A　network　model　at　both　the　population　and　individual　levels,　which　simulates　both　between-patch　and　within-patch　dynamics,　is　proposed.　We　investigated　the　effects　of　dispersal　networks　and　distribution　of　local　dynamics　on　the　outcome　of　an　epidemic　at　the　population　level.　Numerical　studies　show　that　disease　control　on　random　networks　may　be　easier　than　on　small-world　networks,　depending　on　the　initial　distribution　of　the　local　dynamics.　Spatially　separating　instead　of　gathering　patches　where　disease　locally　persists　is　beneficial　to　global　disease　control　if　dispersal　networks　are　a　type　of　small-world　networks.　Dispersal　networks　with　higher　degree　lead　to　a　higher　mean　value　of　R-0.　Furthermore,　irregularity　of　network　and　randomization　are　beneficial　to　disease　stabilization　and　greatly　affect　the　resulting　global　dynamics.