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Table 4 Optimal 5-year (Y1 through Y5) sequences of interventions for the optimistic efficacy scenario

From: Multi-year optimization of malaria intervention: a mathematical model

Geographic
region
Number of
districts
Initial
population state
Y1 intervention
(end pop. state)
Y2 intervention
(end pop. state)
Y3 intervention
(end pop. state)
Y4 intervention
(end pop. state)
Y5 intervention
(end pop. state)
(D, L) 500 (60, 15, 25) LLIN_ACT 60 % ACT 60 % ACT 20 % ACT 60 % None
(90, 0, 10) (95, 0, 5) (100, 0, 0) (100, 0, 0) (100, 0, 0)
(D, M) 500 (60, 15, 25) LLIN_ACT 60 % ACT 40 % ACT 40 % ACT 20 % None
(90, 0, 10) (95, 0, 5) (100, 0, 0) (100, 0, 0) (100, 0, 0)
(D, H) 500 (60, 15, 25) ACT 60 % ACT 60 % ACT 40 % ACT 60 % ACT 40 %
(85, 5, 10) (95, 0, 5) (100, 0, 0) (100, 0, 0) (100, 0, 0)
(M, L) 500 (15, 15, 70) LLIN_ACT 60 % ACT_IRS 60 % None ACT 40 % None
(65, 5, 30) (90, 0, 10) (95, 0, 5) (100, 0, 0) (100, 0, 0)
(M, M) 500 (15, 15, 70) LLIN_ACT 60 % ACT_IRS 60 % None ACT 20 % ACT 40 %
(65, 5, 30) (90, 0, 10) (95, 0, 5) (100, 0, 0) (100, 0, 0)
(M, H) 266 (15, 15, 70) LLIN_ACT 60 % ACT_IRS 60 % ACT 60 % None ACT 40 %
(65, 5, 30) (90, 0, 10) (95, 0, 5) (100, 0, 0) (100, 0, 0)
(M, H) 234 (15, 15, 70) LLIN 60 % ACT_IRS 60 % ACT 60 % None ACT 40 %
(60, 5, 35) (90, 0, 10) (95, 0, 5) (100, 0, 0) (100, 0, 0)
(W, L) 497 (10, 15, 75) None LLIN_ACT 60 % ACT_IRS 60 % ACT_IRS 60 % ACT_IRS 60 %
(10, 15, 75) (60, 5, 35) (80, 5, 15) (75, 10, 15) (75, 10, 15)
(W, L) 3 (10, 15, 75) IPT 40 % LLIN_ACT 60 % ACT_IRS 60 % ACT_IRS 60 % ACT_IRS 60 %
(10, 15, 75) (60, 5, 35) (80, 5, 15) (75, 10, 15) (75, 10, 15)
(W, M) 324 (10, 15, 75) None LLIN_ACT 60 % ACT_IRS 60 % ACT_IRS 60 % ACT_IRS 60 %
(10, 15, 75) (60, 5, 35) (80, 5, 15) (75, 10, 15) (75, 10, 15)
(W, M) 172 (10, 15, 75) None None LLIN_ACT 60 % ACT_IRS 60 % ACT_IRS 60 %
(10, 15, 75) (10, 15, 75) (60, 5, 35) (80, 5, 15) (75, 10, 15)
(W, M) 2 (10, 15, 75) IPT 40 % LLIN_ACT 60 % ACT_IRS 60 % ACT_IRS 60 % ACT_IRS 60 %
(10, 15, 75) (60, 5, 35) (80, 5, 15) (75, 10, 15) (75, 10, 15)
(W, M) 1 (10, 15, 75) None IPT 20 % LLIN_ACT 60 % ACT_IRS 60 % ACT_IRS 60 %
(10, 15, 75) (10, 15, 75) (60, 5, 35) (80, 5, 15) (75, 10, 15)
(W, M) 1 (10, 15, 75) None IPT 40 % LLIN_ACT 60 % ACT_IRS 60 % ACT_IRS 60 %
(10, 15, 75) (10, 15, 75) (60, 5, 35) (80, 5, 15) (75, 10, 15)
(W, H) 498 (10, 15, 75) None None LLIN_ACT 60 % ACT_IRS 60 % ACT_IRS 60 %
(10, 15, 75) (10, 15, 75) (60, 5, 35) (80, 5, 15) (75, 10, 15)
(W, H) 1 (10, 15, 75) IPT 40 % None LLIN_ACT 60 % ACT_IRS 60 % ACT_IRS 60 %
(10, 15, 75) (10, 15, 75) (60, 5, 35) (80, 5, 15) (75, 10, 15)
(W, H) 1 (10, 15, 75) IPT 40 % IPT 40 % LLIN_ACT 60 % ACT_IRS 60 % ACT_IRS 60 %
(10, 15, 75) (10, 15, 75) (60, 5, 35) (80, 5, 15) (75, 10, 15)
  1. Geographic region refers to the climate region and distribution cost pair, where the climate region is dry (D), moderate (M) or wet (W), and the distribution cost is low (L), medium (M) or high (H). Number of districts refers to the number of districts that were assigned a given trajectory. Initial population state is the starting (SIR) percentages of the region; the end population state for a given year is the resulting (SIR) state after distributing the corresponding intervention. The total person-days of malaria infection in this scenario is 2.977 billion