An analysis of clinical and geographical metadata of over 75,000 records in the GISAID COVID-19 database

Velazquez, Anjelina; Bustria, Mhealyssah; Ouyang, Youwen; Moshiri, Niema

During the SARS-CoV-2 outbreak that caused the coronavirus pandemic it is important now more than ever that scientists and public health officials work side-by-side and use their available resources to track patient information from those that have been affected by the novel coronavirus. The ability to track the disease helps identify possible trends and patterns that can be used by public health officials to make more informed decisions. Tracking data like this may be the key to helping states and countries safely re-open. However, when analyzing large collections of data there is the occurrence of confounding factors such as biases in patient sampling. In this project, a massive collection of COVID-19 data was analyzed, and explored potential biases in patient sampling were explored.