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

The role of direct cell-to-cell spread in viral infections-where virions spread between host and susceptible cells without needing to be secreted into the extracellular environment-has come to be understood as essential to the dynamics of medically significant viruses like hepatitis C and influenza. Recent work in both the experimental and mathematical modelling literature has attempted to quantify the prevalence of cell-to-cell infection compared to the conventional free virus route using a variety of methods and experimental data. However, estimates are subject to significant uncertainty and moreover rely on data collected by inhibiting one mode of infection by either chemical or physical factors, which may influence the other mode of infection to an extent which is difficult to quantify. In this work, we conduct a simulation-estimation study to probe the practical identifiability of the proportion of cell-to-cell infection, using two standard mathematical models and synthetic data that would likely be realistic to obtain in the laboratory. We show that this quantity cannot be estimated using non-spatial data alone, and that the collection of a data which describes the spatial structure of the infection is necessary to infer the proportion of cell-to-cell infection. Our results provide guidance for the design of relevant experiments and mathematical tools for accurately inferring the prevalence of cell-to-cell infection in in vitro and in vivo contexts. Viruses are known to spread between host cells either via infection with cell-free virions or through direct cell-to-cell infection. The prevalence of cell-to-cell infection for different virus species is not well known, yet is of huge importance to therapeutic applications due to its resilience to drug interventions and the immune response. In this work, we investigated whether the proportion of infections from each mode of spread could theoretically be inferred from data using two standard mathematical models of viral dynamics with both modes of infection. By generating synthetic observational data and refitting using the models, we found that the proportion of cell-to-cell infections could not be obtained using models or data which did not account for the spatial structure of the infection. However, using a spatially-explicit model and (practically obtainable) observational data which measured spatial features of the infection, the proportion of infections from the cell-to-cell route could be reliably inferred, even when collecting data from only small samples of the model tissue. This work will hopefully inform the development of experimental procedures and mathematical models to improve estimates of the prevalence of cell-to-cell infection.