Invited Paper: The Case for Small-Scale, Mobile-Enhanced COVID-19 Epidemiology

Our understanding of COVID-19 pandemic epidemiology has many gaps, with many challenges arising on a global scale. This paper looks at the problem at a smaller geographical scale, the extent of the campus of a large organization. Equipped with an asymptomatic testing program and rough location data from the campus wireless network, we make the case that epidemiological models may be informed from this new source of data, which offers fidelity at the temporal resolution of seconds and spatial resolution of a Wi-Fi cell size, in particular for the tasks of pinpointing clusters of cases and contexts of infection transmission. We sketch the design of a system that fuses the two foregoing information streams and explain how the result can be incorporated into standard epidemiological models of communicable disease, both for better parameter estimation in elementary models, as well as for providing spatial inputs into more sophisticated models. We conclude with logistical and privacy considerations we have encountered in an associated ongoing study, to inform similar efforts at other organizations.