Burkholderia cepacia (B.cepacia) is a type of bacteria that can cause serious infections, particularly in people with weakened immune systems. While B.cepacia outbreaks can occur in various settings, they are particularly concerning in hospitals and healthcare facilities. This paper presents a comprehensive analysis of four B.cepacia outbreaks over a one-year period that infected 20 patients in a tertiary hospital in South India. The primary objective of the paper is to model the effect of infection control measures on the dynamics of the B.cepacia outbreaks and to estimate the effect of delayed infection control measures. The idea is to separate the outbreaks into two phases: phase I, during the initial outbreak, where infection control measures are not in place and the outbreak is spreading; and phase II, where robust infection control measures are implemented and the outbreak is getting under control. We explore three different models for the two phases: a Poisson model, an unstructured Hidden Markov Model, and a Structured Hidden Markov Model with SIS dynamics. These models are then used to estimate the effect of a delayed infection control response by forecasting the effect of such a delayed response. We find that delaying infection control measures by 2 or 4 weeks led to higher cumulative infection counts compared to the observed count. The model predicted that a 2-week delay could result in approximately 50 cumulative cases, while a 4-week delay could lead to nearly 80 cases, compared to the observed 20 cases. These findings highlight the importance of implementing infection control measures in a timely manner.