Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
Department of Epidemiology, School of Public Health of University of São Paulo, São Paulo, Brazil.
PLoS Negl Trop Dis. 2024 Jun 3;18(6):e0011811. doi: 10.1371/journal.pntd.0011811. eCollection 2024 Jun.
Dengue, Zika, and chikungunya, whose viruses are transmitted mainly by Aedes aegypti, significantly impact human health worldwide. Despite the recent development of promising vaccines against the dengue virus, controlling these arbovirus diseases still depends on mosquito surveillance and control. Nonetheless, several studies have shown that these measures are not sufficiently effective or ineffective. Identifying higher-risk areas in a municipality and directing control efforts towards them could improve it. One tool for this is the premise condition index (PCI); however, its measure requires visiting all buildings. We propose a novel approach capable of predicting the PCI based on facade street-level images, which we call PCINet.
Our study was conducted in Campinas, a one million-inhabitant city in São Paulo, Brazil. We surveyed 200 blocks, visited their buildings, and measured the three traditional PCI components (building and backyard conditions and shading), the facade conditions (taking pictures of them), and other characteristics. We trained a deep neural network with the pictures taken, creating a computational model that can predict buildings' conditions based on the view of their facades. We evaluated PCINet in a scenario emulating a real large-scale situation, where the model could be deployed to automatically monitor four regions of Campinas to identify risk areas.
PCINet produced reasonable results in differentiating the facade condition into three levels, and it is a scalable strategy to triage large areas. The entire process can be automated through data collection from facade data sources and inferences through PCINet. The facade conditions correlated highly with the building and backyard conditions and reasonably well with shading and backyard conditions. The use of street-level images and PCINet could help to optimize Ae. aegypti surveillance and control, reducing the number of in-person visits necessary to identify buildings, blocks, and neighborhoods at higher risk from mosquito and arbovirus diseases.
登革热、寨卡热和基孔肯雅热的病毒主要通过埃及伊蚊传播，对全球人类健康造成重大影响。尽管最近针对登革热病毒开发了有前景的疫苗，但控制这些虫媒病毒病仍然依赖于蚊子监测和控制。尽管如此，几项研究表明，这些措施并不足够有效或无效。确定市县级的高风险区域，并将控制工作集中在这些区域，可以改善这种情况。其中一种工具是前提条件指数（PCI）；然而，其测量需要访问所有建筑物。我们提出了一种基于立面街景图像预测 PCI 的新方法，我们称之为 PCINet。
我们的研究在巴西圣保罗的一个拥有 100 万居民的城市坎皮纳斯进行。我们调查了 200 个街区，访问了它们的建筑物，并测量了 PCI 的三个传统组成部分（建筑物和后院状况和遮阳）、立面条件（拍摄它们的照片）和其他特征。我们用拍摄的照片训练了一个深度神经网络，创建了一个计算模型，可以根据建筑物立面的视图来预测建筑物的状况。我们在模拟真实大规模情况的场景中评估了 PCINet，在该场景中，该模型可以自动监测坎皮纳斯的四个区域，以识别高风险区域。
PCINet 在将立面状况分为三个级别方面产生了合理的结果，并且是一种可扩展的策略，可以对大面积进行分类。整个过程可以通过从立面数据源收集数据和通过 PCINet 进行推断来实现自动化。立面条件与建筑物和后院条件高度相关，与遮阳和后院条件的相关性也很好。使用街景图像和 PCINet 可以帮助优化埃及伊蚊监测和控制，减少识别高风险蚊子和虫媒病毒病的建筑物、街区和社区所需的现场访问次数。
