The primary focus of this work is an application of consensus and distributed algorithms to detect COVID-19 transmission hotspots and to assess the risks for infection. More specifically, we design consensus-based distributed strategies to estimate the size and density of COVID-19 hotspots. We assume every person has a mobile device and rely on data collected from the user devices, such as Bluetooth and WiFi, to detect transmission hotspots. To estimate the number of people in a specific outdoor geographic location and their proximity to each other, we first perform consensus-based distributed clustering to group people into sub-clusters and then estimate the number of users in a cluster. Our algorithm has been configured to work for indoor settings where we consider the signal attenuation due to walls and other obstructions, which are detected by using the Canny edge detection and Hough transforms on the floor maps of the indoor space. Our results on indoor and outdoor hotspot simulations consistently show an accurate estimate of the number of persons in a region.