Covid-19 Hotspot Estimation Using Consensus Methods, SEIR Models and ML Algorithms

The primary objective of this thesis is to identify locations or regions where COVID-19 transmission is more prevalent, termed “hotspots,” assess the likelihood of contracting the virus after visiting crowded areas or potential hotspots, and make predictions on confirmed COVID-19 cases and recoveries. A consensus algorithm is used to identify such hotspots; the SEIR epidemiological model tracks COVID-19 cases, allowing for a better understanding of the disease dynamics and enabling informed decision-making in public health strategies. Consensus-based distributed methodologies have been developed to estimate the magnitude, density, and locations of COVID-19 hotspots to provide well-informed alerts based on continuous data risk assessments. Assuming agents own a mobile device, transmission hotspots use information from user devices with Bluetooth and WiFi. In a consensus-based distributed clustering algorithm, users are divided into smaller groups, and then the number of users is estimated in each group. This process allows for the determination of the population of an outdoor site and the distances between individuals. The proposed algorithm demonstrates versatility by being applicable not only in outdoor environments but also in indoor settings. Considerations are made for signal attenuation caused by walls and other barriers to adapt to indoor environments, and a wall detection algorithm is employed for this purpose. The clustering mechanism is designed to dynamically choose the appropriate clustering technique based on data-dependent patterns, ensuring that every node undergoes proper clustering. After networks have been established and clustered, the output of the consensus algorithmis fed as one of many inputs into the SEIR model. SEIR, representing Susceptible, Exposed, Infectious, and Removed, forms the basis of a model designed to assess the probability of infection at a Point of Interest (POI). The SEIR model utilizes calculated parameters such as β (contact), σ (latency),γ (recovery), ω (loss of immunity) along with current COVID-19 case data to precisely predict the infection spread in a specific area. The SEIR model is implemented with diverse methodologies for transitioning populations between compartments. Hence, the model identifies optimal parameter values under different conditions and scenarios and forecasts the number of infected and recovered cases for the upcoming days.