Many known and unknown factors play significant roles in the persistence of an infectiousdisease, but two that are often ignored in theoretical modelling are the distributions of (i) inherent susceptibility(σinh) and (ii) external infectivity (ʅext), in a population. While the former is determined by theimmunity of an individual towards a disease, the latter depends on the exposure of a susceptible person to theinfection. We model the spatio-temporal propagation of a pandemic as a chemical reaction kinetics on a network using a modified SAIR (Susceptible-Asymptomatic-Infected-Removed) model to include these twodistributions. The resulting integro-differential equations are solved using Kinetic Monte Carlo CellularAutomata (KMC-CA) simulations. Coupling between σinh and ʅext are combined into a new parameter Ω,defined as Ω = σinh ˟ ʅext; infection occurs only if the value of Ω is greater than a Pandemic InfectionParameter (PIP), Ω0. Not only does this parameter provide a microscopic viewpoint of the reproductionnumber R0 advocated by the conventional SIR model, but it also takes into consideration the viral loadexperienced by a susceptible person. We find that the neglect of this coupling could compromise quantitativepredictions and lead to incorrect estimates of the infections required to achieve the herd immunity threshold.