Improving the Hospital Quality of Care during Winter Periods by Optimizing Budget Allocation Between Rotavirus Vaccination and Bed Expansion

Dort, Thibaut; Schecroun, Nadia; Standaert, Baudouin

Results of the one-way sensitivity analyses are presented in Fig. 3 showing the impact of varying inputs by ± 25% on the three model outputs. The most influential factors related to optimal V are those in the composition of C V , namely the vaccine cost per course and the size of the population to vaccinate. Similarly, the optimal percentage of H is most influenced by the components of C H , the number of existing beds, the price of an extra bed, and the length of the evaluation period considered. Since the input data enter the calculation of the budget in the same way (through C V and C H ), variation in the minimum budget remained constant. Figure 4 presents the budget-minimizing allocation for rotavirus vaccination and RVGE treatment for each of the six scenarios in the sensitivity analysis. Although the variations of a and b in panels I and III are quantitatively the same around the baseline scenario (panel II), the resulting variation in the optimal combination of prevention and treatment is not symmetric. For instance, when a is reduced from 0.5 to 0.25, the optimal vaccine coverage decreases from 63.51 to 31.40% (i.e., a 50.56% decrease) (panel I), but when b is reduced from 0.5 to 0.25, the optimal extra hospital beds decreases from 39.36 to 15.32% (i.e., a 61.08% decrease), a higher relative decrease for the optimal percentage of extra hospital beds (panel III).