Applying a Probabilistic Infection Model for studying contagion processes in contact networks

•Developing the Probabilistic Infection Model (PIM) which can assign multiple disease states to each individual, based on their probability of being in that state.•Validating the simulation results of PIM by comparing with those obtained from the Monte-Carlo method.•Studying the spread of three disease (measles, and two strains of influenza) on the overall contact network as well as at individual levels•Studying the relation of network structure to probability of infection.•Studying how varying transmissibility factors affects the spread of COVID in contact networks.