Interconnection of Human Responsive Behavior Patterns and Epidemic Spread Dynamics
Faryad Darabi Sahneh, Caterina Scoglio.
The goal of this project is to model the mutual impact of the human behavior and the epidemic dynamics. Modeling human reactions to the spread of infectious disease is an important topic in current epidemiology, and has recently attracted a substantial attention. However, few results are available in the literature, which consider the human response to the epidemic in a systematic framework and the contributions to the problem are still in an early stage. The challenges in this topic concern not only how to model human reactions to the presence of epidemics, but also how these reactions affect the spread of the disease itself. In a general view, human response to an epidemic spread can be categorized in the following three types: 1) Change in the system state. For example, in a vaccination scenario individuals go directly from susceptible state to recovered without going through infected state. 2) Change in system parameters. For example, individuals might choose to use masks. Those who use masks have a smaller infection rate parameter, 3) Change in the contact topology. For example, due to the perception of a serious danger, individuals reduce their contacts with other people who can potentially be infectious.
In the class of individual-based stochastic epidemic models, each individual is represented by a node and the contacts are represented by links among the nodes. Each individual can be in different states (or compartments). For example, in SIS model each individual can either be susceptible or infected. The transitions among the compartments for individual are determined by the state of the individual and his/her neighborhood. The individual-based epidemic model describes the time evolution of the compartment occupancy probabilities. Although these models provide an individual-level description of the spreading process, they do not incorporate any responsive behavior of the individuals to the spreading of the infection. Extending the existing individual-based models to incorporate human behaviors is the frontier of the state of the art of epidemic modeling. However, the adjustments, modifications, or responses of the environment characteristics as the direct consequence of the spread of a disease are not taken into account. Whereas, in closed-loop modeling, we would like to understand how the response to the epidemic spreading can alter the course of the epidemic itself.
It is very challenging to properly integrate individual behaviors with the infection spreading process in a model. This problem becomes even more challenging when considering the highly complex architecture of modern society. We expect to develop a novel framework for individual-centered epidemic models to incorporate individual preventive behaviors. Our modeling framework is based on rigorous theory of stochastic processes and controls. As an example, we have built a novel model based on the N-intertwined SIS model. In this model, we take into the behavior of susceptible individuals. Specifically, as it is recognized that an infection exists, the susceptible individuals adopt a cautious behavior. We have modeled the cautious behavior as having a lower infection rate. Also, in order to model the behavior of the susceptible individuals, we add a new compartment to the classic SIS model for epidemic spread modeling to propose a Susceptible-Alert-Infected-Susceptible (SAIS) model. Both susceptible and alert individuals can potentially be infected. However, the infection rate of the alert people is lower. Furthermore, we are considering the role of information dissemination policies in boosting public health resilience against infectious diseases.
Darabi Sahneh F., Scoglio C., Van Mieghem P., "Generalized Epidemic Mean-Field Model for Spreading Processes over Multi-Layer Complex networks (pdf)," IEEE/ACM Transactions on Networking, 2013, to appear.
Darabi Sahneh F., Chowdhury F., Scoglio C., "On the Existence of a Threshold for Preventive Behavioral Responses to Suppress Epidemic Spreading," Nature Scientific Reports, Vol 2:632, 2012.
Darabi Sahneh F., Scoglio C., and Chowdhury F. N., "Effect of Coupling on the Epidemic Threshold in Interconnected Complex Networks: A Spectral Analysis" Proceedings of American Control Conference, Washington DC, 2013, to appear.
Darabi Sahneh F., Scoglio C., "Optimal Information Dissemination in Epidemic Networks (pdf)," Proceedings of IEEE Conference on Decision and Control, Maui, Hawaii, Dec. 2012.
Darabi Sahneh F., Scoglio C., "Epidemic Spread in Human Networks," Proceedings of IEEE Conference on Decision and Control,Orlando, Dec. 2011.