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Communications over social media, telephone, email, text etc have emerged as an integral part of modern society and they are popularly used for the expression of anger, anxiety, fear, agitation and opinion by the people. People's social interaction tend to increase dramatically during periods of epidemics, protest and calamities. Therefore, above mentioned communication channels plays an important role in the spread of infectious phenomenon, like rumors, fads and effects. These infectious phenomena alters people's behavior during disease epidemic [1][2]. Social contact networks and epidemics co-evolve [1][2]. The spread of a disease influences people's behavior which in turn changes their social contact network, thereby altering the disease spread itself. As a result, there is a need for modeling the spread of these infectious phenomena that lead to changes in behavior. Their propagation among population primarily depends on the social contact network. The nature of social contagion spread is very similar to the spread of any infectious disease as they are contagious in nature. To spread contagious disease requires direct exposure to an infectious agent, whereas social contagions can be spread using various communications media like social networking forums, phones, emails and tweets. EpiSimdemics is an individual-based modeling environment. It uses a people-location bipartite graph as the underlying network [3]. In its current form, EpiSimdemics requires two people to interact at a location to model simulations. Thus, it cannot simulate the spread of social contagions that do not necessarily require the meeting of two agents at a location. We enhance EpiSimdemics by incorporating Person-Person network, which can model communications between people that are not contact based such as communications over email, phone, text and tweet. This Person-Person network is used to model effects (social contagion) which induce behavioral changes in population and thus impacting the disease spread. The disease spread is modeled on Person-Location network. This leads to the scenario of two interacting networks: Person-Person network modeling social contagion and Person-Location modeling disease. Theoretically, there can be multiple such networks modeling various interacting phenomena. We demonstrate the usefulness of this network by modeling and simulating two interacting PTTSs (probabilistic timed transition systems). To model disease epidemics, we have defined Disease Model and to model effects (social contagion), we have defined Fear Model. We show how these models influence each other by performing simulations on EpiSimdemics with interacting Disease and Fear Model. Therefore a model that does not include the affect adaptations on disease epidemics and vice-versa, fails to reflect the actual behavior of a society during disease epidemic spread. The addition of Person-Person network to EpiSimdemics will allow for a better understanding of the affect adaptions, which can include behavior changes in society during an epidemic outbreak. This would lead to effective interventions and help to better understand the dynamics of disease epidemic.