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 (PfPR2–10) in the two cities through the use of a Random Forest (RF) regressor. The RF models integrated physical and socio-economic information to predict PfPR2–10 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 PfPR2–10 in both cities is diverse and highly variable across the urban fabric. Dense informal settlements exhibit a positive relationship with PfPR2–10 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.

中文翻译：

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使用极高分辨率卫星衍生指标对城市内恶性疟原虫寄生虫率的空间分布进行建模和绘制。


撒哈拉以南非洲地区快速且往往不受控制的城乡移民正在改变城市景观，预计到 2030 年将为 50% 以上的非洲人口提供住所。因此，疟疾负担日益影响城市人口，同时社会经济城市环境内的不平等加剧。很少有研究主要依赖中高分辨率数据集和标准预测变量（例如建筑和植被密度）来解决城市范围内的城市内疟疾建模这一主题。在这项研究中，我们调查了极高分辨率卫星提供的土地利用、土地覆盖和人口信息对城市疟疾流行在大空间范围内的空间分布建模的贡献。作为案例研究，我们将我们的方法应用于两个撒哈拉以南非洲城市：坎帕拉和达累斯萨拉姆。通过使用随机森林 (RF)，利用公开的土地覆盖、土地利用、人口和 OpenStreetMap 数据，对两个城市 2-10 岁年龄组 (PfPR2-10) 标准化的恶性疟原虫寄生虫率进行空间建模。 ) 回归量。 RF 模型综合了物理和社会经济信息来预测整个城市景观的 PfPR2-10。城市内人口分布图用于根据基础人口调整估计值。结果表明，两个城市中 PfPR2-10 的空间分布是多样化的，并且在整个城市结构中变化很大。密集的非正规住区与 PfPR2-10 呈正相关，并且在湿地、沼泽和河岸植被等合适的病媒繁殖地点附近发现了疟疾流行的热点地区。 在这两个城市，非正规住区的较高风险和较富裕社区的较低风险之间存在明显的区别。此外，与城市农业相关的地区显示出较高的疟疾患病率。这项研究的结果强调，与规划居住区的人口相比，居住在非正规住区的人口的疟疾患病率更高。这是由于（i）人类接触病媒的机会增加，（ii）病媒密度增加以及（iii）应对疟疾负担的能力下降。由于非正规住区每年都在迅速扩大，并且往往居住着大部分城市人口，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，这项研究证明了遥感作为流行病学工具的重要性，用于绘制大空间范围内的城市疟疾变化图，以及促进循证政策制定和控制工作。