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The spread of an infectious disease depends on intrinsic properties of thedisease as well as the connectivity and actions of the population. This studyinvestigates the dynamics of an SIR type model which accounts for humantendency to avoid infection while also maintaining preexisting, interpersonalrelationships. Specifically, we use a network model in which individualsprobabilistically deactivate connections to infected individuals and laterreconnect to the same individuals upon recovery. To analyze this network model,a mean field approximation consisting of a system of fourteen ordinarydifferential equations for the number of nodes and edges is developed. Thissystem of equations is closed using a moment closure approximation for thenumber of triple links. By analyzing the differential equations, it is shownthat, in addition to force of infection and recovery rate, the probability ofdeactivating edges and the average node degree of the underlying networkdetermine if an epidemic occurs.

Epidemic Conditions with Temporary Link Deactivation on a Network SIR Disease Model