Disorder is a characteristic of real social networks generated by heterogeneity in person-to-person interactions . This property affects the way a disease spreads through a population, reaches a tipping point in the fraction of infected individuals, and becomes an epidemic. Disorder is usually associated with contact times between individuals , and normalized contact time values%DIFDELCMD < \omegaare %%% taken from the distribution P(\omega) = 1 / (a\omega) that mimics "face-to-face" experiments. To model more realistic systems , we study how heterogeneity in person-to-person interactions affects the spreading of diseases when two different kinds of disorder are present, each with a particular value of a. This allows two different types of interaction to emerge, such as close (family, coworkers) and distant (neighbors, strangers) contacts. We also develop a strategy for controlling distant contact times , which are easier to alter in practice , so as to reduce the total number of infected individuals. Finally, we use "face-to-face" experiments to generate a more accurate distribution of normalized contact times, and we repeat the analysis for this distribution  .
In real social networks , person-to-person interactions are known to be heterogeneous, which can affect the way a disease spreads through a population, reaches a tipping point in the fraction of infected individuals, and becomes an epidemic. This property, called disorder, is usually associated with contact times between individuals %DIFDELCMD < \omegaare %%% and can be modeled by a weighted network, where the weights are related to normalized contact times \omega. In this paper , we study the SIR model for disease spreading when both close and distant types of interactions are present. We develop a mitigation strategy that reduces only the time duration of distant contacts , which are easier to alter in practice . Using branching theory, supported by simulations, we found that the effectiveness of the strategy increases when the density f_1 of close contacts decreases. Moreover, we found a threshold _1 = T_c / \beta below which the strategy can bring the system from an epidemic to a non-epidemic phase, even when close contacts have the longest time durations .
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how heterogeneity in person-to-person interactions affects the spreading of diseases when two different kinds of disorder are present, each with a particular value of a. This allows two different types of interaction to emerge, such as close (family, coworkers) and distant (neighbors, strangers) contacts. We also develop a strategy for controlling distant contact times
the SIR model for disease spreading when both close and distant types of interactions are present. We develop a mitigation strategy that reduces only the time duration of distant contacts
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, so as to reduce the total number of infected individuals. Finally, we use "face-to-face" experiments to generate a more accurate distribution of normalized contact times, and we repeat the analysis for this distribution
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