Department of Geoscience, Environment & Society, Université Libre de Bruxelles, 1050, Brussels, Belgium.
Department of Earth and Environmental Sciences, KU Leuven, Celestijnenlaan 200E, 3001, Louvain, Belgium.
Int J Health Geogr. 2020 Sep 21;19(1):38. doi: 10.1186/s12942-020-00232-2.
The rapid and often uncontrolled rural-urban migration in Sub-Saharan Africa is transforming urban landscapes expected to provide shelter for more than 50% of Africa's population by 2030. Consequently, the burden of malaria is increasingly affecting the urban population, while socio-economic inequalities within the urban settings are intensified. Few studies, relying mostly on moderate to high resolution datasets and standard predictive variables such as building and vegetation density, have tackled the topic of modeling intra-urban malaria at the city extent. In this research, we investigate the contribution of very-high-resolution satellite-derived land-use, land-cover and population information for modeling the spatial distribution of urban malaria prevalence across large spatial extents. As case studies, we apply our methods to two Sub-Saharan African cities, Kampala and Dar es Salaam.
Openly accessible land-cover, land-use, population and OpenStreetMap data were employed to spatially model Plasmodium falciparum parasite rate standardized to the age group 2-10 years (PfPR) in the two cities through the use of a Random Forest (RF) regressor. The RF models integrated physical and socio-economic information to predict PfPR across the urban landscape. Intra-urban population distribution maps were used to adjust the estimates according to the underlying population.
The results suggest that the spatial distribution of PfPR in both cities is diverse and highly variable across the urban fabric. Dense informal settlements exhibit a positive relationship with PfPR and hotspots of malaria prevalence were found near suitable vector breeding sites such as wetlands, marshes and riparian vegetation. In both cities, there is a clear separation of higher risk in informal settlements and lower risk in the more affluent neighborhoods. Additionally, areas associated with urban agriculture exhibit higher malaria prevalence values.
The outcome of this research highlights that populations living in informal settlements show higher malaria prevalence compared to those in planned residential neighborhoods. This is due to (i) increased human exposure to vectors, (ii) increased vector density and (iii) a reduced capacity to cope with malaria burden. Since informal settlements are rapidly expanding every year and often house large parts of the urban population, this emphasizes the need for systematic and consistent malaria surveys in such areas. Finally, this study demonstrates the importance of remote sensing as an epidemiological tool for mapping urban malaria variations at large spatial extents, and for promoting evidence-based policy making and control efforts.
在撒哈拉以南非洲，农村到城市的快速且常常不受控制的迁移正在改变城市景观，预计到 2030 年，城市将为非洲超过 50%的人口提供住所。因此，疟疾负担越来越多地影响城市人口，而城市环境中的社会经济不平等现象也在加剧。很少有研究依靠中高分辨率数据集和建筑物密度、植被密度等标准预测变量来解决城市内部疟疾建模的问题。在这项研究中，我们探讨了利用高分辨率卫星衍生的土地利用、土地覆盖和人口信息来模拟城市疟疾流行的空间分布在大的空间范围内。作为案例研究，我们将方法应用于撒哈拉以南非洲的两个城市，坎帕拉和达累斯萨拉姆。
我们利用公开获取的土地覆盖、土地利用、人口和 OpenStreetMap 数据，通过使用随机森林 (RF) 回归器，在这两个城市中对年龄在 2-10 岁之间的间日疟原虫寄生虫率（PfPR）进行空间建模。RF 模型综合了物理和社会经济信息，预测城市景观中 PfPR 的分布。我们使用城市内部人口分布地图来根据潜在人口调整估计值。
结果表明，这两个城市的 PfPR 空间分布在城市结构中差异很大且高度可变。密集的非正规住区与 PfPR 呈正相关，疟疾高发热点位于湿地、沼泽和河岸植被等适宜媒介滋生地附近。在这两个城市中，在非正规住区的风险较高，而在较富裕的社区风险较低。此外，与城市农业相关的区域也显示出更高的疟疾流行值。
本研究结果强调，居住在非正规住区的人口与居住在规划住宅区的人口相比，疟疾流行率更高。这是由于 (i) 人类接触病媒的机会增加，(ii) 病媒密度增加，(iii) 应对疟疾负担的能力降低。由于非正规住区每年都在迅速扩张，并且往往容纳了城市人口的大部分，因此这强调了在这些地区进行系统和一致的疟疾调查的必要性。最后，本研究表明，遥感作为一种在大的空间范围内绘制城市疟疾变化的流行病学工具的重要性，以及促进基于证据的政策制定和控制工作。
