| Discussion | Because we used simulated data to infer parameters, and hence had knowledge of the true model, we were also assuming that these contacts were reported accurately. |
| Estimating transmissibility and pre-existing immunity | We simulated data using different assumptions about age-specific infection rates but left the inference model unchanged. |
| Estimating transmissibility and pre-existing immunity | Next, we simulated data using two ages groups, but with transmission based on the average number of reported physical contacts across 8 European countries in the POLYMOD study (S1B Fig. |
| Characteristic power-law parameters describe uptake in different cell lines | Changes in many kinetic parameters associated with the Zipper mechanism can be mapped to changes in the power-law parameters (Fig 4A; simulated data in 84C Fig). |
| Log 1IKM | Simulated data presented in S4C Fig (B) Power-law parameters for different hosts and bacterial strains. |
| Supporting Information | (Left) Relationship between bacterial uptake and A-R. (Right) Relationship between probability (uptake/MOI) and AR calculated from simulated data . |