可能，但这些风险绘图工作尚未弥合指导实地控制工作的空间差距。方法/主要发现：我们使用最大熵与专门构建的高分辨率土地覆盖数据和其他环境因素来模拟东非 94,000 km(2) 地区三种主要疟疾媒介物种的空间分布。每个载体的生态位模型都需要遥感土地覆盖。降水的季节性和年最高气温也有助于建立阿拉伯按蚊和按蚊的生态位模型。富内斯图斯有限公司（AUC 分别为 0.989 和 0.991），但冷季降水和海拔对 An. 很重要。冈比亚船队（曲线下面积 0.997）。尽管这些利基模型看起来非常准确，但关键的测试是它们是否可以改善对人群中疟疾患病率的预测。基于社区的疟疾患病率测量结果 1.5 公里范围内的媒介栖息地与海拔相互作用，可大大改善对儿童恶性疟原虫患病率的预测。纳入疟疾流行与病媒栖息地之间的机制联系大大提高了流行率预测的精度和准确度（包括病媒栖息地，r(2) = 0.83，或不包括病媒栖息地，r(2) = 0.50）。包括病媒栖息地在内的预测是无偏的（流行率的观察与模型预测：斜率 = 1.02）。使用该模型，我们生成了整个研究区域预测疟疾患病率的高分辨率地图。 结论/意义：蚊子生态位空间与沿海拔梯度的小气候之间的相互作用表明，气候和土地利用变化可能会加剧东非高地的疟疾死灰复燃。然而，精确地指导干预措施以改善潜在影响是可能的。
Background: Malaria transmission rates in Africa can vary dramatically over the space of a few kilometres. This spatial heterogeneity reflects variation in vector mosquito habitat and presents an important obstacle to the efficient allocation of malaria control resources. Malaria control is further complicated by combinations of vector species that respond differently to control interventions. Recent modelling innovations make it possible to predict vector distributions and extrapolate malaria risk continentally, but these risk mapping efforts have not yet bridged the spatial gap to guide on-the-ground control efforts.Methodology/Principal Findings: We used Maximum Entropy with purpose-built, high resolution land cover data and other environmental factors to model the spatial distributions of the three dominant malaria vector species in a 94,000 km(2) region of east Africa. Remotely sensed land cover was necessary in each vector's niche model. Seasonality of precipitation and maximum annual temperature also contributed to niche models for Anopheles arabiensis and An. funestus s.l. (AUC 0.989 and 0.991, respectively), but cold season precipitation and elevation were important for An. gambiae s.s. (AUC 0.997). Although these niche models appear highly accurate, the critical test is whether they improve predictions of malaria prevalence in human populations. Vector habitat within 1.5 km of community-based malaria prevalence measurements interacts with elevation to substantially improve predictions of Plasmodium falciparum prevalence in children. The inclusion of the mechanistic link between malaria prevalence and vector habitat greatly improves the precision and accuracy of prevalence predictions (r(2) = 0.83 including vector habitat, or r(2) = 0.50 without vector habitat). Predictions including vector habitat are unbiased (observations vs. model predictions of prevalence: slope = 1.02). Using this model, we generate a high resolution map of predicted malaria prevalence throughout the study region.Conclusions/Significance: The interaction between mosquito niche space and microclimate along elevational gradients indicates worrisome potential for climate and land use changes to exacerbate malaria resurgence in the east African highlands. Nevertheless, it is possible to direct interventions precisely to ameliorate potential impacts.