Managers seek ways to survive pandemic situations to avoid huge financial losses. Preventing staff from infecting each other plays a prominent role in survival. To do so, the infectious among employees should be modeled at first. Secondly, the members who directly or indirectly infect the maximum number of other employees are identified. This problem is considered as an instance of Influence Maximization (IM) general problems. We use a Mixed Integer Linear Program (MILP) analytical optimization for IM problem. The MILP optimization guarantees the global optimal solution. However, due to the uncertain 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. To do so, the result of the optimization is simulated with numerous scenarios to evaluate the gap between the objective function and the exact expected value. The proposed stochastic MILP methodology is examined on a company with twelve employees. The efficiency of the method and adequacy of scenarios are then discussed.