<p>Managers aim to minimize financial losses during pandemics by preventing the spread of infections among employees. Identifying employees who are most likely to infect others is crucial for implementing effective preventive measures. To do so, the infectious among employees should be modelled at first. Secondly, the members who directly or indirectly infect the maximum number of other employees are identified. This study addresses this problem as an Influence Maximization (IM) problem using a Mixed Integer Linear Programming (MILP) for analytical optimization. The MILP optimization guarantees the global optimal solution. However, given the stochastic nature of influence, the IM problem is formulated as a stochastic optimization based on a limited number of scenarios. Therefore, the whole stochastic nature of the influence process may not be captured. It is of high importance to check whether the number of scenarios used in stochastic MILP is adequate. This study evaluates it by simulating the results with numerous scenarios to assess the gap between the objective function and the exact expected value. The proposed methodology is tested on real case studies from companies of varying sizes, including a small-scale company with twelve employees and a larger-scale company with more than thousand employees. The results discuss the efficiency and adequacy of the scenarios used in the stochastic MILP approach.</p>