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The role of direct cell-to-cell spread in viral infections - where virionsspread between host and susceptible cells without needing to be secreted intothe extracellular environment - has come to be understood as essential to thedynamics of medically significant viruses like hepatitis C and influenza.Recent work in both the experimental and mathematical modelling literature hasattempted to quantify the prevalence of cell-to-cell infection compared to theconventional free virus route using a variety of methods and experimental data.However, estimates are subject to significant uncertainty and moreover rely ondata collected by inhibiting one mode of infection by either chemical orphysical factors. These methods assume that this inhibition process fullyeliminates its target mode of infection while exactly preserving the dynamicsof the other. In this work, we provide a framework for estimating theprevalence of cell-to-cell infection from data which is experimentallyobtainable without the need for additional interventions, and two standardmathematical models for viral dynamics with the two modes of infection. Weprovide guidance for the design of relevant experiments and mathematical toolsfor accurately inferring the prevalence of cell-to-cell infection.

Spatial information allows inference of the prevalence of direct cell-to-cell viral infection