title: Control of epidemics via social partnership adjustment.
abstract: Epidemic control is of great importance for human society. Adjusting interacting partners is an effective individualized control strategy. Intuitively, it is done either by shortening the interaction time between susceptible and infected individuals or by increasing the opportunities for contact between susceptible individuals. Here, we provide a comparative study on these two control strategies by establishing an epidemic model with nonuniform stochastic interactions. It seems that the two strategies should be similar, since shortening the interaction time between susceptible and infected individuals somehow increases the chances for contact between susceptible individuals. However, analytical results indicate that the effectiveness of the former strategy sensitively depends on the infectious intensity and the combinations of different interaction rates, whereas the latter one is quite robust and efficient. Simulations are shown to verify our analytical predictions. Our work may shed light on the strategic choice of disease control.
authors.affiliation: School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, People's Republic of China.
authors.forename: Bin
authors.initials: B
authors.lastname: Wu
authors.affiliation: School of Mathematical Sciences, Xiamen University, Xiamen 361005, People's Republic of China.
authors.forename: Shanjun
authors.initials: S
authors.lastname: Mao
authors.affiliation: Department of Mathematics, Beijing Technology and Business University, Beijing 100048, People's Republic of China.
authors.forename: Jiazeng
authors.initials: J
authors.lastname: Wang
authors.affiliation: School of Mathematical Sciences and Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific Computation, Xiamen University, Xiamen 361005, People's Republic of China.
authors.forename: Da
authors.initials: D
authors.lastname: Zhou
mesh_terms: D003140:Communicable Disease Control; D003198:Computer Simulation; D004198:Disease Susceptibility; D058872:Epidemics / Q000517:prevention & control*; D006801:Humans; D008954:Models, Biological; D011336:Probability; D013997:Time Factors