Brousse O, Georganos S, Demuzere M, Dujardin S, Lennert M, Linard C, Snow R W, Thiery W, van Lipzig N P M
Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium.
UCL Institute for Environmental Design and Engineering, University College London, London, United Kingdom.
Environ Res Lett. 2020 Dec 15;15(12):124051. doi: 10.1088/1748-9326/abc996.
Malaria burden is increasing in sub-Saharan cities because of rapid and uncontrolled urbanization. Yet very few studies have studied the interactions between urban environments and malaria. Additionally, no standardized urban land-use/land-cover has been defined for urban malaria studies. Here, we demonstrate the potential of local climate zones (LCZs) for modeling malaria prevalence rate ( PR) and studying malaria prevalence in urban settings across nine sub-Saharan African cities. Using a random forest classification algorithm over a set of 365 malaria surveys we: (i) identify a suitable set of covariates derived from open-source earth observations; and (ii) depict the best buffer size at which to aggregate them for modeling PR. Our results demonstrate that geographical models can learn from LCZ over a set of cities and be transferred over a city of choice that has few or no malaria surveys. In particular, we find that urban areas systematically have lower PR (5%-30%) than rural areas (15%-40%). The PR urban-to-rural gradient is dependent on the climatic environment in which the city is located. Further, LCZs show that more open urban environments located close to wetlands have higher PR. Informal settlements-represented by the LCZ 7 (lightweight lowrise)-have higher malaria prevalence than other densely built-up residential areas with a mean prevalence of 11.11%. Overall, we suggest the applicability of LCZs for more exploratory modeling in urban malaria studies.
由于快速且无节制的城市化，撒哈拉以南城市的疟疾负担正在加重。然而，很少有研究探讨城市环境与疟疾之间的相互作用。此外，尚未为城市疟疾研究定义标准化的城市土地利用/土地覆盖。在此，我们展示了局部气候区（LCZ）在模拟疟疾患病率（PR）以及研究撒哈拉以南非洲九个城市的城市环境中疟疾患病率方面的潜力。我们使用随机森林分类算法对365次疟疾调查数据集进行分析：（i）确定一组从开源地球观测数据得出的合适协变量；（ii）描绘为模拟PR而汇总这些协变量的最佳缓冲大小。我们的结果表明，地理模型可以从一组城市的LCZ中学习，并应用于几乎没有或没有疟疾调查的选定城市。特别是，我们发现城市地区的PR（5%-30%）系统性地低于农村地区（15%-40%）。城市到农村的PR梯度取决于城市所处的气候环境。此外，LCZ表明，靠近湿地的更开放城市环境中PR更高。以LCZ 7（轻型低层）为代表的非正式住区的疟疾患病率高于其他密集建成的居民区，平均患病率为11.11%。总体而言，我们建议LCZ在城市疟疾研究中更适用于探索性建模。
