Predicting Onset of COVID-19 with Mobility-Augmented SEIR Model

Wu, N.; Ben, X.; Green, B.; Rough, K.; Venkatramanan, S.; Marathe, M.; Eastham, P.; Sadilek, A.; O'Banion, S.

Timely interventions and early preparedness of healthcare resources are crucial measures to tackle the mbox{COVID-19} disease. To aid these efforts, we developed the Mobility-Augmented SEIR model (mbox{MA-SEIR}) that leverages Google's aggregate and anonymized mobility data to augment classic compartmental models. We show in a retrospective analysis how this method can be applied at an early stage in the mbox{COVID-19} epidemic to forecast its subsequent spread and onset in different geographic regions, with minimal parameterization of the model. This provides insight into the role of near real-time aggregate mobility data in disease spread modeling by quantifying substantial changes in how populations move both locally and globally. These changes would be otherwise very hard to capture using less timely data.