Proposed Epidemiological Models and Methods to Improve COVID 19 Contact Tracing and Outbreak Prediction
In the case of pandemics such as the current COVID 19 pandemic disease spreads rapidly and testing resources are limited. Contact tracing provides an opportunity to fill some of the gaps left by testing limitations. Individuals with confirmed and presumed or suspected positive cases can be identified via user input of testing status or symptoms, as well as by other data sources. The location and movement of these individuals can then be traced. This location information can be used to create a 4D model of locations over time, and the data can be input into a network model to identify other individuals who were likely exposed as well as identify where or among which groups the next outbreaks are likely to occur. This paper describes a few central models based in epidemiology, graph theory, and population biology that can be used, along with location, case, and symptomatology data to create an effective contact tracing algorithm. It then proposes a SEIR model for tracing infections through a network and discusses limitations and options for further fine tuning. Such an algorithm has immense potential to drastically improve epidemiological studies, reduce cases, and prioritize interventions during COVID 19 and future epidemics. The algorithm will be implemented via the Safetrace API, part of the MutualAid.world project.