Modeling the Spread of COVID-19 in Enclosed Spaces

SEIR models are typically conjured for populations in open environments; however, there seems to be a lack of these types of models that deal with infection rates amongst enclosed spaces. We have also seen certain age groups struggle to deal with COVID-19 more than others, and to this end, we have constructed an age-structured SEIR model that incorporates the Gammaitoni–Nucci model, which is used for infective material in an enclosed space with ventilation. We apply some sensitivity analysis to better understand which parameters have the biggest impact on overall infection rates, as well as create a realistic scenario in which we apply our model to see the comparison in sickness rates amongst four different age groups with different ventilation filtration systems (UVGI, HEPA) and differing quanta production rates.

SLAM-OR: Simultaneous Localization, Mapping and Object Recognition Using Video Sensors Data in Open Environments from the Sparse Points Cloud

In this paper, we propose a novel approach that enables simultaneous localization, mapping (SLAM) and objects recognition using visual sensors data in open environments that is capable to work on sparse data point clouds. In the proposed algorithm the ORB-SLAM uses the current and previous monocular visual sensors video frame to determine observer position and to determine a cloud of points that represent objects in the environment, while the deep neural network uses the current frame to detect and recognize objects (OR). In the next step, the sparse point cloud returned from the SLAM algorithm is compared with the area recognized by the OR network. Because each point from the 3D map has its counterpart in the current frame, therefore the filtration of points matching the area recognized by the OR algorithm is performed. The clustering algorithm determines areas in which points are densely distributed in order to detect spatial positions of objects detected by OR. Then by using principal component analysis (PCA)—based heuristic we estimate bounding boxes of detected objects. The image processing pipeline that uses sparse point clouds generated by SLAM in order to determine positions of objects recognized by deep neural network and mentioned PCA heuristic are main novelties of our solution. In contrary to state-of-the-art approaches, our algorithm does not require any additional calculations like generation of dense point clouds for objects positioning, which highly simplifies the task. We have evaluated our research on large benchmark dataset using various state-of-the-art OR architectures (YOLO, MobileNet, RetinaNet) and clustering algorithms (DBSCAN and OPTICS) obtaining promising results. Both our source codes and evaluation data sets are available for download, so our results can be easily reproduced.

Synthetic threads through the web of life

CRISPR-Cas gene editing tools have brought us to an era of synthetic biology that will change the world. Excitement over the breakthroughs these tools have enabled in biology and medicine is balanced, justifiably, by concern over how their applications might go wrong in open environments. We do not know how genomic processes (including regulatory and epigenetic processes), evolutionary change, ecosystem interactions, and other higher order processes will affect traits, fitness, and impacts of edited organisms in nature. However, anticipating the spread, change, and impacts of edited traits or organisms in heterogeneous, changing environments is particularly important with “gene drives on the horizon.” To anticipate how “synthetic threads” will affect the web of life on Earth, scientists must confront complex system interactions across many levels of biological organization. Currently, we lack plans, infrastructure, and funding for field science and scientists to track new synthetic organisms, with or without gene drives, as they move through open environments.