  The SARS-CoV-2 infectious outbreak has rapidly spread across the globe and
precipitated varying policies to effectuate physical distancing to ameliorate
its impact. In this study, we propose a new hybrid machine learning model,
SIRNet, for forecasting the spread of the COVID-19 pandemic that couples with
the epidemiological models. We use categorized spatiotemporally explicit
cellphone mobility data as surrogate markers for physical distancing, along
with population weighted density and other local data points. We demonstrate at
varying geographical granularity that the spectrum of physical distancing
options currently being discussed among policy leaders have epidemiologically
significant differences in consequences, ranging from viral extinction to near
complete population prevalence. The current mobility inflection points vary
across geographical regions. Experimental results from SIRNet establish
preliminary bounds on such localized mobility that asymptotically induce
containment. The model can support in studying non-pharmacological
interventions and approaches that minimize societal collateral damage and
control mechanisms for an extended period of time.
