An interaction-based contagion model over temporal networks demonstrates that reducing temporal network density reduces total infection rate

Contacts' temporal ordering and dynamics, such as their order and timing, are crucial for understanding the transmission of infectious diseases. Using path-preserving temporal networks, we evaluate the effect of spatial pods (social distancing pods) and temporal pods (meetings' rate reduction) on the spread of the disease. We use our interaction-driven contagion model, instantiated for COVID-19, over history-maintaining random temporal networks as well as over real-world contacts. We find that temporal pods significantly reduce the overall number of infected individuals and slow the spread of the disease. This result is robust under changing initial conditions, such as initial patients' numbers and locations. Social distancing (spatial) pods perform well only at the initial phase of the disease, i.e., with a minimal number of initial patients. Using real-life contact information and extending our interaction-driven model to consider the exposures, we demonstrate the beneficial effect of reducing the temporal density on overall infection rates. We further show that slow-spreading pathogens spread almost as fast-spreading pathogens in dense topologies. Our results show that given the same transmission level, there is a decrease in the disease's rate and spread in less dense networks. Thus, reducing the rate of encounters is more effective than social distancing.