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

Qian, William Bhowmick Sanjukta O’Neill Marty Ramisetty-Mikler Suhasini Mikler Armin R.

Modeling the spread of infectious diseases is central to the field of computational epidemiology. Two prominent approaches to modeling the contagion process include (i) simulating the spread in contact networks through Monte-Carlo processes and (ii) tracking the disease dynamics using meta-population models. In both cases, the individuals are explicitly (contact networks) or implicitly (meta-population) assumed to belong to exactly one disease state (e.g., susceptible, infected, etc.). In reality, the disease states of individuals are rarely so cleanly compartmentalized. A particular agent can exist in multiple disease states (such as infected and exposed) concurrently with varying probability. To model this stochasticity, we present a new method, that we term as the Probabilistic Infection Model (PIM). Unlike traditional models that assign exactly one state to each agent at each time step, the PIM computes the probability of each agent being in each of the infectious states. Our proposed PIM provides a more layered understanding of the dynamics of the outbreak at individual levels, by allowing the users to (i) estimate the value of R0 at individual vertices and (ii) instead of an all or none value, provides the probability of each infected state of an agent. Additionally, using our probabilistic approach the overall trajectories of the outbreaks can be computed in one simulation, as opposed to the numerous (order of hundreds) repeated simulations required for the Monte Carlo process. We demonstrate the efficacy of PIM by comparing the results of the PIM simulations with those obtained by simulating stochastic SEIR models, as well as the time required for the simulations. We present results at the system and at the individual levels for three diseases;measles and two strains of influenza. We demonstrate how the PIM can be used to study the effect of varying the transimissibility of COVID-19 on its outbreak. This paper is an extended version of a manuscript published in the proceedings of the 2020 International Conference on Computational Science (ICCS)[30]. These extensions are primarily within Sections 4 (Relationship between graph structure and probability of infection) and 5 (Effect of varying COVID-19 transmissibility on outbreak dynamics).