Facilitating fine-grained intra-urban dengue forecasting by integrating urban environments measured from street-view images

Background Dengue fever (DF) is a mosquito-borne infectious disease that has threatened tropical and subtropical regions in recent decades. An early and targeted warning of a dengue epidemic is important for vector control. Current studies have primarily determined weather conditions to be the main factor for dengue forecasting, thereby neglecting that environmental suitability for mosquito breeding is also an important factor, especially in fine-grained intra-urban settings. Considering that street-view images are promising for depicting physical environments, this study proposes a framework for facilitating fine-grained intra-urban dengue forecasting by integrating the urban environments measured from street-view images. Methods The dengue epidemic that occurred in 167 townships of Guangzhou City, China, between 2015 and 2019 was taken as a study case. First, feature vectors of street-view images acquired inside each township were extracted by a pre-trained convolutional neural network, and then aggregated as an environmental feature vector of the township. Thus, townships with similar physical settings would exhibit similar environmental features. Second, the environmental feature vector is combined with commonly used features (e.g., temperature, rainfall, and past case count) as inputs to machine-learning models for weekly dengue forecasting. Results The performance of machine-learning forecasting models (i.e., MLP and SVM) integrated with and without environmental features were compared. This indicates that models integrating environmental features can identify high-risk urban units across the city more precisely than those using common features alone. In addition, the top 30% of high-risk townships predicted by our proposed methods can capture approximately 50–60% of dengue cases across the city. Conclusions Incorporating local environments measured from street view images is effective in facilitating fine-grained intra-urban dengue forecasting, which is beneficial for conducting spatially precise dengue prevention and control.

Proximity Environmental Feature Based Tree Health Assessment Scheme Using Internet of Things and Machine Learning Algorithm

Improperly grown trees may cause huge hazards to the environment and to humans, through e.g., climate change, soil erosion, etc. A proximity environmental feature-based tree health assessment (PTA) scheme is proposed to prevent these hazards by providing guidance for early warning methods of potential poor tree health. In PTA development, tree health is defined and evaluated based on proximity environmental features (PEFs). The PEF takes into consideration the seven surrounding ambient features that strongly impact tree health. The PEFs were measured by the deployed smart sensors surrounding trees. A database composed of tree health and relative PEFs was established for further analysis. An adaptive data identifying (ADI) algorithm is applied to exclude the influence of interference factors in the database. Finally, the radial basis function (RBF) neural network (NN), a machine leaning algorithm, has been identified as the appropriate tool with which to correlate tree health and PEFs to establish the PTA algorithm. One of the salient features of PTA is that the algorithm can evaluate, and thus monitor, tree health remotely and automatically from smart sensor data by taking advantage of the well-established internet of things (IoT) network and machine learning algorithm.

Using Unsupervised Artificial Neural Networks to Detect Sibling Species: A case in Myxomycetes

Species distribution modelling (SDM) --i.e. the prediction of species potential geographic distributions based on correlations between known presence records and the environmental conditions at occurrence localities-- is one of the most freqently cited developments in recent years in the realm of biodiversity studies (Web of Science 2019). The reasons for the explosion in SDM studies reside in: a) the pressure to know species distributions (under both present and future climate change scenarios) with precision to satisfy scientific as well as societal objectives; b) the impossibility of knowing every occurrence of all but the most conspicuous and/or special interest species; c) the availability of primary occurrence and environmental data in unprecedented amounts, thanks to initiatives like the Global Biodiversity Information Facility (GBIF); and d) the development of algorithms and software, along with the computing power, which allow inference of species distribution models from the available data. The standard methods to produce such models are based on environmental feature vectors, and some well-established algorithms such as distance-based machine learning, regression or a combination of these (Tsoar et al. 2007). In this presentation, we explore deep learning techniques (LeCun et al. 2015), particularly how those developed in recent years could contribute to the study of species distribution (see also Botella et al. 2018; Deneu et al. 2018). In this contribution we aim to identify sibling species on the basis of their ecological preferences. In this exercise, we prepare an image-based environmental representation space using an unsupervised classification approach, instead of a set of environmental feature vectors. For our case study, we chose a well-known cosmopolitan myxomycete (i.e., mycetozoan) species: Hemitrichia serpula (Scop.) Rostaf., whose ecological preferences --involving several biomes-- may suggest that it could comprise several sibling species.